Mass spectrometry-based analysis of gut microbial metabolites of aromatic amino acids

Targeted Metabolomics for the Analysis of p-Cresol in Mouse Brain: Impact of Biological Sex and Strain

p-Cresol, an environmental contaminant and endogenous metabolite derived primarily from the conversion of l-tyrosine by intestinal microflora, is gaining increasing attention, due to its potential impact on human health. Recent studies have highlighted elevated levels of p-cresol and its metabolites, including p-cresyl sulfate and p-cresyl glucuronide, in various populations, suggesting a correlation with neurodevelopmental and neurodegenerative conditions. While the role of this compound as a uremic toxin is well established, its presence and concentration within the central nervous system (CNS) remain largely unexplored. To address this gap, an high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS) method was optimized and validated for the first time in this work for the targeted metabolomics of p-cresol in brain tissues. This method enabled the quantification of this compound in different brain areas of adult male and female C57BL/6J mice and in the cortex of various mouse strains, including CD-1 and the idiopathic autism model BTBR T+Itpr3tf/J. Additionally, preliminary analyses of human cortex samples confirmed the presence of p-cresol, suggesting its relevance in human brain health. Moreover, metabolomic analyses have further explored the correlations between p-cresol and neurotransmitters, with a particular focus on dopaminergic and noradrenergic pathways. These findings pave the way for understanding the potential impact of p-cresol on neurochemical networks and its implications for neurodevelopmental and neurodegenerative disorders.

Using metabolomics to investigate the relationship between the metabolomic profile of the intestinal microbiota derivatives and mental disorders in inflammatory bowel diseases: a narrative review

Individuals with inflammatory bowel disease (IBD) are at a higher risk of developing mental disorders, such as anxiety and depression. The imbalance between the intestinal microbiota and its host, known as dysbiosis, is one of the factors, disrupting the balance of metabolite production and their signaling pathways, leading to disease progression. A metabolomics approach can help identify the role of gut microbiota in mental disorders associated with IBD by evaluating metabolites and their signaling comprehensively. This narrative review focuses on metabolomics studies that have comprehensively elucidated the altered gut microbial metabolites and their signaling pathways underlying mental disorders in IBD patients. The information was compiled by searching PubMed, Web of Science, Scopus, and Google Scholar from 2005 to 2023. The findings indicated that intestinal microbial dysbiosis in IBD patients leads to mental disorders such as anxiety and depression through disturbances in the metabolism of carbohydrates, sphingolipids, bile acids, neurotransmitters, neuroprotective, inflammatory factors, and amino acids. Furthermore, the reduction in the production of neuroprotective factors and the increase in inflammation observed in these patients can also contribute to the worsening of psychological symptoms. Analyzing the metabolite profile of the patients and comparing it with that of healthy individuals using advanced technologies like metabolomics, aids in the early diagnosis and prevention of mental disorders. This approach allows for the more precise identification of the microbes responsible for metabolite production, enabling the development of tailored dietary and pharmaceutical interventions or targeted manipulation of microbiota.

Effects of Phenolic Acids Produced from Food-Derived Flavonoids and Amino Acids by the Gut Microbiota on Health and Disease

The gut microbiota metabolizes flavonoids, amino acids, dietary fiber, and other components of foods to produce a variety of gut microbiota-derived metabolites. Flavonoids are the largest group of polyphenols, and approximately 7000 flavonoids have been identified. A variety of phenolic acids are produced from flavonoids and amino acids through metabolic processes by the gut microbiota. Furthermore, these phenolic acids are easily absorbed. Phenolic acids generally represent phenolic compounds with one carboxylic acid group. Gut microbiota-derived phenolic acids have antiviral effects against several viruses, such as SARS-CoV-2 and influenza. Furthermore, phenolic acids influence the immune system by inhibiting the secretion of proinflammatory cytokines, such as interleukin-1β and tumor necrosis factor-α. In the nervous systems, phenolic acids may have protective effects against neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. Moreover, phenolic acids can improve levels of blood glucose, cholesterols, and triglycerides. Phenolic acids also improve cardiovascular functions, such as blood pressure and atherosclerotic lesions. This review focuses on the current knowledge of the effects of phenolic acids produced from food-derived flavonoids and amino acids by the gut microbiota on health and disease.

Microbiota Metabolite Profiles and Dietary Intake in Older Individuals with Insomnia of Short vs. Normal Sleep Duration

Recent evidence suggests that the gut microbiota plays a role in insomnia pathogenesis. This study compared the dietary habits and microbiota metabolites of older adults with insomnia of short vs. normal sleep duration (ISSD and INSD, respectively). Data collection included sleep assessment through actigraphy, dietary analysis using the Food Frequency Questionnaire, and metabolomic profiling of stool samples. The results show that ISSD individuals had higher body mass index and a greater prevalence of hypertension. Significant dietary differences were observed, with the normal sleep group consuming more kilocalories per day and specific aromatic amino acids (AAAs) phenylalanine and tyrosine and branch-chain amino acid (BCAA) valine per protein content than the short sleep group. Moreover, metabolomic analysis identified elevated levels of the eight microbiota metabolites, benzophenone, pyrogallol, 5-aminopental, butyl acrylate, kojic acid, deoxycholic acid (DCA), trans-anethole, and 5-carboxyvanillic acid, in the short compared to the normal sleep group. The study contributes to the understanding of the potential role of dietary and microbial factors in insomnia, particularly in the context of sleep duration, and opens avenues for targeted dietary interventions and gut microbiota modulation as potential therapeutic approaches for treating insomnia.

Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

Epigenetic Modulations by Microbiome in Breast Cancer

Recent studies have identified a critical role of the diverse and dynamic microbiome in modulating various aspects of host physiology and intrinsic processes. However, the altered microbiome has also become a hallmark of cancer, which could influence the tumor microenvironment. Aberrations in epigenetic regulation of tumor suppressors, apoptotic genes, and oncogenes can accentuate breast cancer onset and progression. Interestingly, recent studies have established that the microbiota modulates the epigenetic mechanisms at global and gene-specific levels. While the mechanistic basis is unclear, the cross-talk between the microbiome and epigenetics influences breast cancer trajectory. Here, we review different epigenetic mechanisms of mammalian gene expression and summarize the host-associated microbiota distributed across the human body and their influence on cancer and other disease-related genes. Understanding this complex relationship between epigenetics and the microbiome holds promise for new insights into effective therapeutic strategies for breast cancer patients.