Indolic Structure Metabolites as Potential Biomarkers of Non-infectious Diseases

Unraveling the Dynamics of Host-Microbiota Indole Metabolism: An Investigation of Indole, Indolin-2-one, Isatin, and 3-Hydroxyindolin-2-one

The gut microbiota produces a variety of bioactive molecules that facilitate host-microbiota interaction. Indole and its metabolites are focused as possible biomarkers for various diseases. However, data on indole metabolism and individual metabolites remain limited. Hence, we investigated the metabolism and distribution of indole, indolin-2-one, isatin, and 3-hydroxyindolin-2-one. First, we orally administered a high dose of indole into C57BL/6J mice and measured the concentrations of indole metabolites in the brain, liver, plasma, large and small intestines, and cecum at multiple time points using HPLC/MS. Absorption in 30 min and full metabolization in 6 h were established. Furthermore, indole, indolin-2-one, and 3-hydroxiindolin-2-one, but not isatin, were found in the brain. Second, we confirmed these findings by using stable isotope-carrying indole. Third, we identified 3-hydroxyindolin-2-one as an indole metabolite in vivo by utilizing a 3-hydroxyindolin-2-one-converting enzyme, IifA. Further, we confirmed the ability of orally administered 3-hydroxyindolin-2-one to cross the blood-brain barrier in a dose-dependent manner. Finally, we detected upregulation of the CYP1A2 and CYP2A5 genes, confirming the importance of these cytochrome isoforms in indole metabolism in vivo. Overall, our results provide a basic characterization of indole metabolism in the host and highlight 3-hydroxyindolin-2-one as a potentially brain-affecting indole metabolite.

A Preliminary Comparison of Plasma Tryptophan Metabolites and Medium- and Long-Chain Fatty Acids in Adult Patients with Major Depressive Disorder and Schizophrenia

Background and Objectives: Disturbance of tryptophan (Trp) and fatty acid (FA) metabolism plays a role in the pathogenesis of psychiatric disorders. However, quantitative analysis and comparison of plasma Trp metabolites and medium- and long-chain fatty acids (MCFAs and LCFAs) in adult patients with major depressive disorder (MDD) and schizophrenia (SCH) are limited. Materials and Methods: Clinical symptoms were assessed and the level of Trp metabolites and MCFAs and LCFAs for plasma samples from patients with MDD (n = 24) or SCH (n = 22) and healthy controls (HC, n = 23) were obtained and analyzed. Results: We observed changes in Trp metabolites and MCFAs and LCFAs with MDD and SCH and found that Trp and its metabolites, such as N-formyl-kynurenine (NKY), 5-hydroxyindole-3-acetic acid (5-HIAA), and indole, as well as omega-3 polyunsaturated fatty acids (N3) and the ratio of N3 to omega-6 polyunsaturated fatty acids (N3: N6), decreased in both MDD and SCH patients. Meanwhile, levels of saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA) decreased in SCH patients, and there was a significant difference in the composition of MCFAs and LCFAs between MDD and SCH patients. Moreover, the top 10 differential molecules could distinguish the two groups of diseases from HC and each other with high reliability. Conclusions: This study provides a further understanding of dysfunctional Trp and FA metabolism in adult patients with SCH or MDD and might develop combinatorial classifiers to distinguish between these disorders.

Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults

BACKGROUND

The fecal metabolome is affected by diet and includes metabolites generated by human and microbial metabolism. Advances in -omics technologies and analytic approaches have allowed researchers to identify metabolites and better utilize large data sets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake.

OBJECTIVES

We aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake.

METHODS

Data were aggregated from 5 controlled feeding studies in adults that tested the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. Fecal samples underwent GC-MS metabolomic analysis; 344 metabolites were detected in preintervention samples, whereas 307 metabolites were detected postintervention. After removing metabolites that were only detected in either pre- or postintervention and those undetectable in ≥80% of samples in all study groups, changes in 96 metabolites relative concentrations (treatment postintervention minus preintervention) were utilized in random forest models to 1) examine the relation between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score).

RESULTS

Using the change in relative concentration of 96 fecal metabolites, 6 single-food random forest models for almond, avocado, broccoli, walnuts, whole-grain barley, and whole-grain oats revealed prediction accuracies between 47% and 89%. When comparing foods with one another, almond intake was differentiated from walnut intake with 91% classification accuracy.

CONCLUSIONS

Our findings reveal promise in utilizing fecal metabolites as objective complements to certain self-reported food intake estimates. Future research on other foods at different doses and dietary patterns is needed to identify biomarkers that can be applied in feeding study compliance and clinical settings.

Electrochemical (Bio)Sensing Devices for Human-Microbiome-Related Biomarkers

The study of the human microbiome is a multidisciplinary area ranging from the field of technology to that of personalized medicine. The possibility of using microbiota biomarkers to improve the diagnosis and monitoring of diseases (e.g., cancer), health conditions (e.g., obesity) or relevant processes (e.g., aging) has raised great expectations, also in the field of bioelectroanalytical chemistry. The well-known advantages of electrochemical biosensors-high sensitivity, fast response, and the possibility of miniaturization, together with the potential for new nanomaterials to improve their design and performance-position them as unique tools to provide a better understanding of the entities of the human microbiome and raise the prospect of huge and important developments in the coming years. This review article compiles recent applications of electrochemical (bio)sensors for monitoring microbial metabolites and disease biomarkers related to different types of human microbiome, with a special focus on the gastrointestinal microbiome. Examples of electrochemical devices applied to real samples are critically discussed, as well as challenges to be faced and where future developments are expected to go.

A Selective and Sensitive LC-MS/MS Method for Quantitation of Indole in Mouse Serum and Tissues

Indole is an endogenous substance currently being evaluated as a biomarker for ulcerative colitis, irritable bowel syndrome, Crohn’s disease and non-alcoholic fatty liver disease. A novel, selective, and sensitive method using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) was developed for quantitation of indole concentrations in mouse plasma and tissues. Samples were prepared by protein precipitation using ice-cold acetonitrile (ACN) followed by injecting the extracted analyte to LC-MS/MS system. Indole was separated using Synergi Fusion C18 (4 µm, 250 × 2.0 mm) column with mobile phase 0.1% aqueous formic acid (A) and methanol (B) using gradient flow with run time 12 min. The mass spectrometer was operated in atmospheric pressure chemical ionization (APCI) positive mode at unit resolution in multiple reaction monitoring (MRM) mode, using precursor ion > product ion combinations of 118.1 > 91.1 m/z for indole and 124.15 > 96.1 m/z for internal standard (IS) indole d7. The MS/MS response was linear over the range of indole concentrations (1−500 ng/mL). The validated method was applied for quantitation of indole concentrations range in mouse lungs (4.3−69.4 ng/g), serum (0.8−38.7 ng/mL) and cecum (1043.8−12,124.4 ng/g). This method would help investigate the role of indole as a biomarker and understand its implications in different disease states.

Gut Microbiota Metabolites in Major Depressive Disorder-Deep Insights into Their Pathophysiological Role and Potential Translational Applications

The gut microbiota is a complex and dynamic ecosystem essential for the proper functioning of the organism, affecting the health and disease status of the individuals. There is continuous and bidirectional communication between gut microbiota and the host, conforming to a unique entity known as "holobiont". Among these crosstalk mechanisms, the gut microbiota synthesizes a broad spectrum of bioactive compounds or metabolites which exert pleiotropic effects on the human organism. Many of these microbial metabolites can cross the blood-brain barrier (BBB) or have significant effects on the brain, playing a key role in the so-called microbiota-gut-brain axis. An altered microbiota-gut-brain (MGB) axis is a major characteristic of many neuropsychiatric disorders, including major depressive disorder (MDD). Significative differences between gut eubiosis and dysbiosis in mental disorders like MDD with their different metabolite composition and concentrations are being discussed. In the present review, the main microbial metabolites (short-chain fatty acids -SCFAs-, bile acids, amino acids, tryptophan -trp- derivatives, and more), their signaling pathways and functions will be summarized to explain part of MDD pathophysiology. Conclusions from promising translational approaches related to microbial metabolome will be addressed in more depth to discuss their possible clinical value in the management of MDD patients.

Metabolomic connections between schizophrenia, antipsychotic drugs and metabolic syndrome: A variety of players

BACKGROUND

Diagnosis of schizophrenia lacks of reliable medical diagnostic tests and robust biomarkers applied to clinical practice. Schizophrenic patients undergoing treatment with antipsychotics suffer a reduced life expectancy due to metabolic disarrangements that co-exist with their mental illness and predispose them to develop metabolic syndrome, also exacerbated by medication. Metabolomics is an emerging and potent technology able to accelerate this biomedical research. <P> Aim: This review focus on a detailed vision of the molecular mechanisms involved both in schizophrenia and antipsychotic-induced metabolic syndrome, based on innovative metabolites that consistently change in nascent metabolic syndrome, drug-naïve, first episode psychosis and/or schizophrenic patients compared to healthy subjects. <P> Main lines: Supported by metabolomic approaches, although not exclusively, noteworthy variations are reported mainly through serum samples of patients and controls in several scenes: 1) alterations in fatty acids, inflammatory response indicators, amino acids and biogenic amines, biometals and gut microbiota metabolites (schizophrenia); 2) alterations in metabolites involved in carbohydrate and gut microbiota metabolism, inflammation and oxidative stress (metabolic syndrome), some of them shared with the schizophrenia scene; 3) alterations of cytokines secreted by adipose tissue, phosphatidylcholines, acylcarnitines, Sirtuin 1, orexin-A and changes in microbiota composition (antipsychotic-induced metabolic syndrome). <P> Conclusion: Novel insights into the pathogenesis of schizophrenia and metabolic side-effects associated to its antipsychotic treatment, represent an urgent request for scientifics and clinicians. Leptin, carnitines, adiponectin, insulin or interleukin-6 represent some examples of candidate biomarkers. Cutting-edge technologies like metabolomics have the power of strengthen research for achieving preventive, diagnostic and therapeutical solutions for schizophrenia.