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There has been a renewed interest in the potential use of psychedelics for the treatment of psychiatric conditions. Nevertheless, little is known about the mechanism of action and molecular pathways influenced by ayahuasca use in humans. Therefore, for the first time, our study aims to investigate the human metabolomics signature after consumption of a psychedelic, ayahuasca, and its connection with both the psychedelic-induced subjective effects and the plasma concentrations of ayahuasca alkaloids.
Plasma samples of 23 individuals were collected both before and after ayahuasca consumption. Samples were analysed through targeted metabolomics and further integrated with subjective ratings of the ayahuasca experience (i.e., using the 5-Dimension Altered States of Consciousness Rating Scale [ASC]), and plasma ayahuasca-alkaloids using integrated network analysis. Metabolic pathways enrichment analysis using diffusion algorithms for specific KEGG modules was performed on the metabolic output.
Compared to baseline, the consumption of ayahuasca increased N-acyl-ethanolamine endocannabinoids, decreased 2-acyl-glycerol endocannabinoids, and altered several large-neutral amino acids (LNAAs). Integrated network results indicated that most of the LNAAs were inversely associated with 9 out of the 11 subscales of the ASC, except for tryptophan which was positively associated. Several endocannabinoids and hexosylceramides were directly associated with the ayahuasca alkaloids. Enrichment analysis confirmed dysregulation in several pathways involved in neurotransmission such as serotonin and dopamine synthesis.
In conclusion, a crosstalk between the circulating LNAAs and the subjective effects is suggested, which is independent of the alkaloid concentrations and provides insights into the specific metabolic fingerprint and mechanism of action underlying ayahuasca experiences.
Trypanosoma cruzi, the causative agent of Chagas disease (American trypanosomiasis), colonizes the intestinal tract of triatomines. Triatomine bugs act as vectors in the life cycle of the parasite and transmit infective parasite stages to animals and humans. Contact of the vector with T. cruzi alters its intestinal microbial composition, which may also affect the associated metabolic patterns of the insect. Earlier studies suggest that the complexity of the triatomine fecal metabolome may play a role in vector competence for different T. cruzi strains. Using high-resolution mass spectrometry and supervised machine learning, we aimed to detect differences in the intestinal metabolome of the triatomine Rhodnius prolixus and predict whether the insect had been exposed to T. cruzi or not based solely upon their metabolic profile. We were able to predict the exposure status of R. prolixus to T. cruzi with accuracies of 93.6%, 94.2% and 91.8% using logistic regression, a random forest classifier and a gradient boosting machine model, respectively. We extracted the most important features in producing the models and identified the major metabolites which assist in positive classification. This work highlights the complex interactions between triatomine vector and parasite including effects on the metabolic signature of the insect.
The emerging disciplines of lipidomics and metabolomics show great potential for the discovery of diagnostic biomarkers, but appropriate pre-analytical sample-handling procedures are critical because several analytes are prone to ex vivo distortions during sample collection. To test how the intermediate storage temperature and storage period of plasma samples from K3EDTA whole-blood collection tubes affect analyte concentrations, we assessed samples from non-fasting healthy volunteers (n = 9) for a broad spectrum of metabolites, including lipids and lipid mediators, using a well-established LC-MS-based platform. We used a fold change-based approach as a relative measure of analyte stability to evaluate 489 analytes, employing a combination of targeted LC-MS/MS and LC-HRMS screening. The concentrations of many analytes were found to be reliable, often justifying less strict sample handling; however, certain analytes were unstable, supporting the need for meticulous processing. We make four data-driven recommendations for sample-handling protocols with varying degrees of stringency, based on the maximum number of analytes and the feasibility of routine clinical implementation. These protocols also enable the simple evaluation of biomarker candidates based on their analyte-specific vulnerability to ex vivo distortions. In summary, pre-analytical sample handling has a major effect on the suitability of certain metabolites as biomarkers, including several lipids and lipid mediators. Our sample-handling recommendations will increase the reliability and quality of samples when such metabolites are necessary for routine clinical diagnosis.
Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects
(2023)
Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders.
The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results.
The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.
Truffles (Tuber spp.) are the fruiting bodies of symbiotic fungi, which are prized food delicacies. The marked aroma variability observed among truffles of the same species has been attributed to a series of factors that are still debated. This is because factors (i.e. genetics, maturation, geographical location and the microbial community colonizing truffles) often co-vary in truffle orchards. Here, we removed the co-variance effect by investigating truffle flavour in axenic cultures of nine strains of the white truffle Tuber borchii. This allowed us to investigate the influence of genetics on truffle aroma. Specifically, we quantified aroma variability and explored whether strain selection could be used to improve human-sensed truffle flavour. Our results illustrate that aroma variability among strains is predominantly linked to amino acid catabolism through the Ehrlich pathway, as confirmed by 13C labelling experiments. We furthermore exemplified through sensory analysis that the human nose is able to distinguish among strains and that sulfur volatiles derived from the catabolism of methionine have the strongest influence on aroma characteristics. Overall, our results demonstrate that genetics influences truffle aroma much more deeply than previously thought and illustrate the usefulness of strain selection for improving truffle flavour.