TransDiscovery: Discovering Biotransformation from Human Microbiota by Integrating Metagenomic and Metabolomic Data

Gut Microbial Metabolome and Dysbiosis in Neurodegenerative Diseases: Psychobiotics and Fecal Microbiota Transplantation as a Therapeutic Approach-A Comprehensive Narrative Review

The comprehensive narrative review conducted in this study delves into the mechanisms of communication and action at the molecular level in the human organism. The review addresses the complex mechanism involved in the microbiota-gut-brain axis as well as the implications of alterations in the microbial composition of patients with neurodegenerative diseases. The pathophysiology of neurodegenerative diseases with neuronal loss or death is analyzed, as well as the mechanisms of action of the main metabolites involved in the bidirectional communication through the microbiota-gut-brain axis. In addition, interventions targeting gut microbiota restructuring through fecal microbiota transplantation and the use of psychobiotics-pre- and pro-biotics-are evaluated as an opportunity to reduce the symptomatology associated with neurodegeneration in these pathologies. This review provides valuable information and facilitates a better understanding of the neurobiological mechanisms to be addressed in the treatment of neurodegenerative diseases.

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.