Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease

Shifts in the magnitude and nature of gut microbial metabolites have been implicated in Alzheimer's disease (AD), but the host receptors that sense and respond to these metabolites are largely unknown. Here, we develop a systems biology framework that integrates machine learning and multi-omics to identify molecular relationships of gut microbial metabolites with non-olfactory G-protein-coupled receptors (termed the "GPCRome"). We evaluate 1.09 million metabolite-protein pairs connecting 408 human GPCRs and 335 gut microbial metabolites. Using genetics-derived Mendelian randomization and integrative analyses of human brain transcriptomic and proteomic profiles, we identify orphan GPCRs (i.e., GPR84) as potential drug targets in AD and that triacanthine experimentally activates GPR84. We demonstrate that phenethylamine and agmatine significantly reduce tau hyperphosphorylation (p-tau181 and p-tau205) in AD patient induced pluripotent stem cell-derived neurons. This study demonstrates a systems biology framework to uncover the GPCR targets of human gut microbiota in AD and other complex diseases if broadly applied.

Model Agnostic Semi-Supervised Meta-Learning Elucidates Understudied Out-of-distribution Molecular Interactions

Many biological problems are understudied due to experimental limitations and human biases. Although deep learning is promising in accelerating scientific discovery, its power compromises when applied to problems with scarcely labeled data and data distribution shifts. We developed a semi-supervised meta learning framework - Meta Model Agnostic Pseudo Label Learning (MMAPLE) - to address these challenges by effectively exploring out-of-distribution (OOD) unlabeled data when transfer learning fails. The power of MMAPLE is demonstrated in multiple applications: predicting OOD drug-target interactions, hidden human metabolite-enzyme interactions, and understudied interspecies microbiome metabolite-human receptor interactions, where chemicals or proteins in unseen data are dramatically different from those in training data. MMAPLE achieves 11% to 242% improvement in the prediction-recall on multiple OOD benchmarks over baseline models. Using MMAPLE, we reveal novel interspecies metabolite-protein interactions that are validated by bioactivity assays and fill in missing links in microbiome-human interactions. MMAPLE is a general framework to explore previously unrecognized biological domains beyond the reach of present experimental and computational techniques.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

Metabolomics Analysis of the Prefrontal Cortex in a Rat Chronic Unpredictable Mild Stress Model of Depression

BACKGROUND

Depressive disorder is the leading cause of disability and suicidality worldwide. Metabolites are considered indicators and regulators of depression. However, the pathophysiology of the prefrontal cortex (PFC) in depression remains unclear.

METHODS

A chronic unpredictable mild stress (CUMS) model and a maturation rodent model of depression was used to investigate metabolic changes in the PFC. Eighteen male Sprague-Dawley rats were randomly divided into CUMS and control groups. The sucrose preference test (SPT) and forced swimming test (FST) were employed to evaluate and record depression-associated behaviors and changes in body weight (BW). High-performance liquid chromatography-tandem mass spectrometry was applied to test metabolites in rat PFC. Furthermore, principal component analysis and orthogonal partial least-squares discriminant analysis were employed to identify differentially abundant metabolites. Metabolic pathways were analyzed using MetaboAnalyst. Finally, a metabolite-protein interaction network was established to illustrate the function of differential metabolites.

RESULTS

SPT and FST results confirmed successful establishment of the CUMS-induced depression-like behavior model in rats. Five metabolites, including 1-methylnicotinamide, 3-methylhistidine, acetylcholine, glycerophospho-N-palmitoyl ethanolamine, α-D-mannose 1-phosphate, were identified as potential biomarkers of depression. Four pathways changed in the CUMS group. Metabolite-protein interaction analysis revealed that 10 pathways play roles in the metabolism of depression.

CONCLUSION

Five potential biomarkers were identified in the PFC and metabolite-protein interactions associated with metabolic pathophysiological processes were explored using the CUMS model. The results of this study will assist physicians and scientists in discovering potential diagnostic markers and novel therapeutic targets for depression.

Investigating metabolite-protein interactions: an overview of available techniques

Metabolites comprise the molar majority of chemical substances in living cells, and metabolite-protein interactions are expected to be quite common. Many interactions have already been identified and have been shown to be involved in the regulation of different types of cellular processes including signaling events, enzyme activities, protein localizations and interactions. Recent technological advances have greatly facilitated the detection of metabolite-protein interactions at high sensitivity and some of these have been applied on a large scale. In this manuscript, we review the available in vitro, in silico and in vivo technologies for mapping small-molecule-protein interactions. Although some of these were developed for drug-protein interactions they can be applied for mapping metabolite-protein interactions. Information gained from the use of these approaches can be applied to the manipulation of cellular processes and therapeutic applications.