In this report, we explored the benefits of cyclic ion mobility (cIM) mass spectrometry in the analysis of isomeric post‐transcriptional modifications of RNA. Standard methyl‐cytidine samples were initially utilized to test the ability to correctly distinguish different structures sharing the same elemental composition and thus molecular mass. Analyzed individually, the analytes displayed characteristic arrival times (tD) determined by the different positions of the modifying methyl groups onto the common cytidine scaffold. Analyzed in mixture, the widths of the respective signals resulted in significant overlap that initially prevented their resolution on the tD scale. The separation of the four isomers was achieved by increasing the number of passes through the cIM device, which enabled to fully differentiate the characteristic ion mobility behaviors associated with very subtle structural variations. The placement of the cIM device between the mass‐selective quadrupole and the time‐of‐flight analyzer allowed us to perform gas‐phase activation of each of these ion populations, which had been first isolated according to a common mass‐to‐charge ratio and then separated on the basis of different ion mobility behaviors. The observed fragmentation patterns confirmed the structures of the various isomers thus substantiating the benefits of complementing unique tD information with specific fragmentation data to reach more stringent analyte identification. These capabilities were further tested by analyzing natural mono‐nucleotide mixtures obtained by exonuclease digestion of total RNA extracts. In particular, the combination of cIM separation and post‐mobility dissociation allowed us to establish the composition of methyl‐cytidine and methyl‐adenine components present in the entire transcriptome of HeLa cells. For this reason, we expect that this technique will benefit not only epitranscriptomic studies requiring the determination of identity and expression levels of RNA modifications, but also metabolomics investigations involving the analysis of natural extracts that may possibly contain subsets of isomeric/isobaric species.

Missense mutations in leucine-rich repeat kinase 2 (LRRK2) are the most common cause of familial Parkinson's Disease (PD); however, pathways regulating LRRK2 subcellular localization, function, and turnover are not fully defined. We performed quantitative mass spectrometry-based interactome studies to identify 48 novel LRRK2 interactors, including the microtubule-associated E3 ubiquitin ligase TRIM1 (Tripartite Motif Family 1). TRIM1 recruits LRRK2 to the microtubule cytoskeleton for ubiquitination and proteasomal degradation by binding LRRK2 822-982, a flexible interdomain region we designate the "Regulatory Loop" (RL). Phosphorylation of LRRK2 Ser910/935 within LRRK2 RL serves as a molecular switch controlling LRRK2's association with cytoplasmic 14-3-3 versus microtubule-bound TRIM1. Association with TRIM1 prevents upregulation of LRRK2 kinase activity by Rab29 and also rescues neurite outgrowth deficits caused by PD-driving mutant LRRK2 G2019S. Our data suggest that TRIM1 is a critical regulator of LRRK2, modulating its cytoskeletal recruitment, turnover, kinase activity, and cytotoxicity.

Missense mutations in leucine-rich repeat kinase 2 (LRRK2) are the most common cause of familial Parkinson’s disease (PD); however, pathways regulating LRRK2 subcellular localization, function, and turnover are not fully defined. We performed quantitative mass spectrometry–based interactome studies to identify 48 novel LRRK2 interactors, including the microtubule-associated E3 ubiquitin ligase TRIM1 (tripartite motif family 1). TRIM1 recruits LRRK2 to the microtubule cytoskeleton for ubiquitination and proteasomal degradation by binding LRRK2911–919, a nine amino acid segment within a flexible interdomain region (LRRK2853–981), which we designate the “regulatory loop” (RL). Phosphorylation of LRRK2 Ser910/Ser935 within LRRK2 RL influences LRRK2’s association with cytoplasmic 14-3-3 versus microtubule-bound TRIM1. Association with TRIM1 modulates LRRK2’s interaction with Rab29 and prevents upregulation of LRRK2 kinase activity by Rab29 in an E3-ligase–dependent manner. Finally, TRIM1 rescues neurite outgrowth deficits caused by PD-driving mutant LRRK2 G2019S. Our data suggest that TRIM1 is a critical regulator of LRRK2, controlling its degradation, localization, binding partners, kinase activity, and cytotoxicity.

We propose a novel approach for building a classification/identification framework based on the full complement of RNA post-transcriptional modifications (rPTMs) expressed by an organism at basal conditions. The approach relies on advanced mass spectrometry techniques to characterize the products of exonuclease digestion of total RNA extracts. Sample profiles comprising identities and relative abundances of all detected rPTM were used to train and test the capabilities of different machine learning (ML) algorithms. Each algorithm proved capable of identifying rigorous decision rules for differentiating closely related classes and correctly assigning unlabeled samples. The ML classifiers resolved different members of the Enterobacteriaceae family, alternative Escherichia coli serotypes, a series of Saccharomyces cerevisiae knockout mutants, and primary cells of the Homo sapiens central nervous system, which shared very similar genetic backgrounds. The excellent levels of accuracy and resolving power achieved by training on a limited number of classes were successfully replicated when the number of classes was significantly increased to escalate complexity. A dendrogram generated from ML-curated data exhibited a hierarchical organization that closely resembled those afforded by established taxonomic systems. Finer clustering patterns revealed the extensive effects induced by the deletion of a single pivotal gene. This information provided a putative roadmap for exploring the roles of rPTMs in their respective regulatory networks, which will be essential to decipher the epitranscriptomics code. The ubiquitous presence of RNA in virtually all living organisms promises to enable the broadest possible range of applications, with significant implications in the diagnosis of RNA-related diseases.