Small Molecule Analysis of Extracellular Vesicles Produced by Cryptococcus gattii: Identification of a Tripeptide Controlling Cryptococcal Infection in an Invertebrate Host Model

The small molecule (molecular mass <900 Daltons) composition of extracellular vesicles (EVs) produced by the pathogenic fungus Cryptococcus gattii is unknown, which limits the understanding of the functions of cryptococcal EVs. In this study, we analyzed the composition of small molecules in samples obtained from solid cultures of C. gattii by a combination of chromatographic and spectrometric approaches, and untargeted metabolomics. This analysis revealed previously unknown components of EVs, including small peptides with known biological functions in other models. The peptides found in C. gattii EVs had their chemical structure validated by chemical approaches and comparison with authentic standards, and their functions tested in a Galleria mellonella model of cryptococcal infection. One of the vesicular peptides (isoleucine-proline-isoleucine, Ile-Pro-Ile) improved the survival of G. mellonella lethally infected with C. gattii or C. neoformans. These results indicate that small molecules exported in EVs are biologically active in Cryptococcus. Our study is the first to characterize a fungal EV molecule inducing protection, pointing to an immunological potential of extracellular peptides produced by C. gattii.

Small molecule analysis of extracellular vesicles produced by Cryptococcus gattii: identification of a tripeptide controlling cryptococcal infection in an invertebrate host model

The small molecule (molecular mass < 900 Daltons) composition of extracellular vesicles (EVs) produced by the pathogenic fungus Cryptococcus gattii is unknown, which limits the understanding of the functions of cryptococcal EVs. In this study, we analyzed the composition of small molecules in samples obtained from solid cultures of C. gattii by a combination of chromatographic and spectrometric approaches, and untargeted metabolomics. This analysis revealed previously unknown components of EVs, including small peptides with known biological functions in other models. The peptides found in C. gattii EVs had their chemical structure validated by chemical approaches and comparison with authentic standards, and their functions tested in a Galleria mellonella model of cryptococcal infection. One of the vesicular peptides (isoleucine-proline-isoleucine, Ile-Pro-Ile) improved the survival of G. mellonella lethally infected with C. gattii or C. neoformans. These results indicate that small molecules exported in EVs are biologically active in Cryptococcus. Our study is the first to characterize a fungal EV molecule inducing protection, pointing to an immunological potential of extracellular peptides produced by C. gattii.

Lignin as a MALDI matrix for small molecules：A proof of concept

Driven by the interest in metabolomic studies and the progress of imaging technique, small molecule analysis is booming, while it remains challenging to be realized by matrix-assisted laser desorption/ionization mass...

The METLIN small molecule dataset for machine learning-based retention time prediction

Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70$$\%$$% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

Presoaking dried blood spot with water improves efficiency for small-molecule analysis

The current method of extracting small molecules from dried blood spots (DBSs) and liquid blood is similar. However, owing to their different physical characteristics, a modification of the extraction process for DBS is required. We propose a modified method involving presoaking in water that results in better extraction efficiency for small-molecule analysis than the conventional protein precipitation method. Using blood and DBSs from eight subjects, the similarities, recovery rates and extraction efficiencies of both methods were compared. Quantitative analysis showed that seven and six out of ten conditions for the modified method group exhibited almost 100% recovery and extraction efficiency rates, respectively, compared with the conventional method group. Taken together, the results suggest that a presoaking step is needed for efficient DBS analysis.