Sudo, H. | bioRxiv Science

bioRxiv2021

Spatial metabolomics using imaging mass spectrometry to identify the localization of asparaptine in Asparagus officinalis

Spatial metabolomics uses imaging mass spectrometry (IMS) to localize metabolites within tissue section. Here, we performed matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resonance-IMS (MALDI-FTICR-IMS) to identify the localization of asparaptine, a naturally occurring inhibitor of angiotensin-converting enzyme, in green spears of asparagus (Asparagus officinalis). Spatial metabolome data were acquired with an untargeted manner. Segmentation analysis using the data characterized tissue-type-dependent and - independent distribution patterns in cross-sections of asparagus spears. Moreover, asparaptine accumulated at high levels in developing lateral shoot tissues. Quantification of asparaptine in lateral shoots using liquid chromatography-tandem mass spectrometry (LC-MS/MS) validated the IMS analysis. These results provide valuable information for understanding the function of asparaptine in asparagus, and identify the lateral shoot as a potential region of interest for multiomic studies to examine gene-to-metabolite associations in asparaptine biosynthesis.

plant biology↗

bioRxiv2016

Secure Wavelet Matrix: Alphabet-Friendly Privacy-Preserving String Search

MotivationPrivacy-preserving substring matching is an important task for sensitive biological/biomedical sequence database searches. It enables a user to obtain only a substring match while his/her query is concealed to a server. The previous approach for this task is based on a linear-time algorithm in terms of alphabet size |{Sigma}|. Therefore, a more efficient method is needed to deal with strings with large alphabet size such as a protein sequence, time-series data, and a clinical document.\n\nResultsWe present a novel algorithm that can search a string in logarithmic time of |{Sigma}|. In our algorithm, named secure wavelet matrix (sWM), we use an additively homomorphic encryption to build an efficient data structure called a wavelet matrix. In an experiment using a simulated string of length 10,000 whose alphabet size ranges from 4 to 1024, the run time of the sWM was an order of magnitude faster than that of the previous method. We also tested the sWM on all sequences of one protein family in Pfam (9,826 residues in total) and clinical texts written in a natural language (77,712 letters in total). By using a laptop computer for the user and a desktop PC for the server, we found that its run time was {approx} 2.5 s (user) and {approx} 6.7 s (server) for the protein sequences and {approx} 10 s (user) and {approx} 60 s (server) for the clinical texts.\n\nAvailabilityhttps://github.com/cBioLab/sWM

bioinformatics↗