t6A and ms2t6A Modified Nucleosides in Serum and Urine as Strong Candidate Biomarkers of COVID-19 Infection and Severity.

SARS-CoV-2 infection alters cellular RNA content. Cellular RNAs are chemically modified and eventually degraded, depositing modified nucleosides into extracellular fluids such as serum and urine. Here we searched for COVID-19-specific changes in modified nucleoside levels contained in serum and urine of 308 COVID-19 patients using liquid chromatography-mass spectrometry (LC-MS). We found that two modified nucleosides, N6-threonylcarbamoyladenosine (t6A) and 2-methylthio-N6-threonylcarbamoyladenosine (ms2t6A), were elevated in serum and urine of COVID-19 patients. Moreover, these levels were associated with symptom severity and decreased upon recovery from COVID-19. In addition, the elevation of similarly modified nucleosides was observed regardless of COVID-19 variants. These findings illuminate specific modified RNA nucleosides in the extracellular fluids as biomarkers for COVID-19 infection and severity.

Fake metabolomics chromatogram generation for facilitating deep learning of peak-picking neural networks.

Finding peaks in chromatograms and determining their start and end points (peak picking) is a core task in chromatography based biotechnology. Construction of peak-picking neural networks by deep learning was, however, hampered from the preparation of exact peak-picked or "labeled" chromatograms since the exact start and end points were often unclear in overlapping peaks in real chromatograms. We present a design of a fake chromatogram generator, along with a method for deep learning of peak-picking neural networks. Fake chromatograms were generated by generation of fake peaks, random sampling of peak positions from feature distributions, and merging with real blank sample chromatograms. Information on the exact start and end points, as labeled on the fake chromatograms, were effective for training and evaluating peak-picking neural networks. The peak-picking neural networks constructed herein outperformed conventional peak-picking software and showed comparable performance with that of experienced operators for processing the widely targeted metabolome data. Results of this study indicate that generation of fake chromatograms would be crucial for developing peak-picking neural networks and a key technology for further improvement of peak picking neural networks.

Free D-amino acids produced by commensal bacteria in the colonic lumen.

D-amino acids (D-AAs) have various biological activities, such as activation of N-methyl-D-aspartic acid (NMDA) receptor as a co-agonist by D-Ser. Since several free D-AAs are released in the broth monocultured with bacterium and D-AAs are probably utilized for bacterial communication, we presume that intestinal microbiota releases several kinds of free D-AAs, which may be involved in the hosts' health. However, presently, only four free D-AAs have been found in the ceacal lumen, but not in the colonic lumen. Here, we showed, by simultaneous analysis of chiral AAs using high-sensitivity liquid chromatography-tandem mass spectrometry (LC-MS/MS), that 12 free D-AAs (D-Ala, D-Arg, D-Asp, D-Gln, D-Glu, D-allo-Ile, D-Leu, D-Lys, D-Met, D-Phe, D-Ser, and D-Trp) are produced by intestinal microbiota and identified bacterial groups belonging to Firmicutes as the relevant bacterial candidates.