Abnormal inflammation and oxidative stress are involved in various diseases. Papaver rhoeas L. possesses various pharmacological activities, and a previously reported analysis of the anti-inflammatory effect of P. nudicaule ethanol extracts and alkaloid profiles of the plants suggest isoquinoline alkaloids as potential pharmacologically active compounds. Here, we investigated anti-inflammatory and antioxidant activities of ethyl acetate (EtOAc) fractions of P. nudicaule and P. rhoeas extracts in lipopolysaccharide (LPS)-stimulated RAW264.7 cells. EtOAc fractions of P. nudicaule and P. rhoeas compared to their ethanol extracts showed less toxicity but more inhibitory activity against LPS-induced nitric oxide production. Moreover, EtOAc fractions lowered the LPS-induced production of proinflammatory molecules and cytokines and inhibited LPS-activated STAT3 and NF-κB, and additionally showed significant free radical scavenging activity and decreased LPS-induced reactive oxygen species and oxidized glutathione. EtOAc fractions of P. nudicaule increased the expression of HO-1, GCLC, NQO-1, and Nrf2 in LPS-stimulated cells and that of P. rhoeas enhanced NQO-1. Furthermore, metabolomic and biochemometric analyses of ethanol extracts and EtOAc fractions indicated that EtOAc fractions of P. nudicaule and P. rhoeas have potent anti-inflammatory and antioxidant activities, further suggesting that alkaloids in EtOAc fractions are potent active molecules of tested plants.

Short DNA oligonucleotides (~4 mer) have been used to index samples from different sources, such as in multiplex sequencing. Presently, longer oligonucleotides (8–12 mer) are being used as molecular barcodes with which to distinguish among raw DNA molecules in many high-tech sequence analyses, including low-frequent mutation detection, quantitative transcriptome analysis, and single-cell sequencing. Despite some advantages of using molecular barcodes with random sequences, such an approach, however, makes it impossible to know the exact sequences used in an experiment and can lead to inaccurate interpretation due to misclustering of barcodes arising from the occurrence of unexpected mutations in the barcodes. The present study introduces a tool developed for selecting an optimal barcode subset during molecular barcoding. The program considers five barcode factors: GC content, homopolymers, simple sequence repeats with repeated units of dinucleotides, Hamming distance, and complementarity between barcodes. To evaluate a selected barcode set, penalty scores for the factors are defined based on their distributions observed in random barcodes. The algorithm employed in the program comprises two steps: i) random generation of an initial set and ii) optimal barcode selection via iterative replacement. Users can execute the program by inputting barcode length and the number of barcodes to be generated. Furthermore, the program accepts a user’s own values for other parameters, including penalty scores, for advanced use, allowing it to be applied in various conditions. In many test runs to obtain 100000 barcodes with lengths of 12 nucleotides, the program showed fast performance, efficient enough to generate optimal barcode sequences with merely the use of a desktop PC. We also showed that VFOS has comparable performance, flexibility in program running, consideration of simple sequence repeats, and fast computation time in comparison with other two tools (DNABarcodes and FreeBarcodes). Owing to the versatility and fast performance of the program, we expect that many researchers will opt to apply it for selecting optimal barcode sets during their experiments, including next-generation sequencing.