Search

Trypsin inhibitors (TIs) in soybean seeds reduce the availability of processed soybean foods and animal feed. This study aimed to evaluate the trypsin inhibitor activity (TIA) in 999 Korean soybean accessions and conduct molecular characterization of soybean accessions with low TIA. TIA was evaluated using colorimetric analysis through a substrate–enzyme reaction. The average TIA of the 999 soybean accessions was 90.31%, ranging from 43.70% to 99.51%. Kunitz trypsin inhibitor 3 (KTi-3) gene target sequencing analysis was performed on seven soybean germplasm accessions (IT105782, IT170889, IT273590, IT274513, IT274515, IT276197, and IT022891) showing less than 60% TIA. Four soybean accessions (IT274513, IT274515, IT276197, and IT022891) exhibited the same mutations (a G-to-T transversion and an AG deletion) in the KTi-3 gene (Glyma.08g341500) in PI542044. In addition, whole-genome re-sequencing was performed on three soybean accessions with no mutations in the KTi-3 gene. Compared with the reference soybean genome, an in-frame insertion and five missense mutations were identified in the coding sequencing of the KTi-1 gene (Glyma.01g09500) in IT105782. A RT-qPCR analysis showed that the mRNA expression level of KTi-1 was reduced by approximately 17% in IT105782 during seed development. In this study, we identified a previously unreported mutation in the KTi-1 gene and developed a KASP marker using this new KTi-1 variant. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Identification of a Novel KTi-1 Allele Associated with Reduced Trypsin Inhibitor Activity in Soybean Accessions." The correction addresses an error in Table 3 of the original publication, where an accession number was incorrectly entered and the genotype name was modified. The corrected table is provided in the document. The authors state that the scientific conclusions of the article are unaffected by this correction. [Extracted from the article]

Motivation Predicting drug response is critical for precision medicine. Diverse methods have predicted drug responsiveness, as measured by the half-maximal drug inhibitory concentration (IC50), in cultured cells. Although IC50s are continuous, traditional prediction models have dealt mainly with binary classification of responsiveness. However, since there are few regression-based IC50 predictions, comprehensive evaluations of regression-based IC50 prediction models, including machine learning (ML) and deep learning (DL), for diverse data types and dataset sizes, have not been addressed. Results Here, we constructed 11 input data settings, including multi-omics settings, with varying dataset sizes, then evaluated the performance of regression-based ML and DL models to predict IC50s. DL models considered two convolutional neural network architectures: CDRScan and residual neural network (ResNet). ResNet was introduced in regression-based DL models for predicting drug response for the first time. As a result, DL models performed better than ML models in all the settings. Also, ResNet performed better than or comparable to CDRScan and ML models in all settings. Availability and implementation The data underlying this article are available in GitHub at https://github.com/labnams/IC50evaluation. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. Results Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. Availability and implementation The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]