Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.

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Background: The aims of this study were to (1) evaluate the efficacy and safety of targeted antibiotics for the treatment of culture-negative prosthetic joint infection based on metagenomic next-generation sequencing results and (2) verify the accuracy and reliability of metagenomic next-generation sequencing for identifying pathogens related to culture-negative prosthetic joint infection. Methods: Ninety-seven consecutive PJI patients, including 27 patients with culture-negative prosthetic joint infection, were treated surgically at our center. Thirteen of the 27 culture-negative prosthetic joint infection patients, who were admitted before June 2017 and treated with empirical antibiotics, comprised the empirical antibiotic group (EA group), and the other 14 patients, who were admitted after June 2017 and treated with targeted antibiotics according to their metagenomic next-generation sequencing results, were classified as the targeted antibiotic group (TA group). The short-term infection control rate, incidence of antibiotic-related complications and costs were compared between the two groups. Results: Two of the patients in the EA group experienced debridement and prolonged antimicrobial therapy due to wound infection after the initial revision surgery. No recurrent infections were observed in the TA group; however, no significant difference in the infection control rate was found between the two groups (83.33% vs 100%, P = 0.217). More cases of antibiotic-related complications were recorded in the EA group (6 cases) than in the TA group (1 case), but the difference was not statistically significant (P = 0.0697). The cost of antibiotics obtained for the EA group was 20,168.37 Yuan (3236.38-45,297.16), which was higher than that found for the TA group (10, 164.16 Yuan, 2959.54-16,661.04, P = 0.04). Conclusions: Targeted antibiotic treatment for culture-negative prosthetic joint infection based on metagenomic next-generation sequencing results is associated with a favorable outcome, and metagenomic next-generation sequencing is a reliable tool for identifying pathogens related to culture-negative prosthetic joint infection.

Stomatal density (SD) and stomatal complex area (SCA) are important traits that regulate gas exchange and abiotic stress response in plants. Despite sorghum (Sorghum bicolor) adaptation to arid conditions, the genetic potential of stomata-related traits remains unexplored due to challenges in available phenotyping methods. Hence, identifying loci that control stomatal traits is fundamental to designing strategies to breed sorghum with optimized stomatal regulation. We implemented both classical and deep-learning methods to characterize genetic diversity in 311 grain sorghum accessions for stomatal traits at two different field environments. Nearly 12,000 images collected from abaxial and adaxial leaf surfaces revealed substantial variation in stomatal traits. Our study demonstrated significant accuracy between manual and deep-learning methods in predicting SD and SCA. In sorghum, SD was 32-39% greater on the abaxial vs. the adaxial surface, while SCA on the abaxial surface was 2-5% lower than on the adaxial surface. GWAS identified 71 genetic loci (38 were environment-specific) with significant genotype to phenotype associations for stomatal traits. Putative causal genes underlying the phenotypic variation were identified. Accessions with similar SCA but carrying contrasting haplotypes for SD were tested for stomatal conductance and carbon assimilation under field conditions. Our findings provide a foundation for further studies on the genetic and molecular mechanisms controlling stomata patterning and regulation in sorghum. An integrated physiological, deep learning, and genomic approach allowed us to clarify the genetic control of natural variation in stomata traits in sorghum, and can be applied to other plants.

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