Plant parasitic nematodes are harmful to agricultural crops and plants, and may cause severe yield losses. Cinnamaldehyde, a volatile, yellow liquid commonly used as a flavoring or food additive, is increasingly becoming a popular natural nematicide because of its high nematicidal activity and, there is a high demand for the development of a biological platform to produce cinnamaldehyde. We engineered Escherichia coli as an eco-friendly biological platform for the production of cinnamaldehyde. In E. coli, cinnamaldehyde can be synthesized from intracellular l-phenylalanine, which requires the activities of three enzymes: phenylalanine-ammonia lyase (PAL), 4-coumarate:CoA ligase (4CL), and cinnamoyl-CoA reductase (CCR). For the efficient production of cinnamaldehyde in E. coli, we first examined the activities of enzymes from different sources and a gene expression system for the selected enzymes was constructed. Next, the metabolic pathway for l-phenylalanine biosynthesis was engineered to increase the intracellular pool of l-phenylalanine, which is a main precursor of cinnamaldehyde. Finally, we tried to produce cinnamaldehyde with the engineered E. coli. According to this result, cinnamaldehyde production as high as 75 mg/L could be achieved, which was about 35-fold higher compared with that in the parental E. coli W3110 harboring a plasmid for cinnamaldehyde biosynthesis. We also confirmed that cinnamaldehyde produced by our engineered E. coli had a nematicidal activity similar to the activity of commercial cinnamaldehyde by nematicidal assays against Bursaphelenchus xylophilus. As a potential natural pesticide, cinnamaldehyde was successfully produced in E. coli by construction of the biosynthesis pathway and, its production titer was also significantly increased by engineering the metabolic pathway of l-phenylalanine.

Human papillomavirus (HPV), a non-enveloped, double-stranded DNA tumor virus, is a primary etiological agent of cervical cancer development. As a potential tool for prophylactic vaccination, the development of virus-like particles (VLPs) containing the HPV16 L1 capsid protein is highly desired. In this study, we developed a high-level expression system of the HPV16 L1 in Escherichia coli for the purpose of VLP development. The native gene of HPV16 L1 has many rare codons that cause the early termination of translation and result in the production of truncated forms. First, we optimized the codon of the HPV16 L1 gene to the preferable codons of E. coli, and we succeeded in producing the full-size HPV16 L1 protein without early termination. Next, to find the best host for the production of HPV16 L1, we examined a total of eight E. coli strains, and E. coli BL21(DE3) with the highest yield among the strains was selected. With the selected host-vector system, we did a fed-batch cultivation in a lab-scale bioreactor. Two different feeding solutions (complex and defined feeding solutions) were examined and, when the complex feeding solution was used, a 6-fold higher production yield (4.6 g/l) was obtained compared with that with the defined feeding solution.

Recently, anoctamin1 (ANO1), a calcium-activated chloride channel, has been considered an important drug target, due to its involvement in various physiological functions, as well as its possibility for treatment of cancer, pain, diarrhea, hypertension, and asthma. Although several ANO1 inhibitors have been discovered by high-throughput screening, a discovery of new ANO1 inhibitors is still in the early phase, in terms of their potency and specificity. Moreover, there is no computational model to be able to identify a novel lead candidate of ANO1 inhibitor. Therefore, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore modeling approach was employed for identifying the essential chemical features to be required in the inhibition of ANO1. The pharmacophore hypothesis 2 (Hypo2) was selected as the best model based on the highest correlation coefficient of prediction on the test set (0.909). Hypo2 comprised a hydrogen bond acceptor, a hydrogen bond donor, a hydrophobic, and a ring aromatic feature with good statistics of the total cost (73.604), the correlation coefficient of the training set (0.969), and the root-mean-square deviation (RMSD) value (0.946). Hypo2 was well assessed by the test set, Fischer randomization, and leave-one-out methods. Virtual screening of the ZINC database with Hypo2 retrieved the 580 drug-like candidates with good potency and ADMET properties. Finally, two compounds were selected as novel lead candidates of ANO1 inhibitor, based on the molecular docking score and the interaction analysis. In this study, the best pharmacophore model, Hypo2, with notable predictive ability was successfully generated, and two potential leads of ANO1 inhibitors were identified. We believe that these compounds and the 3D-QSAR pharmacophore model could contribute to discovering novel and potent ANO1 inhibitors in the future.

Pathogen-derived nuclear localization signals (NLSs) enable vigorous nuclear invasion in the host by the virulence proteins harboring them. Herein, inspired by the robust nuclear import mechanism, we show that NLSs originating from the plant infection–associated Agrobacterium proteins VirD2 and VirE2 can be incorporated into the Cas9 system as efficient nuclear delivery enhancers, thereby improving the low gene-editing frequency in a model microalga, Chlamydomonas reinhardtii , caused by poor nuclear localization of the bulky nuclease. Prior to evaluation of the NLSs, IPA1 (Cre04.g215850) was first defined in the alga as the nuclear import-related importin alpha (Impα) that serves as a counterpart adaptor protein of the NLSs, based on extensive in silico analyses considering the protein’s sequence, tertiary folding behavior, and structural basis when interacting with a well-studied SV40TAg NLS. Through precursive affinity explorations, we reproducibly found that the NLSs mediated the binding between the Cas9 and Impα with nM affinities and visually confirmed that the fusion of the NLSs strictly localized the peptide-bearing cargoes in the microalgal nucleus without compensating for their cleavage ability. When employed in a real-world application, the VirD2 NLS increases the mutation frequency (~1.12 × 10 ⁻⁵ ) over 2.4-fold compared to an archetypal SV40TAg NLS (~0.46 × 10 ⁻⁵ ) when fused with Cas9. We demonstrate the cross-species versatility of the Impα-dependent strategy by successfully applying it to an industrial alga, Chlorella Sp. HS2. This work, focused on affinity augmentation, provides insights into increasing the frequency of gene editing, which can be advantageously used in programmable mutagenesis with broad applicability.

Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme’s metabolites and drugs. We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden’s index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness. In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence. This study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.

Solid-State Drives (SSDs) now power data centers, high-performance computing, and artifical intelligence workloads, but the increasing complexity of modern SSD controllers has surpassed the capabilities of traditional rule-based verification, necessitating scalable, data-driven testing methods. In this paper, we present ARTEMIS, a reinforcement learning (RL)-based framework for automatic SSD test case (TC) generation that operates directly on commercial devices. Our contributions are threefold. First, we formulate a novel RL problem, enabling seamless deployment across heterogeneous commercial products without requiring any device-specific customization. Second, to cope with the highly dynamic and non-stationary operating environments of SSDs, we introduce an ensemble-based inference mechanism that aggregates policies learned under diverse workload distributions, thereby improving generalization and robustness. Third, we validate the approach on two representative stress-testing tasks: Meta and User Garbage Collection. We show that the RL-driven tester discovers high-impact TCs that trigger critical blocking garbage collection events more frequently than conventional methods in diverse tasks.