AlphaFold2-based prediction of the co-condensation propensity of proteins.

The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates.

Multi-task bioassay pre-training for protein-ligand binding affinity prediction.

Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional (3D) structure of protein-ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. Although there is a vast amount of affinity data available in large-scale databases such as ChEMBL, issues such as inconsistent affinity measurement labels (i.e. IC50, Ki, Kd), different experimental conditions, and the lack of available 3D binding structures complicate the development of high-precision affinity prediction models using these data. To address these issues, we (i) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (ii) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked 3D structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP on the structure-based PLBA prediction task. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development. MBP web-server is now available for free at: https://huggingface.co/spaces/jiaxianustc/mbp.

Efficient Holes Abstraction by Precisely Decorating Ruthenium Single Atoms and RuOx Clusters on ZnIn2S4 for Photocatalytic Pure Water Splitting.

Developing efficient photocatalysts for two-electron water splitting with simultaneous H2O2 and H2 generation shows great promise for practical application. Currently, the efficiency of two-electron water splitting is still restricted by the low utilization of photogenerated charges, especially holes, of which the transfer rate is much slower than that of electrons. Herein, Ru single atoms and RuOx clusters are co-decorated on ZnIn2S4 (RuOx/Ru-ZIS) to employ as multifunctional sites for efficient photocatalytic pure water splitting. Doping of Ru single atoms in the ZIS basal plane enhances holes abstraction from bulk ZIS by regulating the electronic structure, and RuOx clusters offer a strong interfacial electric field to remarkably promote the out-of-plane migration of holes from ZIS. Moreover, Ru single atoms and RuOx clusters also serve as active sites for boosting surface water oxidation. As a result, an excellent H2 and H2O2 evolution rates of 581.9 µmol g-1 h-1 and 464.4 µmol g-1 h-1 is achieved over RuOx/Ru-ZIS under visible light irradiation, respectively, with an apparent quantum efficiency (AQE) of 4.36% at 400 nm. This work paves a new way to increase charge utilization by manipulating photocatalyst using single atom and clusters.

SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance.

Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistance. Therefore, quantitative estimations of how mutations would affect the interaction between a drug and the target protein would be of vital significance for the drug development and the clinical practice. Computational methods that rely on molecular dynamics simulations, Rosetta protocols, as well as machine learning methods have been proven to be capable of predicting ligand affinity changes upon protein mutation. However, the severely limited sample size and heavy noise induced overfitting and generalization issues have impeded wide adoption of machine learning for studying drug resistance. In this paper, we propose a robust machine learning method, termed SPLDExtraTrees, which can accurately predict ligand binding affinity changes upon protein mutation and identify resistance-causing mutations. Especially, the proposed method ranks training data following a specific scheme that starts with easy-to-learn samples and gradually incorporates harder and diverse samples into the training, and then iterates between sample weight recalculations and model updates. In addition, we calculate additional physics-based structural features to provide the machine learning model with the valuable domain knowledge on proteins for these data-limited predictive tasks. The experiments substantiate the capability of the proposed method for predicting kinase inhibitor resistance under three scenarios and achieve predictive accuracy comparable with that of molecular dynamics and Rosetta methods with much less computational costs.

Fabrication of Uniform Melatonin Microparticles Potentially for Nasal Delivery: A Comparison of Spray Drying and Spray Freeze Drying.

PURPOSE: Insomnia is a major health concern, and melatonin (MLT) is key for initiating sleep. Delivering MLT nasally can enhance brain bioavailability by targeting the olfactory region. This study aimed to fabricate MLT embedded microparticles for nasal delivery. METHODS: MLT-cyclodextrin (CD) derivatives complex microparticles (MCCMPs) were fabricated by spray drying and spray freeze drying MLT and CD derivative solutions. Phase solubility and 1H-1H ROSEY NMR analysis assessed MLT-CD assembly. The effects of formulation compositions and process parameters on microparticle structural attributes were investigated. The in vitro nasal release and deposition performances were evaluated by a modified paddle-over-disk apparatus and 3D-printed nasal cavity cast, respectively. RESULTS: Sodium sulphobutylether-ß-cyclodextrin (SBE-ß-CD) exhibited the best complexation ability with MLT, with the indole structure of MLT included in its cavity. Spray dried MCCMPs showed dense structure with high density, while the spray freeze dried counterpart showed the brittle and porous structure with low density. Despite the porous structure may promote the release rate of spray freeze dried samples, the high hydrophilicity of the CD derivative overshadows this advantage. Samples prepared by spray drying not only exhibited rapid release rates but also could deposit more effectively in the olfactory region, as they avoid breakage due to their higher mechanical strength. The optimal sample showed ~ 86.70% of the MLT released at 20 min and ~ 10.57% of the deposition fraction in the olfactory region. CONCLUSIONS: This work compares MCCMPs fabricated by spray drying and spray freeze drying, providing the optimal formulation and process combinations.

Preoperative H. pylori Eradication Therapy Facilitates Precise Delineation in Early Gastric Cancer with Current H. pylori Infection.

INTRODUCTION: Early gastric cancer with current Helicobacter pylori infection (HpC-EGC) is common, but it is still unclear whether H. pylori eradication therapy (Hp-ET) or endoscopic submucosal dissection (ESD) should be performed first. We evaluated Hp-ETs short-term effects on horizontal boundary delineations of HpC-EGC in ESD. METHODS: Prospectively enrolled HpC-EGC patients were randomly assigned to eradication or control groups. Operation scopes of HpC-EGC lesions were delineated with marking dots at 5 mm out of the endoscopic demarcation line by an independent endoscopist, unaware of eradication status, before formal circumferential incision. As representatives, precise delineation rate, the shortest distance of all marking dots to the pathological demarcation line in all slices of one intact resected specimen (Dmin), and negative marking dot specimen rate were examined. RESULTS: Twenty-three HpC-EGC patients (25 lesions) were allocated to eradication group and 26 patients (27 lesions) were allocated to the control group with similar eradication success rates and all were differentiated type. With improving background mucosa inflammation after Hp-ET and similar gastritis-like epithelium rates, 10 lesions (40.0%) in the eradication group were of precise delineation compared to control group with 2 lesions (7.4%) (relative risk = 5.40, 95% CI 1.31-22.28). Dmin of eradication and control groups were 4.17 ± 2.52 mm and 2.67 ± 2.30 mm (p = 0.029), accompanied by 4 (14.8%) and none (0.0%) specimens that exhibited positive marking dots (p = 0.11), respectively. CONCLUSION: For HpC-EGC patients, administrating eradication medication before ESD is beneficial for the precise delineation of lesions and reducing the risk of positive horizontal resection margins.

TrimNet: learning molecular representation from triplet messages for biomedicine.

MOTIVATION: Computational methods accelerate drug discovery and play an important role in biomedicine, such as molecular property prediction and compound-protein interaction (CPI) identification. A key challenge is to learn useful molecular representation. In the early years, molecular properties are mainly calculated by quantum mechanics or predicted by traditional machine learning methods, which requires expert knowledge and is often labor-intensive. Nowadays, graph neural networks have received significant attention because of the powerful ability to learn representation from graph data. Nevertheless, current graph-based methods have some limitations that need to be addressed, such as large-scale parameters and insufficient bond information extraction. RESULTS: In this study, we proposed a graph-based approach and employed a novel triplet message mechanism to learn molecular representation efficiently, named triplet message networks (TrimNet). We show that TrimNet can accurately complete multiple molecular representation learning tasks with significant parameter reduction, including the quantum properties, bioactivity, physiology and CPI prediction. In the experiments, TrimNet outperforms the previous state-of-the-art method by a significant margin on various datasets. Besides the few parameters and high prediction accuracy, TrimNet could focus on the atoms essential to the target properties, providing a clear interpretation of the prediction tasks. These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning. AVAILABILITY: The quantum and drug datasets are available on the website of MoleculeNet: http://moleculenet.ai. The source code is available in GitHub: https://github.com/yvquanli/trimnet. CONTACT: xjyao@lzu.edu.cn, songsen@tsinghua.edu.cn.

Discrete Time Crystal Enabled by Stark Many-Body Localization.

Discrete time crystals (DTCs) have recently attracted increasing attention, but most DTC models and their properties are only revealed after disorder average. In this Letter, we propose a simple disorder-free periodically driven model that exhibits nontrivial DTC order stabilized by Stark many-body localization (MBL). We demonstrate the existence of the DTC phase by analytical analysis from perturbation theory and convincing numerical evidence from observable dynamics. The new DTC model paves a new promising way for further experiments and deepens our understanding of DTCs. Since the DTC order does not require special quantum state preparation and the strong disorder average, it can be naturally realized on the noisy intermediate-scale quantum hardware with much fewer resources and repetitions. Moreover, in addition to the robust subharmonic response, there are other novel robust beating oscillations in the Stark-MBL DTC phase that are absent in random or quasiperiodic MBL DTCs.

A single-center experience on endoscopic ultrasonography-guided ethanol ablation of insulinomas.

BACKGROUND/OBJECTIVES: As the most frequent functional pancreatic neuroendocrine tumor, insulinomas may cause a plethora of symptoms and severe impairment in the living of patients by endogenous hyperinsulinemia and subsequent hypoglycemia. Surgery has been regarded as the first choice although a high risk of complications. Ethanol ablation is a promising non-surgical option that could achieve tumor shrinking in a short-term period. But the impact of symptom control and the long-term efficacy lack sufficient and good-quality evidence. METHODS: A total number of 14 endoscopic ultrasonography-guided ethanol ablations were performed in 9 patients between September 2016 and September 2018 in Peking Union Medical College Hospital. The data were collected and prospectively analyzed. RESULTS: The follow-up duration ranged from 21 to 1567 days in 9 patients, with a median of 994 days. 4 patients were free from relapse during a median follow-up of 1108 days (range: 994-1567 days). In 5 patients who suffered relapses, the median duration with symptom relief after the first ablation was 128 days (range: 13-393 days). If only repeated ablation was taken into consideration, the median duration with symptom relief was 26 days (range: 1-516 days). No complications happened during the procedures. The severe complication rate after the first ablation was 0.0% (0/9), compared to 7.14% (1/14) if each procedure was counted separately. The only severe complication documented was acute pancreatitis which was completely relieved after symptomatic treatment. CONCLUSIONS: For patients who are not suitable for surgical resections, endoscopic ultrasonography-guided ethanol ablation of insulinomas could be an effective and safe alternative to relieve symptoms of hypoglycemia.

Endoscopic ultrasound-guided tissue acquisition with or without rapid on-site evaluation for solid pancreatic lesions: five years of experience from a single center.

BACKGROUND: Endoscopic ultrasound (EUS)-guided tissue acquisition (TA) by EUS-guided fine needle aspiration (FNA) or fine needle biopsy (FNB) is a standard diagnostic procedure for solid pancreatic lesions. Whether rapid on-site evaluation (ROSE) should be used to support EUS-TA remains controversial. Here we assessed the diagnostic performance of EUS-TA with or without self-ROSE for solid pancreatic masses. METHODS: Three hundred and seventy EUS-TA cases with self-ROSE and 244 cases without ROSE were retrospectively enrolled between August 2018 and June 2022. All procedures including ROSE were performed by the attending endoscopist. Clinical data, EUS characteristics, and diagnostic performance for distinguishing benign from malignant solid pancreatic masses including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were compared between groups. RESULTS: Self-ROSE improved the diagnostic accuracy of solid pancreatic lesions by 16.7% in the EUS-TA group (p < 0.001) and by 18.9% in the EUS-FNA alone group (p < 0.001). Self-ROSE also improved the diagnostic sensitivity by 18.6% in the EUS-TA group (p < 0.001) and by 21.2% in the EUS-FNA alone group (p < 0.001). Improvements in the diagnostic accuracy by self-ROSE in the EUS-FNB group were not significant. 2.2 ± 0.7, 2.4 ± 0.9, 2.3 ± 0.7, 2.5 ± 0.9, 2.1 ± 0.6, and 2.1 ± 0.7 needle passes were required in the EUS-TA, EUS-FNA, and EUS-FNB with or without self-ROSE groups, respectively. CONCLUSIONS: Self-ROSE significantly improved the accuracy and sensitivity of EUS-FNA alone and EUS-TA diagnosis of solid pancreatic lesions and helped to reduce needle passes during the procedure. Whether self-ROSE benefits EUS-FNB and whether EUS-FNB alone is comparable to EUS-FNA with self-ROSE require further clarification.

Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes.

Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.

FDP-Na-induced enhancement of glycolysis impacts larval growth and development and chitin biosynthesis in fall webworm, Hyphantria cunea (Lepidoptera: Arctiidae).

Fructose 1, 6-diphosphate (FDP) is an endogenous intermediate in the glycolytic pathway, as well as an allosteric activator of phosphofructokinase (PFK). Based on the role in promoting glycolysis, FDP has been widely used as a therapeutic agent for mitigating the damage of endotoxemia and ischemia/reperfusion in clinical practice. However, the effect of exogenous FDP-induced glycolysis activation on insect carbohydrate metabolism and chitin synthesis remains largely unclear. Here, we investigated for the first time the effects of FDP-Na, an allosteric activator of PFK, on the growth and development of Hyphantria cunea larvae, a serious defoliator in agriculture and forestry, especially on glycolysis and chitin synthesis. The results showed that FDP-Na significantly restrained the growth and development of H. cunea larvae and resulted in larval lethality. After treatment with FDP-Na, hexokinase (HK), phosphofructokinase (PFK) and pyruvate kinase (PK) were significantly activated, and HcHK2, HcPFK, HcPK were dramatically upregulated, which suggested that FDP-Na enhanced glycolysis in H. cunea larvae. Meanwhile, FDP-Na also distinctly impacted chitin biosynthesis by disturbing transcriptions of genes in the chitin synthesis pathway, resulting in changes of chitin contents in the midgut and epidermis of H. cunea larvae. Therefore, we considered that FDP-Na caused the growth and development arrest, and impacted chitin biosynthesis, probably by disturbing in vivo glycolysis and carbohydrate metabolism in H. cunea larvae. The findings provide a new perspective on the mechanism by which glycolysis regulates insect growth and development, and lay the foundation for exploring the potential application of glycolysis activators in pest control as well.

Diabetes mellitus in patients with type 1 autoimmune pancreatitis at diagnosis and after corticosteroid therapy.

BACKGROUND: A high prevalence of diabetes mellitus (DM) coexisting with autoimmune pancreatitis (AIP) is observed. However, evidence on the circumstances under which corticosteroid therapy (CST) for AIP improves or worsens DM is scarce. This study aimed to demonstrate and identify predictors of DM control under the influence of CST. METHODS: Patients diagnosed with type 1 AIP were enrolled from a prospectively maintained cohort and were classified into three groups according to the chronology in which AIP and DM were diagnosed: pre-existing DM (pDM), concurrent DM (cDM), and non-DM (nDM). The responses of DM to CST were assessed when corticosteroid was ceased or tapered to a maintenance dose and classified as 'improvement' and 'non-improvement' (including 'no change' and 'exacerbation'). RESULTS: Among 101 patients with type 1 AIP, 52 (51.5%) patients were complicated with DM at the time of AIP diagnosis, with 36 patients in the cDM group and 16 patients in the pDM group. The incidences of diffuse pancreatic swelling (72.2%) and pancreatic body/tail involvement (91.7%) were significantly higher in the cDM group than in both the pDM and nDM groups. Of the 52 patients with DM, CST was administered in 48 cases. Multivariate logistic analysis identified that elevated serum gamma-glutamyl transferase (GGT) level at AIP diagnosis [odds ratio (OR) = 0.032, 95% confidence interval (CI): 0.003-0.412, P = 0.008] and pancreatic atrophy after CST (OR = 0.027, 95% CI: 0.003-0.295, P = 0.003) were negatively associated with DM control improvement. CONCLUSIONS: Patients with diffuse pancreatic swelling and pancreatic body/tail involvement in pancreatitis tended to be complicated with cDM at AIP diagnosis. CST exerted a beneficial effect on the clinical course of DM in nearly half of the AIP patients complicated with DM at diagnosis, particularly in those without elevated serum GGT levels at diagnosis and who did not experience pancreatic atrophy after CST.

Spray freeze dried niclosamide nanocrystals embedded dry powder for high dose pulmonary delivery.

Based on the drug repositioning strategy, niclosamide (NCL) has shown potential applications for treating COVID-19. However, the development of new formulations for effective NCL delivery is still challenging. Herein, NCL-embedded dry powder for inhalation (NeDPI) was fabricated by a novel spray freeze drying technology. The addition of Tween-80 together with 1,2-Distearoyl-sn-glycero-3-phosphocholine showed the synergistic effects on improving both the dispersibility of primary NCL nanocrystals suspended in the feed liquid and the spherical structure integrity of the spray freeze dried (SFD) microparticle. The SFD microparticle size, morphology, crystal properties, flowability and aerosol performance were systematically investigated by regulating the feed liquid composition and freezing temperature. The addition of leucine as the aerosol enhancer promoted the microparticle sphericity with greatly improved flowability. The optimal sample (SF- 80D-N20L2D2T1) showed the highest fine particle fraction of ∼47.83%, equivalently over 3.8 mg NCL that could reach the deep lung when inhaling 10 mg dry powders.

Chemical Modification of Polysaccharides: A Review of Synthetic Approaches, Biological Activity and the Structure-Activity Relationship.

Natural polysaccharides are macromolecular substances with great potential owing to their wide biological activity and low toxicity. However, not all polysaccharides have significant pharmacodynamic activity; hence, appropriate chemical modification methods can be selected according to the unique structural characteristics of polysaccharides to assist in enhancing and promoting the presentation of their biological activities. This review summarizes research progress on modified polysaccharides, including common chemical modification methods, the change in biological activity following modification, and the factors affecting the biological activity of chemically modified polysaccharides. At the same time, the difficulties and challenges associated with the structural modification of natural polysaccharides are also outlined in this review. Thus, research on polysaccharide structure modification is critical for improving the development and utilization of sugar products.

Variational Quantum-Neural Hybrid Eigensolver.

The variational quantum eigensolver (VQE) is one of the most representative quantum algorithms in the noisy intermediate-scale quantum (NISQ) era, and is generally speculated to deliver one of the first quantum advantages for the ground-state simulations of some nontrivial Hamiltonians. However, short quantum coherence time and limited availability of quantum hardware resources in the NISQ hardware strongly restrain the capacity and expressiveness of VQEs. In this Letter, we introduce the variational quantum-neural hybrid eigensolver (VQNHE) in which the shallow-circuit quantum Ansatz can be further enhanced by classical post-processing with neural networks. We show that the VQNHE consistently and significantly outperforms the VQE in simulating ground-state energies of quantum spins and molecules given the same amount of quantum resources. More importantly, we demonstrate that, for arbitrary postprocessing neural functions, the VQNHE only incurs a polynomial overhead of processing time and represents the first scalable method to exponentially accelerate the VQE with nonunitary postprocessing that can be efficiently implemented in the NISQ era.

ALipSol: An Attention-Driven Mixture-of-Experts Model for Lipophilicity and Solubility Prediction.

Lipophilicity (logD) and aqueous solubility (logSw) play a central role in drug development. The accurate prediction of these properties remains to be solved due to data scarcity. Current methodologies neglect the intrinsic relationships between physicochemical properties and usually ignore the ionization effects. Here, we propose an attention-driven mixture-of-experts (MoE) model named ALipSol, which explicitly reproduces the hierarchy of task relationships. We adopt the principle of divide-and-conquer by breaking down the complex end point (logD or logSw) into simpler ones (acidic pKa, basic pKa, and logP) and allocating a specific expert network for each subproblem. Subsequently, we implement transfer learning to extract knowledge from related tasks, thus alleviating the dilemma of limited data. Additionally, we substitute the gating network with an attention mechanism to better capture the dynamic task relationships on a per-example basis. We adopt local fine-tuning and consensus prediction to further boost model performance. Extensive evaluation experiments verify the success of the ALipSol model, which achieves RMSE improvement of 8.04%, 2.49%, 8.57%, 12.8%, and 8.60% on the Lipop, ESOL, AqSolDB, external logD, and external logS data sets, respectively, compared with Attentive FP and the state-of-the-art in silico tools. In particular, our model yields more significant advantages (Welch's t-test) for small training data, implying its high robustness and generalizability. The interpretability analysis proves that the atom contributions learned by ALipSol are more reasonable compared with the vanilla Attentive FP, and the substitution effects in benzene derivatives agreed well with empirical constants, revealing the potential of our model to extract useful patterns from data and provide guidance for lead optimization.

Hypomagnesemia in intestinal lymphangiectasia: a case report and review of the literature.

BACKGROUND: Intestinal lymphangiectasia (IL) is a rare disease characterized by dilation of lymphatic vessels and leakage of lymphatic fluids into the intestinal lumen, causing depletion of lymphocytes, protein, lipids, fat-soluble vitamins, and electrolytes. Hypomagnesemia can occur in IL patients but is seldom discussed. CASE PRESENTATION: A 30-year-old Tibetan woman who had chronic diarrhea, edema, tetany, and tingling was diagnosed with IL. Prominent hypomagnesemia was noticed. She was treated with a medium-chain triglyceride (MCT) diet and nutrient supplementation with satisfactory results. We also present a systematic review of hypomagnesemia in IL cases from the published literature. CONCLUSIONS: Hypomagnesemia may be an overlooked complication of IL, thus monitoring serum magnesium concentrations in IL patients is crucial.

Novel ocotillol-derived lactone derivatives: design, synthesis, bioactive evaluation, SARs and preliminary antibacterial mechanism.

A new series of ocotillol-derived lactone derivatives were designed and synthesized to consider their antibacterial activity, structure-activity relationships (SARs), antibacterial mechanism and in vivo antibacterial efficacy. Compound 6d, which exhibited broad antibacterial spectrum, was found to be the most active with minimum inhibitory concentrations (MICs) of 1-2 µg/mL against Gram-positive bacteria and 8-16 µg/mL against Gram-negative bacteria. The subsequent synergistic antibacterial tests displayed that 6d had the ability to improve the susceptibility of MRSA USA300, B. subtilis 168, and E. coli DH5α to kanamycin and chloramphenicol. This active molecule 6d also induced bacterial resistance more slowly than norfloxacin and kanamycin. Furthermore, compound 6d was membrane active and low toxic against mammalian cells, and it could rapidly inhibit the growth of MRSA and E. coli and did not obviously trigger bacterial resistance. Compound 6d also displayed strong in vivo antibacterial activity against S. aureus RN4220 in murine corneal infection models. Additionally, absorption, distribution, metabolism, and excretion properties of this type of compounds have shown drug-likeness with good oral absorption and moderate blood-brain barrier permeability. The obtained results demonstrated that ocotillol-derived compounds are a promising class of antibacterial agents worthy of further study.

E74 knockdown represses larval development and chitin synthesis in Hyphantria cunea.

E74 is a key transcription factor induced by 20E, which plays a broad role in many physiological events during insect growth and development, including vitellogenesis, organ remodeling and new tissue formation, programmed cell death and metamorphosis. However, whether it is involved in regulating insect chitin biosynthesis remains largely unclear. Here, the E74 gene was identified for the first time from Hyphantria cunea, a notorious defoliator of forestry. Thereafter, the role of HcE74 in regulating growth, development and chitin synthesis in H. cunea larvae was evaluated. Bioinformatics analysis showed that HcE74 shared the highest identity (95.53%) with E74A of Spodoptera litura, which belonged to Ets superfamily. The results of RNAi bioassay showed that the larval mortality on 6 d after HcE74 knockdown was up to 51.11 ± 6.94%. Meanwhile, a distinct developmental deformity phenotype was found when HcE74 was silenced. These results indicated that HcE74 plays an important role in the development and molting of H. cunea larvae. Moreover, HcE74 knockdown also significantly decreased the expression of four key genes related to chitin synthesis, including glucose-6-phosphate isomerase (HcG6PI), UDP-N-acetylglucosamine pyrophosphorylase (HcUAP), chitin synthetase A (HcCHSA), and chitin synthetase B (HcCHSB). As a result, the content of chitin in midgut and epidermis decreased by 0.54- and 0.08-fold, respectively. Taken together, these results demonstrated that HcE74 not only plays a critical role in the growth and molting of H. cunea larvae, but also probably participates in the transcriptional regulation of genes involved in chitin biosynthesis.