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Abstract Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 14 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC50) of 1.9 μM. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field.

Abstract Background Flavivirus is a challenge all over the world. The replication of flavivirus takes place within membranous replication compartments (RCs) derived from endoplasmic reticulum (ER). Flavivirus NS1 proteins have been proven essential for the formation of viral RCs by remodeling the ER. The glycosylation of flavivirus NS1 proteins is important for viral replication, yet the underlying mechanism remains unclear. Methods HeLa cells were used to visualize the ER remodeling effects induced by NS1 expression. ZIKV replicon luciferase assay was performed with BHK-21 cells. rZIKV was generated from BHK-21 cells and the plaque assay was done with Vero Cells. Liposome co-floating assay was performed with purified NS1 proteins from 293T cells. Results We found that the glycosylation of flavivirus NS1 contributes to its ER remodeling activity. Glycosylation deficiency of NS1, either through N-glycosylation sites mutations or tunicamycin treatment, compromises its ER remodeling activity and interferes with viral RCs formation. Disruption of NS1 glycosylation results in abnormal aggregation of NS1, rather than reducing its membrane-binding activity. Consequently, deficiency in NS1 glycosylation impairs virus replication. Conclusions In summary, our results highlight the significance of NS1 glycosylation in flavivirus replication and elucidate the underlying mechanism. This provides a new strategy for combating flavivirus infections.

ObjectiveHigh-precision localization technology serves as the crucial foundation in enabling the autonomous navigation operations of intelligent agricultural robots. However, the traditional global navigation satellite system (GNSS) localization method faces numerous limitations, such as tree shadow, electromagnetic interference, and other factors in the agricultural environment brings challenges to the accuracy and reliability of localization technology. To address the deficiencies and achieve precise localization of agricultural robots independent of GNSS, a localization method was proposed based on the fusion of three-dimensional light detection and ranging (LiDAR) data and inertial measurement unit (IMU) information to enhance localization accuracy and reliability.MethodsLiDAR was used to obtain point cloud data in the agricultural environment and realize self-localization via point cloud matching. By integrating real-time motion parameter measurements from the IMU with LiDAR data, a high-precision localization solution for agricultural robots was achieved through a specific fusion algorithm. Firstly, the LiDAR-obtained point cloud data was preprocessed and the depth map was used to save the data. This approach could reduce the dimensionality of the original LiDAR point cloud, and eliminate the disorder of the original LiDAR point cloud arrangement, facilitating traversal and clustering through graph search. Given the presence of numerous distinct crops like trees in the agricultural environment, an angle-based clustering method was adopted. Specific angle-based clustering criteria were set to group the point cloud data, leading to the segmentation of different clusters of points, and obvious crops in the agricultural environment was effectively perceived. Furthermore, to improve the accuracy and stability of positioning, an improved three-dimensional normal distribution transform (3D-NDT) localization algorithm was proposed. This algorithm operated by matching the LiDAR-scanned point cloud data in real time with the pre-existing down sampled point cloud map to achieve real-time localization. Considering that direct down sampling of LiDAR point clouds in the agricultural environment could result in the loss of crucial environmental data, a point cloud clustering operation was used in place of down sampling operation, thereby improving matching accuracy and positioning precision. Secondly, to address potential constraints and shortcomings of using a single sensor for robot localization, a multi-sensor information fusion strategy was deployed to improve the localization accuracy. Specifically, the extended Kalman filter algorithm (EKF) was chosen to fuse the localization data from LiDAR point cloud and the IMU odometer information. The IMU provided essential motion parameters such as acceleration and angular velocity of the agricultural robot, and by combining with the LiDAR-derived localization information, the localization of the agricultural robot could be more accurately estimated. This fusion approach maximized the advantages of different sensors, compensated for their individual limitations, and improved the overall localization accuracy of the agricultural robot.Results and DiscussionsA series of experimental results in the Gazebo simulation environment of the robot operating system (ROS) and real operation scenarios showed that the fusion localization method proposed had significant advantages. In the simulation environment, the average localization errors of the proposed multi-sensor data fusion localization method were 1.7 and 1.8 cm, respectively, while in the experimental scenario, these errors were 3.3 and 3.3 cm, respectively, which were significantly better than the traditional 3D-NDT localization algorithm. These findings showed that the localization method proposed in this study could achieve high-precision localization in the complex agricultural environment, and provide reliable localization assistance for the autonomous functioning of agricultural robots.ConclusionsThe proposed localization method based on the fusion of LiDAR data and IMU information provided a novel localization solution for the autonomous operation of agricultural robots in areas with limited GNSS reception. Through the comprehensive utilization of multi-sensor information and adopting advanced data processing and fusion algorithms, the localization accuracy of agricultural robots could be significantly improved, which could provide a new reference for the intelligence and automation of agricultural production.

Abstract Background Gut microbiome metabolites are important modulators of host health and disease. However, the overall metabolic potential of the gut microbiome and interactions with the host organs have been underexplored. Results Using stable isotope resolved metabolomics (SIRM) in mice orally gavaged with 13C-inulin (a tracer), we first observed dynamic enrichment of 13C-metabolites in cecum contents in the amino acids and short-chain fatty acid metabolism pathways. 13C labeled metabolites were subsequently profiled comparatively in plasma, liver, brain, and skeletal muscle collected at 6, 12, and 24 h after the tracer administration. Organ-specific and time-dependent 13C metabolite enrichments were observed. Carbons from the gut microbiome were preferably incorporated into choline metabolism and the glutamine-glutamate/GABA cycle in the liver and brain, respectively. A sex difference in 13C-lactate enrichment was observed in skeletal muscle, which highlights the sex effect on the interplay between gut microbiome and host organs. Choline was identified as an interorgan metabolite derived from the gut microbiome and fed the lipogenesis of phosphatidylcholine and lysophosphatidylcholine in host organs. In vitro and in silico studies revealed the de novo synthesis of choline in the human gut microbiome via the ethanolamine pathway, and Enterococcus faecalis was identified as a major choline synthesis species. These results revealed a previously underappreciated role for gut microorganisms in choline biosynthesis. Conclusions Multicompartmental SIRM analyses provided new insights into the current understanding of dynamic interorgan metabolite transport between the gut microbiome and host at the whole-body level in mice. Moreover, this study singled out microbiota-derived metabolites that are potentially involved in the gut-liver, gut-brain, and gut-skeletal muscle axes. Video Abstract

Connection of microstructure with corrosion properties of an Al-3.62Cu-0.9Mg-0.45Ag alloy in natural aging (NA) state and peak aging (PA) state at different final rolling temperatures (20 °C, 180 °C and 280 °C) are studied by scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), transmission electron microscopy (TEM), X-ray diffraction (XRD), texture and electrochemical analysis. The results show that the deformation texture and recrystallization texture of T280-NA alloy account for the largest percentage with the largest degree of recrystallization. With the increase of final rolling temperature, the proportion of low angle grain boundaries (LAGBs) of NA alloy increases, the diameter, thickness and volume fraction of Ω phase in PA alloy decrease, and the grain boundary precipitates (GBPs) are gradually transformed from discontinuous distribution to continuous distribution. The grain boundary precipitates are also found to be consist of alternating S and θ phases and the width of precipitation free zones (PFZs) is essentially independent of final rolling temperature. The intergranular corrosion (IGC) resistance of T280-NA alloy and T20-PA alloy is the best, which is a result of the combined effect of GBPs, texture and grain boundary properties. In addition, this result is verified in this paper by studying the electrochemical properties of the alloy.

Abstract Amino acid availability is monitored by animals to adapt to their nutritional environment. Beyond gustatory receptors and systemic amino acid sensors, enteroendocrine cells (EECs) are believed to directly percept dietary amino acids and secrete regulatory peptides. However, the cellular machinery underlying amino acid-sensing by EECs and how EEC-derived hormones modulate feeding behavior remain elusive. Here, by developing tools to specifically manipulate EECs, we find that Drosophila neuropeptide F (NPF) from mated female EECs inhibits feeding, similar to human PYY. Mechanistically, dietary L-Glutamate acts through the metabotropic glutamate receptor mGluR to decelerate calcium oscillations in EECs, thereby causing reduced NPF secretion via dense-core vesicles. Furthermore, two dopaminergic enteric neurons expressing NPFR perceive EEC-derived NPF and relay an anorexigenic signal to the brain. Thus, our findings provide mechanistic insights into how EECs assess food quality and identify a conserved mode of action that explains how gut NPF/PYY modulates food intake.

Abstract The release of cellular DNA as neutrophil extracellular traps (NETs) plays a pivotal role in the immune response to pathogens by physically entrapping and killing microbes. NET release occurs at a greater frequency within neutrophil clusters and swarms, indicating a potential for collective behavior. However, little is known about how dense clustering of cells influences the frequency of NET release. Using an image-based assay for NETosis in nanowells, we show that the frequency of NETosis increases with cell density. We then co-incubate NETotic neutrophils with naïve neutrophils and find that NETotic neutrophils can induce secondary NETosis in naïve neutrophils in a cell density-dependent manner. Further mechanistic studies show that secondary NETosis is caused by a combination of DNA and protein factors. Finally, we immobilize NETotic neutrophils in a plaque, and then place the plaque near naïve neutrophils to characterize the spatial propagation of secondary NETosis. We find that secondary NETosis from naïve neutrophils increases over time, but remains spatially restricted to the periphery of the plaque. Together, we show that NETosis is an auto-amplified process, but that the spatial propagation of NET release is strictly regulated.

Mylabris sibirica is a hypermetamorphic insect whose adults feed on oilseed rape. However, due to a shortage of effective and appropriate endogenous references, studies on molecular functional genes in Mylabris sibirica, have been tremendously limited. In this study, ten internal reference genes (ACT, ARF1, AK, EF1α, GAPDH, α-TUB, RPL6, RPL13, RPS3 and RPS18) were tested and assessed under four selected treatments including adult ages, adult tissues, temperatures, and sex by RT-qPCR based on five methods (Ct value, geNorm, NormFinder, BestKeeper and RefFinder). Our findings showed that RPL6 and RPL13 were the most optimal internal reference gene combination for gene expression during various adult ages and under diverse temperatures; The combination of RPL6 and RPS18 was recommended to test gene transcription levels under different adult tissues. AK and RPL6 were the best reference genes in male and female adults. RPL6 and RPL13 were the most appropriate reference gene pair to estimate gene expression levels under four different tested backgrounds. The relative transcript levels of a uridine diphosphate (UDP)-N-acetylglucosamine-pyrophosphorylase (MsUAP), varied greatly according to normalization with the two most- and least-suited reference genes. This study will lay the basis for further molecular physiology and biochemistry studies in M. sibirica, such as development, reproduction, sex differentiation, cold and heat resistance.

IntroductionThe soybean hawkmoth, Clanis bilineata tsingtauica, is an edible insect that possesses high nutritional, medicinal and economic value. It has developed into a characteristic agricultural industry in China.MethodsThe dominant gut bacterium in diapause larvae of soybean hawkmoths was identified by metagenomics, and the effect of diapause time on gut microbiome composition, diversity and function was investigated.ResultsEnterococcus and Enterobacter were measured to be the dominant genera, with Enterococcus casseliflavus and Enterococcus pernyi being the dominant species. Compared to the controls, the relative abundance of E. casseliflavus and E. pernyi on day 14 was lower by 54.51 and 42.45%, respectively. However, the species richness (including the index of Chao and ACE) of gut microbiota increased on day 28 compared to controls. The gene function was mainly focused on carbohydrate and amino acid metabolism. Metabolic pathways annotated for amino acids on day 14 increased by 9.83% compared to controls. It is speculated that diapause soybean hawkmoths may up-regulate amino acid metabolism by reducing E. casseliflavus abundance to maintain their nutritional balance. Additionally, tetracycline, chloromycetin and ampicillin were screened as the top three antibiotics against E. casseliflavus.DiscussionThis study not only extends our knowledge of gut microbiome in soybean hawkmoths at the species level, but also provides an initial investigation of gene functionality in interaction with insect hosts.

Abstract Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2 Mb from target genes. Coupled with a training strategy that predicts gene expression from flanking candidate cis-regulatory elements (cCREs), CREaTor can model cell type-specific cis-regulatory patterns in new cell types without prior knowledge of cCRE-gene interactions or additional training. The zero-shot modeling capability, combined with the use of only RNA-seq and ChIP-seq data, allows for the ready generalization of CREaTor to a broad range of cell types.