Reasoning with the inverse of 3D cardinal direction relations based on direction matrices.

In order to improve the ability of intelligent reasoning and prediction of 3DR27 model which presented in our previous work, enhance the usability of this model, and better fulfill the demands of real applications for spatial database, we focused on the problem of reasoning with the inverse of 3D cardinal direction relations between spatial objects. In order to realize automated reasoning, an algorithm for computing the inverse of 3D cardinal direction relations based on matrices is proposed on the basis of the mapping between the 3D rectangular cardinal direction relations and 3D interval relation matrix. This algorithm improves the power of reasoning for this model by means of the excellent properties of matrix operations. Theorems are provided to prove formally that our algorithm is correct and complete. This study realized the automatic inference and calculation of the inverse of the 3D cardinal direction relations based on 3DR27 model and further improved the ability of spatial reasoning and spatial analysis of spatial database.

Green investor behavior and corporate green innovation: Evidence from Chinese listed companies.

Businesses embracing green innovation can encourage high-quality green economic development in addition to reducing emissions. In this paper, we use the Difference-in-Differences (DID) to investigate the influence of green investor behavior on the green innovation of companies, using the first-ever green investor investment in a company as a quasi-natural experiment. According to research, green investors have the power to accelerate corporate green innovation greatly. Three key strategies that green investors can use to do this include raising institutional investment levels, enhancing the green perception of executives, and bringing in top talent. Heterogeneity analysis shows that non-high-polluting, big, and state-owned enterprises (SOEs) are more likely to benefit from green investors' green innovation effects. Further analysis reveals that (ⅰ) green investors' influence on an enterprise's level of green innovation can help it improve its ESG ratings; (ii) green investors can encourage green innovation in source control but have little effect on green innovation in end-of-pipe treatment; (ⅲ) green investors can support both non-green and green innovation in enterprises, but have a greater influence on green innovation. This study strengthens the micro relationship between green investors and corporate green innovation. It also supports the theoretical underpinnings of corporate green innovation, which is significant for advancing green innovation, environmental protection, and high-quality economic development in emerging economies.

Neutrophil in the suppressed immune microenvironment: Critical prognostic factor for lung adenocarcinoma patients with KEAP1 mutation.

Purpose: It is still unclear whether KEAP1 mutation is detrimental to immunotherapy of lung adenocarcinoma (LUAD) patients, we try to analyse the exact changes in the TME in LUAD patients with KEAP1 mutations and to identify key factors influencing prognosis. Experimental design: A total of 1,029 patients with lung squamous carcinoma (LUSC) or LUAD with data obtained from The Cancer Genome Atlas were included in this study. The TME and OS of patients with LUAD stratified by mutant versus wild-type KEAP1 status were comprehensively measured. Moreover, we classified LUAD patients with KEAP1 mutations into three subtypes, by unsupervised consensus clustering. We further analysed the TME, OS, commutated genes and metabolic pathways of different subgroups. A total of 40 LUAD patients underwent immunotherapy were collected and classified into mutant KEAP1 group and wild-type KEAP1 group. We also conducted immunohistochemical staining in KEAP1-MT groups. Result: Suppressed TME was observed not only in LUAD patients but also in LUSC patients. LUAD patients with mutant KEAP1 underwent immunotherapy had worse PFS than wild-type KEAP1. Unsupervised consensus clustering analysis suggested that the three subtypes of patients exhibited different densities of neutrophil infiltration and had different OS results: cluster 2 patients had significantly higher levels of neutrophils had significantly worse prognoses than those of patients in clusters 1 and 3 and patients with wild-type KEAP1. Univariate and multivariate Cox analyses proved that a high density of neutrophils was significantly associated with worse OS and immunohistochemical staining proved that shorter PFS showed high density of neutrophils. Conclusion: KEAP1 mutation significantly suppresses the tumour immune microenvironment in LUAD patients. LUAD patients with mutant KEAP1 underwent immunotherapy had worse PFS than with wild-type KEAP1. Neutrophils may play an important role in the prognosis of LUAD patients with KEAP1 mutations and may provide a promising therapeutic target.

Medical image registration via neural fields.

Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images. Recent learning-based methods, trained to directly predict transformations between two images, run much faster, but suffer from performance deficiencies due to domain shift. Here we present a new neural network based image registration framework, called NIR (Neural Image Registration), which is based on optimization but utilizes deep neural networks to model deformations between image pairs. NIR represents the transformation between two images with a continuous function implemented via neural fields, receiving a 3D coordinate as input and outputting the corresponding deformation vector. NIR provides two ways of generating deformation field: directly output a displacement vector field for general deformable registration, or output a velocity vector field and integrate the velocity field to derive the deformation field for diffeomorphic image registration. The optimal registration is discovered by updating the parameters of the neural field via stochastic mini-batch gradient descent. We describe several design choices that facilitate model optimization, including coordinate encoding, sinusoidal activation, coordinate sampling, and intensity sampling. NIR is evaluated on two 3D MR brain scan datasets, demonstrating highly competitive performance in terms of both registration accuracy and regularity. Compared to traditional optimization-based methods, our approach achieves better results in shorter computation times. In addition, our methods exhibit performance on a cross-dataset registration task, compared to the pre-trained learning-based methods.

Targeting HTR2B suppresses non-functioning pituitary adenoma growth and sensitizes cabergoline treatment via inhibiting Gαq/PLC/PKCγ/STAT3 axis.

BACKGROUND: Managing non-functioning pituitary adenomas (NFPAs) is difficult due to limited drug treatments. Cabergoline's (CAB) effectiveness for NFPAs is debated. This study explores the role of HTR2B in NFPAs and its therapeutic potential. METHODS: We conducted screening of bulk RNA-sequencing data to analyze HTR2B expression levels in NFPA samples. In vitro and in vivo experiments were performed to evaluate the effects of HTR2B modulation on tumor growth and cell cycle regulation. Mechanistic insights into the HTR2B-mediated signaling pathway were elucidated using pharmacological inhibitors and molecular interaction assays. RESULTS: Elevated HTR2B expression was detected in NFPA samples, which was associated with increased tumor survival. Inhibition of HTR2B activity resulted in the suppression of tumor growth through modulation of the G2M cell cycle. The inhibition of HTR2B with PRX-08066 was found to block STAT3 phosphorylation and nuclear translocation by interfering with the Gαq/PLC/PKC pathway. A direct interaction between PKC-γ and STAT3 was critical for STAT3 activation. CAB was shown to activate pSTAT3 via HTR2B, reducing its therapeutic potential. However, the combination of an HTR2B antagonist with CAB significantly inhibited tumor cell proliferation in HTR2B-expressing pituitary tumor cell lines, a xenografted pituitary tumor model, and patient-derived samples. Analysis of patient-derived data indicated that a distinct molecular pattern characterized by upregulated HTR2B/PKC-γ and downregulated BTG2/GADD45A may benefit from combination treatment with CAB and PRX-08066. CONCLUSIONS: HTR2B is a potential therapeutic target for NFPAs, and its inhibition could improve CAB efficacy. A dual therapy approach may be beneficial for NFPA patients with high HTR2B expression.

Physical Adversarial Attack Meets Computer Vision: A Decade Survey.

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision, their vulnerability to adversarial attacks remains a critical concern. Extensive research has demonstrated that incorporating sophisticated perturbations into input images can lead to a catastrophic degradation in DNNs' performance. This perplexing phenomenon not only exists in the digital space but also in the physical world. Consequently, it becomes imperative to evaluate the security of DNNs-based systems to ensure their safe deployment in real-world scenarios, particularly in security-sensitive applications. To facilitate a profound understanding of this topic, this paper presents a comprehensive overview of physical adversarial attacks. Firstly, we distill four general steps for launching physical adversarial attacks. Building upon this foundation, we uncover the pervasive role of artifacts carrying adversarial perturbations in the physical world. These artifacts influence each step. To denote them, we introduce a new term: adversarial medium. Then, we take the first step to systematically evaluate the performance of physical adversarial attacks, taking the adversarial medium as a first attempt. Our proposed evaluation metric, hiPAA, comprises six perspectives: Effectiveness, Stealthiness, Robustness, Practicability, Aesthetics, and Economics. We also provide comparative results across task categories, together with insightful observations and suggestions for future research directions.

Enriched environment treatment promotes neurofunctional recovery by regulating the ALK5/Smad2/3/Gadd45ß signaling pathway in rats with cerebral ischemia /reperfusion injury.

It has been demonstrated that an enriched environment (EE) treatment can alter neuroplasticity in neurodegenerative diseases. However, the role of EE treatment in ischemic stroke remains unclear. Previous findings have revealed that EE treatment can promote cerebral activin-receptor-like-kinase-5 (ALK5) expression after cerebral ischemia/reperfusion (I/R) injury. ALK5 has been identified as a potential mediator of neuroplasticity through its modulation of Smad2/3 and Gadd45ß. Therefore, the aim of this study was to investigate whether EE treatment could promote neurofunctional recovery by regulating the ALK5/Smad2/3/Gadd45ß pathway. The study utilized the rat model of middle cerebral artery occlusion/reperfusion (MCAO/R). The ALK5/Smad2/3/Gadd45ß signaling pathway changes were evaluated using western blotting (WB). Brain injury was assessed by infarct volume and neurobehavioral scores. The effect of EE treatment on neurogenesis was evaluated using Doublecortin (DCX) and Nestin, axonal plasticity with biotinylated dextran amine (BDA) nerve tracing, and dendritic plasticity was assessed using Golgi-Cox staining. EE treatment has been demonstrated to modulate the Smad2/3/Gadd45ß pathway by regulating the expression of ALK5. The protective effects of EE treatment on brain infarct volume, neurological function, newborn neurons, dendritic and axonal plasticity following cerebral I/R injury were counteracted by ALK5 silencing. EE treatment can enhance neurofunctional recovery after cerebral I/R injury, which is achieved by regulating the ALK5/Smad2/3/Gadd45ß signaling pathway to promote neuroplasticity.

[Research Progress on Predictive Biomarkers of Immunotherapy Efficacy
in Non-small Cell Lung Cancer].

Lung cancer is one of the most common malignant tumors in the world, of which non-small cell lung cancer (NSCLC) is the majority. The emergence of immune checkpoint inhibitors (ICIs) has greatly changed the treatment strategy of NSCLC and improved the prognosis of patients. However, in reality, only a small number of patients can achieve long-term benefit. Therefore, the identification of reliable predictive biomarkers is essential for the selection of treatment modalities. With the development of molecular biology and genome sequencing technology in recent years, as well as the in-depth understanding of tumor and its host immune microenvironment, research on biomarkers has emerged in an endless stream. This review focuses on the predictive biomarkers of immunotherapy efficacy in NSCLC, in order to provide some guidance for precision immunotherapy.
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Non-insulin-based insulin resistance indices predict early neurological deterioration in elderly and middle-aged acute ischemic stroke patients in Northeast China.

Insulin resistance (IR) has a strong association with acute ischemic stroke (AIS) occurrence and poor prognosis of afflicted patients. However, the relation between early neurological deterioration (END) risk and IR in elderly and middle-aged patients remains to be thoroughly studied. Here, we investigated the relationship between four indicators of IR and the risk of END in middle-aged patients patients with AIS. The study retrospectively analyzed 1696 elderly and middle-aged patients having AIS between January 2019 and June 2023. Within 7 days of admission, the patients were then stratified relying upon alternations in the National Institutes of Health Stroke Scale. Subsequently, we employed logistic regression analyses for assessing each index correlation with END on the basis of the tertiles of TyG index (TyGI), triglyceride to high-density lipoprotein ratio (TG/HDL), TyG-BMI, alongside IR metabolic score (METS-IR). These four indicators were significantly heightened in the END group (n = 680) in comparison to the non-END group (n = 1016). When grouping using tertiles, the four aforementioned indicators emerged as independent risk factors for END occurrence, whether or not adjusted for confounding factors. The results revealed a progressive elevation in END occurrence risk with the rise in the tertile of each indicator. Finally, we utilized receiver operating characteristic (ROC) curves for assessing the indicators' predictive power. TyG-BMI, TyGI, TG/HDL, and METS-IRs' area under the curve (AUC) were, respectively, 0.736 (95% CI: 0.712-0.761; P < 0.001), 0. 694 (95% CI: 0.668-0.721; P < 0.001), 0.684 (95% CI: 0.658-0.711; P < 0.001), and 0.722 (95% CI: 0.697-0.747; P < 0.001). IR is associated with END risk in middle-aged AIS patients. TyG-BMI, TyGI, TG/HDL, and METS-IR are independent risk factors of END in elderly and middle-aged AIS patients. Simultaneously, these four IR indicators have significant predictive power for END.

Food Grinding Behavior: A Review of Causality and Influential Factors.

Food waste is a common issue arising from grinding of food by experimental animals, leading to excessive food scraps falling into cages. In the wild, animals grind food by gnawing vegetation and seeds, potentially damaging the ecological environment. However, limited ecology studies have focused on food grinding behavior since the last century, with even fewer on rodent food grinding, particularly recently. Although food grinding's function is partially understood, its biological purposes remain under-investigated and driving factors unclear. This review aims to explain potential causes of animal food grinding, identify influencing factors, and discuss contexts and limitations. Specifically, we emphasize recent progress on gut microbiota significance for food grinding. Moreover, we show abnormal food grinding is determined by degree of excess normal behavior, emphasizing food grinding is not meaningless. Findings from this review promote comprehensive research on the myriad factors, multifaceted roles, and intricate evolution underlying food grinding behavior, benefiting laboratory animal husbandry and ecological environment protection, and identifying potential physiological benefits yet undiscovered.

[Research on intelligent tooth segmentation method combining multiple seed region growth and boundary extension].

The segmentation of dental models is a crucial step in computer-aided diagnosis and treatment systems for oral healthcare. To address the issues of poor universality and under-segmentation in tooth segmentation techniques, an intelligent tooth segmentation method combining multiple seed region growth and boundary extension is proposed. This method utilized the distribution characteristics of negative curvature meshes in teeth to obtain new seed points and effectively adapted to the structural differences between the top and sides of teeth through differential region growth. Additionally, the boundaries of the initial segmentation were extended based on geometric features, which was effectively compensated for under-segmentation issues in region growth. Ablation experiments and comparative experiments with current state-of-the-art algorithms demonstrated that the proposed method achieved better segmentation of crowded dental models and exhibited strong algorithm universality, thus possessing the capability to meet the practical segmentation needs in oral healthcare.

Genome-Wide Identification and Characterization of the RWP-RK Proteins in Zanthoxylum armatum.

Apomixis is a common reproductive characteristic of Zanthoxylum plants, and RWP-RKs are plant-specific transcription factors known to regulate embryonic development. However, the genome-wide analysis and function prediction of RWP-RK family genes in Z. armatum are unclear. In this study, 36 ZaRWP-RK transcription factors were identified in the genome of Z. armatum, among which 15 genes belonged to the RKD subfamily and 21 belonged to the NLP subfamily. Duplication events of ZaRWP-RK genes were mainly segmental duplication, and synteny analysis revealed a close phylogenetic relationship between Z. armatum and Arabidopsis. The analysis of cis-elements indicated that ZaRWP-RK genes may be involved in the regulation of the embryonic development of Z. armatum by responding to plant hormones such as abscisic acid, auxin, and gibberellin. Results of a real-time PCR showed that the expression levels of most ZaRWP-RK genes were significantly increased from flowers to young fruits. Protein-protein interaction network analysis further revealed the potential roles of the ZaRWP-RK proteins in apomixis. Collectively, this study is expected to improve our understanding of ZaRWP-RK transcription factors and provide a theoretical basis for future investigations into the ZaRWP-RK genes and their regulatory mechanisms in the apomixis process of Z. armatum.

Norboldine improves cognitive impairment and pathological features in Alzheimer's disease by activating AMPK/GSK3ß/Nrf2 signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Lindera aggregata (Sims) Kosterm is a common traditional herb that has multiple bioactivities. Radix Linderae (LR), the dry roots of Lindera aggregata (Sims) Kosterm, is a traditional Chinese herbal medicine with antioxidant, anti-inflammatory and immunomodulatory properties, first found in Kaibao Era. Norboldine (Nor) is an alkaloid extracted from LR and is one of the primary active ingredients of LR. However, the pharmacological functions and mechanism of Nor in Alzheimer's disease (AD) are still unknown. AIM OF THE STUDY: This study aims to investigate the effect and mechanism of Nor therapy in improving the cognitive impairment and pathological features of 3 × Tg mice. MATERIALS AND METHODS: 3 × Tg mice were treated with two concentrations of Nor for one month and then the memory and cognitive abilities of mice were assessed by novel object recognition experiment and Morris water maze. The impact of Nor on the pathology of ADwere examined in PC12 cells and animal tissues using western blotting and immunofluorescence. Finally, western blotting was used to verify the anti-apoptotic effect of Nor by activating AMPK/GSK3ß/Nrf2 signaling pathway at animal and cellular levels. RESULTS: In this study, we showed that Nor treatment improved the capacity of the learning and memory of 3 × Tg mice and alleviated AD pathology such as Aß deposition. In addition, Nor restored the abnormalities of mitochondrial membrane potential, significantly reduced the production of intracellular ROS and neuronal cell apoptosis. Mechanistically, we combined network pharmacology and experimental verification to show that Nor may exert antioxidant stress and anti-apoptotic through the AMPK/GSK3ß/Nrf2 signaling pathway. CONCLUSION: Our data provide some evidence that Nor exerts a neuroprotective effect through the AMPK/GSK3ß/Nrf2 pathway, thereby improving cognitive impairment in AD model mice. Natural products derived from traditional Chinese medicines are becoming increasingly popular in the process of new drug development and discovery, and our findings provide new perspectives for the discovery of improved treatment strategies for AD.

One-pot formal [3 + 2] annulation of 2-pyridinyl-substituted p-quinone methides and arynes for the synthesis of 3-aryl pyrido[1,2-a]indoles.

An efficient and metal-free method for the synthesis of 3-aryl pyrido[1,2-a]indoles from aryne intermediates and 2-pyridinyl-substituted p-QMs was successfully developed under ambient conditions. The reaction offered a novel and practical protocol to access some diverse functional molecules in good to excellent yields. The proposed mechanism indicated that the reaction proceeded via a formal [3 + 2] cycloaddition step.

Multi-Domain Based Dynamic Graph Representation Learning for EEG Emotion Recognition.

Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured data, making them a promising method for electroencephalogram (EEG) emotion recognition. However, due to dynamic functional connectivity and nonlinear relationships between brain regions, representing EEG as graph data remains a great challenge. To solve this problem, we proposed a multi-domain based graph representation learning (MD 2 GRL) framework to model EEG signals as graph data. Specifically, MD 2 GRL leverages gated recurrent units (GRU) and power spectral density (PSD) to construct node features of two subgraphs. Subsequently, the self-attention mechanism is adopted to learn the similarity matrix between nodes and fuse it with the intrinsic spatial matrix of EEG to compute the corresponding adjacency matrix. In addition, we introduced a learnable soft thresholding operator to sparsify the adjacency matrix to reduce noise in the graph structure. In the downstream task, we designed a dual-branch GNN and incorporated spatial asymmetry for graph coarsening. We conducted experiments using the publicly available datasets SEED and DEAP, separately for subject-dependent and subject-independent, to evaluate the performance of our model in emotion classification. Experimental results demonstrated that our method achieved state-of-the-art (SOTA) classification performance in both subject-dependent and subject-independent experiments. Furthermore, the visualization analysis of the learned graph structure reveals EEG channel connections that are significantly related to emotion and suppress irrelevant noise. These findings are consistent with established neuroscience research and demonstrate the potential of our approach in comprehending the neural underpinnings of emotion.

Chromosome-level assembly of the synthetic hexaploid wheat-derived cultivar Chuanmai 104.

Synthetic hexaploid wheats (SHWs) are effective genetic resources for transferring agronomically important genes from wild relatives to common wheat (Triticum aestivum L.). Dozens of reference-quality pseudomolecule assemblies of hexaploid wheat have been generated, but none is reported for SHW-derived cultivars. Here, we generated a chromosome-scale assembly for the SHW-derived cultivar 'Chuanmai 104' based on PacBio HiFi reads and chromosome conformation capture sequencing. The total assembly size was 14.81 Gb with a contig N50 length of 58.25 Mb. A BUSCO analysis yielded a completeness score of 99.30%. In total, repetitive elements comprised 81.36% of the genome and 122,554 high-confidence protein-coding gene models were predicted. In summary, the first chromosome-level assembly for a SHW-derived cultivar presents a promising outlook for the study and utilization of SHWs in wheat improvement, which is essential to meet the global food demand.

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods.

OBJECTIVE: The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors. METHOD: Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation. RESULTS: Indoor particles were measured in a range of 0.8-370.7 µg/m3 and 1.9-465.2 µg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0-211.0 µg/m3 and 0.0-493.0 µg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 µg/m3 and 4.56 µg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 µg/m3 and 17.54 µg/m3, respectively), respectively. CONCLUSIONS: Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance.

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory.

The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.

Engineering of a TetR family transcriptional regulator BkdR enhances heterologous spinosad production in Streptomyces albus B4 chassis.

Precursor supply plays a significant role in the production of secondary metabolites. In Streptomyces bacteria, propionyl-, malonyl-, and methylmalonyl-CoA are the most common precursors used for polyketide biosynthesis. Although propionyl-CoA synthetases participate in the propionate assimilation pathway and directly convert propionate into propionyl-CoA, malonyl- and methylmalonyl-CoA cannot be formed using common acyl-CoA synthetases. Therefore, both acetyl- and propionyl-CoA carboxylation, catalyzed by acyl-CoA carboxylases, should be considered when engineering a microorganism chassis to increase polyketide production. In this study, we identified a transcriptional regulator of the TetR family, BkdR, in Streptomyces albus B4, which binds directly to the promoter region of the neighboring pccAB operon. This operon encodes acetyl/propionyl-CoA carboxylase and negatively regulates its transcription. In addition to acetate and propionate, the binding of BkdR to pccAB is disrupted by acetyl- and propionyl-CoA ligands. We identified a 16-nucleotide palindromic BkdR-binding motif (GTTAg/CGGTCg/TTAAC) in the intergenic region between pccAB and bkdR. When bkdR was deleted, we found an enhanced supply of malonyl- and methylmalonyl-CoA precursors in S. albus B4. In this study, spinosad production was detected in the recombinant strain after introducing the entire artificial biosynthesized gene cluster into S. albus B4. When supplemented with propionate to provide propionyl-CoA, the novel bkdR-deleted strain produced 29.4% more spinosad than the initial strain in trypticase soy broth (TSB) medium. IMPORTANCE: In this study, we describe a pccAB operon involved in short-chain acyl-CoA carboxylation in S. albus B4 chassis. The TetR family regulator, BkdR, represses this operon. Our results show that BkdR regulates the precursor supply needed for heterologous spinosad biosynthesis by controlling acetyl- and propionyl-CoA assimilation. The deletion of the BkdR-encoding gene exerts an increase in heterologous spinosad yield. Our research reveals a regulatory mechanism in short-chain acyl-CoA metabolism and suggests new possibilities for S. albus chassis engineering to enhance heterologous polyketide yield.

Chenodeoxycholic Acid-Modified Polyethyleneimine Nano-Composites Deliver Low-Density Lipoprotein Receptor Genes for Lipid-Lowering Therapy by Targeting the Liver.

Lipid-lowering drugs, especially statins, are extensively utilized in clinical settings for the prevention of hyperlipidemia. Nevertheless, prolonged usage of current lipid-lowering medications is associated with significant adverse reactions. Therefore, it is imperative to develop novel therapeutic agents for lipid-lowering therapy. In this study, a chenodeoxycholic acid and lactobionic acid double-modified polyethyleneimine (PDL) nanocomposite as a gene delivery vehicle for lipid-lowering therapy by targeting the liver, are synthesized. Results from the in vitro experiments demonstrate that PDL exhibits superior transfection efficiency compared to polyethyleneimine in alpha mouse liver 12 (AML12) cells and effectively carries plasmids. Moreover, PDL can be internalized by AML12 cells and rapidly escape lysosomal entrapment. Intravenous administration of cyanine5.5 (Cy5.5)-conjugated PDL nanocomposites reveals their preferential accumulation in the liver compared to polyethyleneimine counterparts. Systemic delivery of low-density lipoprotein receptor plasmid-loaded PDL nanocomposites into mice leads to reduced levels of low-density lipoprotein cholesterol (LDL-C) and triglycerides (TC) in the bloodstream without any observed adverse effects on mouse health or well-being. Collectively, these findings suggest that low-density lipoprotein receptor plasmid-loaded PDL nanocomposites hold promise as potential therapeutics for lipid-lowering therapy.