stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics.

Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST. stDiff employs a conditional diffusion model, capturing gene expression abundance relationships in scRNA-seq data through two Markov processes: one introducing noise to transcriptomics data and the other denoising to recover them. The missing portion of ST is predicted by incorporating the original ST data into the denoising process. In our comprehensive performance evaluation across 16 datasets, utilizing multiple clustering and similarity metrics, stDiff stands out for its exceptional ability to preserve topological structures among cells, positioning itself as a robust solution for cell population identification. Moreover, stDiff's enhancement outcomes closely mirror the actual ST data within the batch space. Across diverse spatial expression patterns, our model accurately reconstructs them, delineating distinct spatial boundaries. This highlights stDiff's capability to unify the observed and predicted segments of ST data for subsequent analysis. We anticipate that stDiff, with its innovative approach, will contribute to advancing ST imputation methodologies.

Spatial enrichment and genomic analyses reveal the link of NOMO1 with amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease with uncertain genetic predisposition in most sporadic cases. Spatial architecture of cell types and gene expression is the basis of cell-cell interactions, biological function and disease pathology, but is not well investigated in human motor cortex, a key ALS relevant brain region. Recent studies indicated single nucleus transcriptomic features of motor neuron vulnerability in ALS motor cortex. However, it remains largely unclear what is the brain regional vulnerability of ALS-associated genes, and what is the genetic link between region-specific genes and ALS risk. Here, we developed an entropy-weighted differential gene expression matrix-based tool (SpatialE) to identify the spatial enrichment of gene sets in spatial transcriptomics (ST). We benchmarked SpatialE against another enrichment tool (Multimodal Intersection Analysis, MIA) using ST data from both human and mouse brain tissues. To investigate regional vulnerability, we analyzed three human motor cortex and two dorsolateral prefrontal cortex tissues for spatial enrichment of ALS-associated genes. We also used Cell2location to estimate the abundance of cell types in ALS-related cortex layers. To dissect the link of regionally expressed genes and ALS risk, we performed burden analyses of rare loss-of-function (LOF) variants detected by whole-genome sequencing in ALS patients and controls, and then analyzed differential gene expression in the TargetALS RNA-seq dataset. SpatialE showed more accurate and specific spatial enrichment of regional cell type markers than MIA in both mouse brain and human dorsolateral prefrontal cortex. Spatial transcriptomic analyses of human motor cortex showed heterogenous cell types and spatial gene expression profiles. We found that 260 manually curated ALS-associated genes are significantly enriched in layer 5 (L5) motor cortex, with abundant expression of upper motor neurons and L5 excitatory neurons. Burden analyses of rare LOF variants in L5-associated genes nominated NOMO1 as a novel ALS-associated gene in a combined sample set of 6,814 ALS patients and 3,324 controls (P = 0.029). Gene expression analyses in central nervous system tissues revealed down-regulation of NOMO1 in ALS, which is consistent with a LOF disease mechanism. In conclusion, our integrated ST and genomic analyses identified regional brain vulnerability in ALS and the association of a L5 gene (NOMO1) with ALS risk.

Strain Engineering of Ion-Coordinated Nanochannels in Nanocellulose.

Expanding the interlayer spacing plays a significant role in improving the conductivity of a cellulose-based conductor. However, it remains a challenge to regulate the cellulose nanochannel expanded by ion coordination. Herein, starting from multiscale mechanics, we proposed a strain engineering method to regulate the interlayer spacing of the cellulose nanochannels. First-principles calculations were conducted to select the most suitable ions for coordination. Large-scale molecular dynamics simulations were performed to reveal the mechanism of interlayer spacing expansion by the ion cross-linking. Combining the shear-lag model, we established the relationship between interfacial cross-link density and interlayer spacing of an ion-coordinated cellulose nanochannel. Consequently, fast ion transport and current regulation were realized via the strain engineering of nanochannels, which provides a promising strategy for the current regulation of a cellulose-based conductor.

Anisotropic Fracture of Two-Dimensional Ta2NiSe5.

Anisotropic two-dimensional materials present a diverse range of physical characteristics, making them well-suited for applications in photonics and optoelectronics. While mechanical properties play a crucial role in determining the reliability and efficacy of 2D material-based devices, the fracture behavior of anisotropic 2D crystals remains relatively unexplored. Toward this end, we herein present the first measurement of the anisotropic fracture toughness of 2D Ta2NiSe5 by microelectromechanical system-based tensile tests. Our findings reveal a significant in-plane anisotropic ratio (∼3.0), accounting for crystal orientation-dependent crack paths. As the thickness increases, we observe an intriguing intraplanar-to-interplanar transition of fracture along the a-axis, manifesting as stepwise crack features attributed to interlayer slippage. In contrast, ruptures along the c-axis surprisingly exhibit persistent straightness and smoothness regardless of thickness, owing to the robust interlayer shear resistance. Our work affords a promising avenue for the construction of future electronics based on nanoribbons with atomically sharp edges.

A Drug Molecule-Modified Graphene Field-Effect Transistor Nanosensor for Rapid, Label-Free, and Ultrasensitive Detection of Estrogen Receptor α Protein.

Estrogen receptor α (ERα) is an important biomarker in breast cancer diagnosis and treatment. Sensitive and accurate detection of ERα protein expression is crucial in guiding selection of an appropriate therapeutic strategy to improve the effectiveness and prognosis of breast cancer treatment. Herein, we report a liquid-gated graphene field-effect transistor (FET) biosensor that enables rapid, sensitive, and label-free detection of the ERα protein by employing a novel drug molecule as a capture probe. The drug molecule was synthesized and subsequently immobilized onto the sensing surface of the fabricated graphene FET, which was able to distinguish the ERα-positive from the ERα-negative protein. The developed sensor not only demonstrated a low detection limit (LOD: 2.62 fM) but also achieved a fast response to ERα protein samples within 30 min. Moreover, depending on the relationship between the change of dirac point and the ERα protein concentrations, the dissociation constant (Kd) was estimated to be 7.35 ± 0.06 pM, indicating that the drug probe-modified graphene FET had a good affinity with ERα protein. The nanosensor was able to analyze ERα proteins from 36 cell samples lysates. These results show that the graphene FET sensor was able to differentiate between ERα-positive and ERα-negative cells, indicating a promising biosensor for the ultrasensitive and rapid detection of ERα protein without antibody labeling.

Advances in Atomically Dispersed Metal and Nitrogen Co-Doped Carbon Catalysts for Advanced Oxidation Technologies and Water Remediation: From Microenvironment Modulation to Non-Radical Mechanisms.

Atomically dispersed metal and nitrogen co-doped carbon catalysts (M-N-C) have been attracting tremendous attentions thanks to their unique MNx active sites and fantastic catalytic activities in advanced oxidation technologies (AOTs) for water remediation. However, precisely tailoring the microenvironment of active sites at atomic level is still an intricate challenge so far, and understanding of the non-radical mechanisms in persulfate activation exists many uncertainties. In this review, latest developments on the microenvironment modulation strategies of atomically dispersed M-N-C catalysts including regulation of central metal atoms, regulation of coordination numbers, regulation of coordination heteroatoms, and synergy between single-atom catalysts (SACs) with metal species are systematically highlighted and discussed. Afterwards, progress and underlying limitations about the typical non-radical pathways from production of singlet oxygen, electron transfer mechanism to generation of high-valent metal species are well demonstrated to inspire intrinsic insights about the mechanisms of M-N-C/persulfate systems. Lastly, perspectives for the remaining challenges and opportunities about the further development of carbon-based SACs in environment remediation are also pointed out. It is believed that this review will be much valuable for the further design of active sites in M-N-C/persulfate catalytic systems and promote the wide application of SACs in various fields.

Ultra-high Q-factor quasi-BIC BaTiO3 metasurface for electro-optic modulation.

Metasurfaces play a crucial role in trapping electromagnetic waves with specific wavelengths, serving as a significant platform for enhancing light-matter interactions. In all kinds of dynamic modulation metasurfaces, electro-optic modulation metasurfaces have attracted much attention due to its advantages of fast, stable and high efficiency. In order to respond to the extremely weak refractive index change of the electro-optical effect of the materials, the metasurfaces are required to support optical signals with high Q values. The quasi-bound state in the continuum (Q-BIC) is often used to enhance the light-field modulation capability of metasurfaces and to improve the modulation sensitivity of electro-optic modulators due to its ability to generate high Q-factor resonances. However, the design of an electro-optic modulation metasurface that facilitates the application of voltage and achieves modulation efficiency of nearly 100% is still in urgent need of development. In this study, single-crystal BTO metasurfaces are modeled using finite-difference time-domain method, and the structural symmetry is broken to introduce a Q-BIC resonance to generate a high Q-factor optical signal of 2.45 × 104 for high-depth electro-optic modulation. By simulating an applied electric field of 143 V/mm on the metasurface, a slight refractive index change of BTO of 8 × 10-4 was produced, leading to an electro-optical intensity modulation depth of 100%. Furthermore, the nanostructure of the metasurface was carefully designed to facilitate nano-fabrication and voltage application, and it is ideal for the development of low-power, CMOS-compatible, and miniaturized electro-optic modulation devices. Although the results of this study are based on simulations, they provide a crucial theoretical basis and guidance for the realization of efficient and realistic design of dynamic metasurfaces.

SLC25A17 inhibits autophagy to promote triple-negative breast cancer tumorigenesis by ROS-mediated JAK2/STAT3 signaling pathway.

BACKGROUND: SLC25A17, a peroxisomal solute carrier, has been implicated in various physiological and pathological processes. However, its precise roles and underlying mechanisms in triple-negative breast cancer (TNBC) remain incompletely understood. METHODS: The expression and survival data of breast cancer were derived from TCGA and GEO databases. A variety of in vitro assays were conducted, including proliferation, apoptosis, cell cycle, migration, and invasion. Reactive oxygen species (ROS) were measured by immunofluorescence microscopy and flow cytometry. The levels of autophagy were assessed by mRFP-GFP-LC3 confocal microscopy scanning, western blotting, and electron microscopy. RESULTS: SLC25A17 was highly expressed in breast cancer tissues, which was found to be associated with unfavorable prognosis. Functional assays demonstrated that SLC25A17 knockdown suppressed proliferation, epithelial-mesenchymal transition (EMT), migration, and invasion. Moreover, it prompted apoptosis and autophagy. On the other hand, SLC25A17 knockdown promoted autophagy through triggering ROS accumulation, which was counteracted by N-acetyl-l-cysteine (NAC). Furthermore, the pro-apoptotic effect of SLC25A17 knockdown was reversed when treated with autophagy inhibitor 3-MA in TNBC cells, suggesting that SLC25A17 knockdown-induced autophagic cell death. Mechanistically, SLC25A17 performed its function through regulation JAK2/STAT3 signaling in TNBC. In a nude mice xenograft study, SLC25A17 knockdown markedly decreased breast tumor growth and metastasis. CONCLUSION: SLC25A17 up-regulation may be a critical factor driving TNBC progression by modulating ROS production and autophagy. Consequently, targeting SLC25A17 could be an effective therapeutic strategy against TNBC.

Self-assembly mechanism, physicochemical analyses and application performance investigations of branched alkyl glycosides with alcohol ether carboxylic acids of varied epoxide numbers.

Green surfactants, specifically alkyl glycosides and fatty alcohol ether carboxylic acids, are known for their biocompatibility, multiresponsiveness, and versatile applications, garnering significant attention in the realms of green and colloid chemistry. This study systematically investigated the mechanism underlying micelle formation within aqueous solutions comprising alcohol ether carboxylic acids featuring diverse EO group chain quantities (AEC-nH, where n equals 5, 7, and 9) and branched alkyl glycosides (IG). The elucidation of these mechanisms sheds light on their prospective application properties. It was observed that the self-assembly of micelles in these hybrid systems is predominantly influenced by hydrogen bonding, electrostatic interactions, and hydrophobic forces. The spherical-rod morphology of the micelles responds to the varying numbers of EO group chains, with an increased number of EO leading to the formation of rod-like micelles, which exhibit relative instability, while a decreased number of EO results in the formation of spherical micelles with relative stability. Additionally, by means of kinetic analysis, it was determined that the micelle formation process of the three hybrid systems is driven by enthalpy, and a mixed diffusion-kinetics adsorption mechanism is involved in the adsorption process. These findings significantly impact their application properties. This report stands as the first exploration of the synergistic mechanisms and application performance of two types of green surfactants in aqueous solutions, considering the influence of different numbers of EO group chains. Not only does it provide fundamental insights into their properties, but it also offers novel perspectives on the applications of green surface activation.

NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions.

PURPOSE: Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-level spatial relationships, leading to ambiguous associations between target voxels and their neighbors, which, in turn, adversely impacts FOD accuracy. This study aims to address this issue by introducing a novel neural network, the neighboring voxel attention mechanism network (NVAM-Net), designed to reconstruct high-quality FOD images. METHODS: The NVAM-Net leverages a Transformer architecture and incorporates two innovative attention mechanisms: voxel attention and surface attention. These mechanisms are specifically designed to capture overlooked features among neighboring voxels. The processed features are subsequently passed through two fully connected layers, further enhancing FOD estimation accuracy by separately estimating spherical harmonics (SH) coefficients of varying orders. RESULTS: The experimental findings, based on the Human Connectome Project (HCP) dataset, reveal that the reconstructed super-resolution FOD images achieve results comparable to those obtained through more advanced dMRI acquisition protocols. These results underscore the NVAM-Net's robust performance in reconstructing multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD). CONCLUSION: In summary, this research underscores the NVAM-Net's advantages and practical feasibility in reconstructing high-quality FOD images. It provides a reliable reference point for clinical applications in the field of diffusion magnetic resonance imaging.

Bridge effect on charge transfer and energy transfer in fullerene-chromophore dyads.

Fullerene-chromophore dyads have attracted a great deal of research interest because these complexes can be potentially designed as nanoscale artificial photosynthetic centers, in which the chromophore and fullerene function as the electron donor and acceptor, respectively. The basic operation of this dyad-type artificial reaction center is photoinduced electron transfer from the donor to the acceptor. The fullerene and chromophore are usually covalently linked so that sufficient electronic coupling between these two moieties can facilitate the electron transfer. However, other deactivation pathways for the chromophore excited state, such as energy transfer to the fullerene, may reduce the quantum yield of the photoinduced electron transfer. Here, a series of C60-perylene dyads is exploited to interrogate the effect of the linkage on deactivation mechanisms of the chromophore excited state. For the C60-perylene dyads with a single or double bond bridge, we find that the decay of the singlet state of the chromophore is dominated by the electron transfer, and the corresponding time constant is determined to be 45 ps. On the other hand, for the dyad with a triple bond bridge, the singlet state of the chromophore is quickly quenched through energy transfer to fullerene, and the time constant is as short as 7.9 ps. Our finding suggests that the bond order of the bridge in the fullerene-chromophore dyads can be utilized to control the deactivation pathways of the excited state.

A portable transistor immunosensor for fast identification of porcine epidemic diarrhea virus.

Widespread distribution of porcine epidemic diarrhea virus (PEDV) has led to catastrophic losses to the global pig farming industry. As a result, there is an urgent need for rapid, sensitive and accurate tests for PEDV to enable timely and effective interventions. In the present study, we develop and validate a floating gate carbon nanotubes field-effect transistor (FG CNT-FET)-based portable immunosensor for rapid identification of PEDV in a sensitive and accurate manner. To improve the affinity, a unique PEDV spike protein-specific monoclonal antibody is prepared by purification, and subsequently modified on FG CNT-FET sensor to recognize PEDV. The developed FET biosensor enables highly sensitive detection (LoD: 8.1 fg/mL and 100.14 TCID50/mL for recombinant spike proteins and PEDV, respectively), as well as satisfactory specificity. Notably, an integrated portable platform consisting of a pluggable FG CNT-FET chip and a portable device can discriminate PEDV positive from negative samples and even identify PEDV and porcine deltacoronavirus within 1 min with 100% accuracy. The portable sensing platform offers the capability to quickly, sensitively and accurately identify PEDV, which further points to a possibility of point of care (POC) applications of large-scale surveillance in pig breeding facilities.

The establishment of diseased human whole organ model by normothermic machine perfusion technique: Principle of concept.

BACKGROUND: The global incidence of liver diseases is rising, yet there remains a dearth of precise research models to mimic these diseases. The use of normothermic machine perfusion (NMP) to study diseased livers recovered from liver transplantation (LT) recipients presents a promising avenue. Accordingly, we have developed a machine perfusion system tailored specifically for the human whole diseased livers and present our experience from the NMP of diseased livers. METHODS: Six diseased livers recovered from LT recipients with different diagnoses were collected. The diseased livers were connected to the machine perfusion system that circulated tailored perfusate, providing oxygen and nutrients. The pressure and flow of the system were recorded, and blood gas analysis and laboratory tests of perfusate and bile were examined to analyze the function of the diseased livers. Liver tissues before and after NMP were collected for histological analysis. RESULTS: Experiments showed that the system maintained the diseased livers in a physiological state, ensuring stable hemodynamics and a suitable partial pressure of oxygen and carbon dioxide. The results of blood gas analysis and laboratory tests demonstrated a restoration and sustenance of metabolism with minimal damage. Notably, a majority of the diseased livers exhibited bile production continuously, signifying their vivid functional integrity. The pathological characteristics remained stable before and after NMP. CONCLUSION: We successfully established the machine perfusion system tailored specifically for diseased human whole livers. Through the application of this system, we have developed a novel in vitro model that faithfully recapitulates the main features of human liver disease. This model holds immense promise as an advanced disease modeling platform, offering profound insights into liver diseases and potential implications for research and therapeutic development.

Maternal inulin supplementation ameliorates prenatal methamphetamine exposure-induced hepatotoxicity and restores gut microbiota in mouse offspring.

Prenatal exposure to methamphetamine (METH) is an issue of global concern due to its adverse effects on offspring, particularly its impact on liver health, an area still not fully understood. Inulin, a recognized prebiotic, is thought to potentially ameliorate these developmental disorders and toxic injuries in progeny. To investigate the effects of prenatal METH exposure on the liver and the role of gut microbiota, we established a murine model, the subjects of which were exposed to METH prenatally and subsequently treated with inulin. Our findings indicate that prenatal METH exposure causes liver damage in offspring, as evidenced by a decreased liver index, histopathological changes, diminished glycogen synthesis, hepatic dysfunction, and alterations in mRNA profiles. Furthermore, it impairs the antioxidant system and induces oxidative stress, possibly due to changes in cecal microbiota and dysregulation of bile acid homeostasis. However, maternal inulin supplementation appears to restore the gut microbiota in offspring and mitigate the hepatotoxic effects induced by prenatal METH exposure. Our study provides definitive evidence of METH's transgenerational hepatotoxicity and suggests that maternal inulin supplementation could be an effective preventive strategy.

The gut microbiota contributes to methamphetamine-induced reproductive toxicity in male mice.

Methamphetamine (METH) is a psychostimulant drug belonging to the amphetamine-type stimulant class, known to exert male reproductive toxicity. Recent studies suggest that METH can disrupt the gut microbiota. Furthermore, the gut-testis axis concept has gained attention due to the potential link between gut microbiome dysfunction and reproductive health. Nonetheless, the role of the gut microbiota in mediating the impact of METH on male reproductive toxicity remains unclear. In this study, we employed a mouse model exposed to escalating doses of METH to assess sperm quality, testicular pathology, and reproductive hormone levels. The fecal microbiota transplantation method was employed to investigate the effect of gut microbiota on male reproductive toxicity. Transcriptomic, metabolomic, and microbiological analyses were conducted to explore the damage mechanism to the male reproductive system caused by METH. We found that METH exposure led to hormonal disorders, decreased sperm quality, and changes in the gut microbiota and testicular metabolome in mice. Testicular RNA sequencing revealed enrichment of several Gene Ontology terms associated with reproductive processes, as well as PI3K-Akt signaling pathways. FMT conveyed similar reproductive damage from METH-treated mice to healthy recipient mice. The aforementioned findings suggest that the gut microbiota plays a substantial role in facilitating the reproductive toxicity caused by METH, thereby highlighting a prospective avenue for therapeutic intervention in the context of METH-induced infertility.

A tailored ratiometric fluorescent sensor based on CdTe and MgF2 quantum dots for trace N-ethylpentylone detection.

The development of low-cost and highly sensitive ratiometric fluorescence sensor, CdTe@MIPs/MgF2, for N-Ethylpentylone (NEP) detection in wastewater samples is described. In this system, CdTe@MIPs (λex = 370, λem = 570) are employed as the receptor and response unit for NEP, with MgF2 (λex = 370, λem = 470) as the reference signal to enhance stability. Under optimal conditions, the sensor shows fluorescent quenching response at 570 nm to NEP in linear range of 2-200 nM, with LOD of 0.6 nM. The sensor also demonstrates significant selectivity for NEP over other analogues and interferents, making it ideal for practical applications in wastewater analysis. This approach is potentially more cost-effective and sensitive than conventional mass spectrometry in detecting abused substances in sewage. Additionally, the MgF2 fluorescent nano-material was first-ever developed and investigated, which may be significant in future research.

Metagenomic Next-Generation Sequencing Contributes to the Early Diagnosis of Mixed Infections in Central Nervous System.

Central nervous system (CNS) infections represent a challenge due to the complexities associated with their diagnosis and treatment, resulting in a high incidence rate and mortality. Here, we presented a case of CNS mixed infection involving Candida and human cytomegalovirus (HCMV), successfully diagnosed through macrogenomic next-generation sequencing (mNGS) in China. A comprehensive review and discussion of previously reported cases were also provided. Our study emphasizes the critical role of early pathogen identification facilitated by mNGS, underscoring its significance. Notably, the integration of mNGS with traditional methods significantly enhances the diagnostic accuracy of CNS infections. This integrated approach has the potential to provide valuable insights for clinical practice, facilitating early diagnosis, allowing for treatment adjustments, and ultimately, improving the prognosis for patients with CNS infections.

Infrared and Visible Image Fusion Algorithm Based on Double-Domain Transform Filter and Contrast Transform Feature Extraction.

Current challenges in visible and infrared image fusion include color information distortion, texture detail loss, and target edge blur. To address these issues, a fusion algorithm based on double-domain transform filter and nonlinear contrast transform feature extraction (DDCTFuse) is proposed. First, for the problem of incomplete detail extraction that exists in the traditional transform domain image decomposition, an adaptive high-pass filter is proposed to decompose images into high-frequency and low-frequency portions. Second, in order to address the issue of fuzzy fusion target caused by contrast loss during the fusion process, a novel feature extraction algorithm is devised based on a novel nonlinear transform function. Finally, the fusion results are optimized and color-corrected by our proposed spatial-domain logical filter, in order to solve the color loss and edge blur generated in the fusion process. To validate the benefits of the proposed algorithm, nine classical algorithms are compared on the LLVIP, MSRS, INO, and Roadscene datasets. The results of these experiments indicate that the proposed fusion algorithm exhibits distinct targets, provides comprehensive scene information, and offers significant image contrast.

Discovery of N-(phenylsulfonyl)thiazole-2-carboxamides as potent α-glucosidase inhibitors.

In a search for novel nonsugar α-glucosidase inhibitors for diabetes treatment, a series of N-(phenylsulfonyl)thiazole-2-carboxamide derivatives were designed and synthesized, the α-glucosidase inhibitory activities were then evaluated. Several compounds with promising α-glucosidase inhibitory effects were identified. Among these, compound W24 which shows low cytotoxicity and good α-glucosidase inhibitory activity with an IC50 value of 53.0 ± 7.7 µM, is more competitive compared with the commercially available drug acarbose (IC50 = 228.3 ± 9.2 µM). W24 was identified as a promising candidate in the development of α-glucosidase inhibitors. Molecular docking studies and molecular dynamics simulation were also performed to reveal the binding pattern of the active compound to α-glucosidase, and the binding free energy of the best compound W24 was 36.3403 ± 3.91 kcal/mol.

Multidimensional Criteria for Virtual Screening of PqsR Inhibitors Based on Pharmacophore, Docking, and Molecular Dynamics.

Pseudomonas aeruginosa is a clinically challenging pathogen due to its high resistance to antibiotics. Quorum sensing inhibitors (QSIs) have been proposed as a promising strategy to overcome this resistance by interfering with the bacterial communication system. Among the potential targets of QSIs, PqsR is a key regulator of quorum sensing in Pseudomonas aeruginosa. However, the current research on PqsR inhibitors is limited by the lack of diversity in the chemical structures and the screening methods. Therefore, this study aims to develop a multidimensional screening model for PqsR inhibitors based on both ligand- and receptor-based approaches. First, a pharmacophore model was constructed from a training set of PqsR inhibitors to identify the essential features and spatial arrangement for the activity. Then, molecular docking and dynamics simulations were performed to explore the core interactions between PqsR inhibitors and their receptor. The results indicate that an effective PqsR inhibitor should possess two aromatic rings, one hydrogen bond acceptor, and two hydrophobic groups and should form strong interactions with the following four amino acid residues: TYR_258, ILE_236, LEU_208, and GLN_194. Moreover, the docking score and the binding free energy should be lower than -8 kcal/mol and -40 kcal/mol, respectively. Finally, the validity of the multidimensional screening model was confirmed by a test set of PqsR inhibitors, which showed a higher accuracy than the existing screening methods based on single characteristics. This multidimensional screening model would be a useful tool for the discovery and optimization of PqsR inhibitors in the future.