Gene copy-number alterations: a cost-benefit analysis.

Changes in DNA copy number, whether confined to specific genes or affecting whole chromosomes, have been identified as causes of diseases and developmental abnormalities and as sources of adaptive potential. Here, we discuss the costs and benefits of DNA copy-number alterations. Changes in DNA copy number are largely detrimental. Amplifications or deletions of specific genes can elicit discrete defects. Large-scale changes in DNA copy number can also cause detrimental phenotypes that are due to the cumulative effects of copy-number alterations of many genes simultaneously. On the other hand, studies in microorganisms show that DNA copy-number alterations can be beneficial, increasing survival under selective pressure. As DNA copy-number alterations underlie many human diseases, we will end with a discussion of gene copy-number changes as therapeutic targets.

AptaDB: a comprehensive database integrating aptamer-target interactions.

Aptamers have emerged as research hotspots of the next generation due to excellent performance benefits and application potentials in pharmacology, medicine, and analytical chemistry. Despite the numerous aptamer investigations, the lack of comprehensive data integration has hindered the development of computational methods for aptamers and the reuse of aptamers. A public access database named AptaDB, derived from experimentally validated data manually collected from the literature, was hence developed, integrating comprehensive aptamer-related data, which include six key components: (i) experimentally validated aptamer-target interaction information, (ii) aptamer property information, (iii) structure information of aptamer, (iv) target information, (v) experimental activity information, and (vi) algorithmically calculated similar aptamers. AptaDB currently contains 1350 experimentally validated aptamer-target interactions, 1230 binding affinity constants, 1293 aptamer sequences, and more. Compared to other aptamer databases, it contains twice the number of entries found in available databases. The collection and integration of the above information categories is unique among available aptamer databases and provides a user-friendly interface. AptaDB will also be continuously updated as aptamer research evolves. We expect that AptaDB will become a powerful source for aptamer rational design and a valuable tool for aptamer screening in the future. For access to AptaDB, please visit http://lmmd.ecust.edu.cn/aptadb/.

Identification of aneuploidy-selective antiproliferation compounds.

Aneuploidy, an incorrect chromosome number, is a hallmark of cancer. Compounds that cause lethality in aneuploid, but not euploid, cells could therefore provide new cancer therapies. We have identified the energy stress-inducing agent AICAR, the protein folding inhibitor 17-AAG, and the autophagy inhibitor chloroquine as exhibiting this property. AICAR induces p53-mediated apoptosis in primary mouse embryonic fibroblasts (MEFs) trisomic for chromosome 1, 13, 16, or 19. AICAR and 17-AAG, especially when combined, also show efficacy against aneuploid human cancer cell lines. Our results suggest that compounds that interfere with pathways that are essential for the survival of aneuploid cells could serve as a new treatment strategy against a broad spectrum of human tumors.

admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties.

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370 000 high-quality experimental ADMET data for 104 652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

A c-di-GMP binding effector controls cell size in a cyanobacterium.

Cyclic-di-GMP (c-di-GMP) is a ubiquitous bacterial signaling molecule. It is also a critical player in the regulation of cell size and cell behaviors such as cell aggregation and phototaxis in cyanobacteria, which constitute an important group of prokaryotes for their roles in the ecology and evolution of the Earth. However, c-di-GMP receptors have never been revealed in cyanobacteria. Here, we report the identification of a c-di-GMP receptor, CdgR, from the filamentous cyanobacterium Anabaena PCC 7120. Crystal structural analysis and genetic studies demonstrate that CdgR binds c-di-GMP at the dimer interface and this binding is required for the control of cell size in a c-di-GMP-dependent manner. Different functions of CdgR, in ligand binding and signal transmission, could be separated genetically, allowing us to dissect its molecular signaling functions. The presence of the apo-form of CdgR triggers cell size reduction, consistent with the similar effects observed with a decrease of c-di-GMP levels in cells. Furthermore, we found that CdgR exerts its function by interacting with a global transcription factor DevH, and this interaction was inhibited by c-di-GMP. The lethal effect triggered by conditional depletion of DevH or by the production of several point-mutant proteins of CdgR in cells indicates that this signaling pathway plays critical functions in Anabaena. Our studies revealed a mechanism of c-di-GMP signaling in the control of cell size, an important and complex trait for bacteria. CdgR is highly conserved in cyanobacteria, which will greatly expand our understanding of the roles of c-di-GMP signaling in these organisms.

Identification of vital chemical information via visualization of graph neural networks.

Qualitative or quantitative prediction models of structure-activity relationships based on graph neural networks (GNNs) are prevalent in drug discovery applications and commonly have excellently predictive power. However, the network information flows of GNNs are highly complex and accompanied by poor interpretability. Unfortunately, there are relatively less studies on GNN attributions, and their developments in drug research are still at the early stages. In this work, we adopted several advanced attribution techniques for different GNN frameworks and applied them to explain multiple drug molecule property prediction tasks, enabling the identification and visualization of vital chemical information in the networks. Additionally, we evaluated them quantitatively with attribution metrics such as accuracy, sparsity, fidelity and infidelity, stability and sensitivity; discussed their applicability and limitations; and provided an open-source benchmark platform for researchers. The results showed that all attribution techniques were effective, while those directly related to the predicted labels, such as integrated gradient, preferred to have better attribution performance. These attribution techniques we have implemented could be directly used for the vast majority of chemical GNN interpretation tasks.

Large Polarization Near 50 µC/cm2 in a Single Unit Cell Layer SrTiO3.

The generally nonpolar SrTiO3 has attracted more attention recently because of its possibly induced novel polar states and related paraelectric-ferroelectric phase transitions. By using controlled pulsed laser deposition, high-quality, ultrathin, and strained SrTiO3 layers were obtained. Here, transmission electron microscopy and theoretical simulations have unveiled highly polar states in SrTiO3 films even down to one unit cell at room temperature, which were stabilized in the PbTiO3/SrTiO3/PbTiO3 sandwich structures by in-plane tensile strain and interfacial coupling, as evidenced by large tetragonality (∼1.05), notable polar ion displacement (0.019 nm), and thus ultrahigh spontaneous polarization (up to ∼50 µC/cm2). These values are nearly comparable to those of the strong ferroelectrics as the PbZrxTi1-xO3 family. Our findings provide an effective and practical approach for integrating large strain states into oxide films and inducing polarization in nonpolar materials, which may broaden the functionality of nonpolar oxides and pave the way for the discovery of new electronic materials.

Hydrogen-Bonded Mesoporous Frameworks with Tunable Pore Sizes and Architectures from Nanocluster Assembly Units.

Construction of mesoporous frameworks by noncovalent bonding still remains a great challenge. Here, we report a micelle-directed nanocluster modular self-assembly approach to synthesize a novel type of two-dimensional (2-D) hydrogen-bonded mesoporous frameworks (HMFs) for the first time based on nanoscale cluster units (1.0-3.0 nm in size). In this 2-D structure, a mesoporous cluster plate with ∼100 nm in thickness and several micrometers in size can be stably formed into uniform hexagonal arrays. Meanwhile, such a porous plate consists of several (3-4) dozens of layers of ultrathin mesoporous cluster nanosheets. The size of the mesopores can be precisely controlled from 11.6 to 18.5 nm by utilizing the amphiphilic diblock copolymer micelles with tunable block lengths. Additionally, the pore configuration of the HMFs can be changed from spherical to cylindrical by manipulating the concentration of the micelles. As a general approach, various new HMFs have been achieved successfully via a modular self-assembly of nanoclusters with switchable configurations (nanoring, Keggin-type, and cubane-like) and components (titanium-oxo, polyoxometalate, and organometallic clusters). As a demonstration, the titanium-oxo cluster-based HMFs show efficient photocatalytic activity for hydrogen evolution (3.6 mmol g-1h-1), with a conversion rate about 2 times higher than that of the unassembled titanium-oxo clusters (1.5 mmol g-1h-1). This demonstrates that HMFs exhibited enhanced photocatalytic activity compared with unassembled titanium-oxo clusters units.

Inferring Incidence of Unreported SARS-CoV-2 Infections Using Seroprevalence of Open Reading Frame 8 Antigen, Hong Kong.

We tested seroprevalence of open reading frame 8 antigens to infer the number of unrecognized SARS-CoV-2 Omicron infections in Hong Kong during 2022. We estimate 33.6% of the population was infected, 72.1% asymptomatically. Surveillance and control activities during large-scale outbreaks should account for potentially substantial undercounts.

Treponema pallidum lipoprotein Tp0768 promotes the migration and adhesion of THP-1 cells to vascular endothelial cells through stress of the endoplasmic reticulum and the NF-κB/HIF-1α pathway.

Endothelial cell injury is a key factor in the spread of infection and pathogenicity of Treponema pallidum. The migration and adhesion reaction mediated by T. pallidum lipoprotein plays an important role. This study aimed to systematically explore the migration and adhesion effect of T. pallidum lipoprotein Tp0768 and its molecular mechanism. Stimulating vascular endothelial cells with Tp0768 increased the expression of ICAM-1, MCP-1, and IL-8. Moreover, it promoted the migration and adhesion of THP-1 cells to vascular endothelial cells. Our results revealed that Tp0768 promoted the THP-1 cells migrating and adhering to vascular endothelial cells by the PERK and IRE-1α pathways of endoplasmic reticulum (ER) stress. We further demonstrated that the inhibition of the NF-κB pathway and the downregulation of hypoxia-inducible factor 1 alpha (HIF-1α) reduced the mRNA levels of ICAM-1, MCP-1, and IL-8 induced by Tp0768. Also, the adhesion rate of THP-1 cells to endothelial cells decreased. After inhibiting ER stress, NF-κB p65 nuclear translocation was weakened, and the mRNA level of HIF-1α was also significantly downregulated. Our results indicated that T. pallidum lipoprotein Tp0768 promoted the migration and adhesion of THP-1 cells to vascular endothelial cells through ER stress and NF-κB/HIF-1α pathway.

MEG3 in hematologic malignancies: from the role of disease biomarker to therapeutic target.

Maternally expressed gene 3 ( MEG3 ) is a noncoding RNA that is known as a tumor suppressor in solid cancers. Recently, a line of studies has emphasized its potential role in hematological malignancies in terms of tumorigenesis, metastasis, and drug resistance. Similar to solid cancers, MEG3 can regulate various cancer hallmarks via sponging miRNA, transcriptional, or posttranslational regulation mechanisms, but may regulate different key elements. In contrast with solid cancers, in some subtypes of leukemia, MEG3 has been found to be upregulated and oncogenic. In this review, we systematically describe the role and underlying mechanisms of MEG3 in multiple types of hematological malignancies. Particularly, we highlight the role of MEG3 in drug resistance and as a novel therapeutic target.

A Multifunctional Zwitterion Electrolyte Additive for Highly Reversible Zinc Metal Anode.

Although zinc metal anode is promising for zinc-ion batteries (ZIBs) owing to high energy density, its reversibility is significantly obstructed by uncontrolled dendrite growth and parasitic reactions. Optimizing electrolytes is a facile yet effective method to simultaneously address these issues. Herein, 2-(N-morpholino)ethanesulfonic acid (MES), a pH buffer as novel additive, is initially introduced into conventional ZnSO4 electrolyte to ensure a dendrite-free zinc anode surface, enabling a stable Zn/electrolyte interface, which is achieved by controlling the solvated sheath through H2O poor electric double layer (EDL) derived from zwitterionic groups. Moreover, this zwitterionic additive can balance localized H+ concentration of the electrolyte system, thus preventing parasitic reactions in damaging electrodes. DFT calculation proves that the MES additive has a strong affinity with Zn2+ and induces uniform deposition along (002) orientation. As a result, the Zn anode in MES-based electrolyte exhibits exceptional plating/stripping lifespan with 1600 h at 0.5 mA cm-2 (0.5 mAh cm-2) and 430 h at 5.0 mA cm-2 (5.0 mAh cm-2) while it maintains high coulombic efficiency of 99.8%. This work proposes an effective and facile approach for designing dendrite-free anode for future aqueous Zn-based storage devices.

ADENet: a novel network-based inference method for prediction of drug adverse events.

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and improve drug safety assessment. With an increasing number of biological and medical data, computational methods such as network-based methods were proposed for ADE prediction with high efficiency and low cost. However, previous network-based methods rely on the topological information of known drug-ADE networks, and hence cannot make predictions for novel compounds without any known ADE. In this study, we introduced chemical substructures to bridge the gap between the drug-ADE network and novel compounds, and developed a novel network-based method named ADENet, which can predict potential ADEs for not only drugs within the drug-ADE network, but also novel compounds outside the network. To show the performance of ADENet, we collected drug-ADE associations from a comprehensive database named MetaADEDB and constructed a series of network-based prediction models. These models obtained high area under the receiver operating characteristic curve values ranging from 0.871 to 0.947 in 10-fold cross-validation. The best model further showed high performance in external validation, which outperformed a previous network-based and a recent deep learning-based method. Using several approved drugs as case studies, we found that 32-54% of the predicted ADEs can be validated by the literature, indicating the practical value of ADENet. Moreover, ADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer). In summary, our method would provide a promising tool for ADE prediction and drug safety assessment in drug discovery and development.

Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening.

Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.

A group VIIIa ethylene-responsive factor, CmERF4, negatively regulates waterlogging tolerance in chrysanthemum.

Ethylene-responsive factors (ERF) play an important role in plant responses to waterlogging stress. However, the function and mechanism of action of ERFVIII in response to waterlogging stress remain poorly understood. In this study, we found that expression of the ERF VIIIa gene CmERF4 in chrysanthemum was induced by waterlogging stress. CmERF4 localized to the nucleus when expressed in tobacco leaves. Yeast two-hybrid and luciferase assays showed that CmERF4 is a transcriptional inhibitor. CmERF4 overexpression in chrysanthemum reduced plant waterlogging tolerance, whereas overexpression of the chimeric activator CmERF4-VP64 reversed its transcriptional activity, promoting higher waterlogging tolerance than that observed in wild-type plants, indicating that CmERF4 negatively regulates waterlogging tolerance. Transcriptome profiling showed that energy metabolism and reactive oxygen species (ROS) pathway-associated genes were differentially expressed between CmERF4-VP64 and wild-type plants. RT-qPCR analysis of selected energy metabolism and reactive oxygen species-related genes showed that the gene expression patterns were consistent with the expression levels obtained from RNA-seq analysis. Overall, we identified new functions of CmERF4 in negatively regulating chrysanthemum waterlogging tolerance by modulating energy metabolism and ROS pathway genes.

Long range topography by dispersion unmatched spectral-domain interferometry based on virtually imaged phased array modes.

We propose to realize a long range topography by dispersion unmatched spectral-domain interferometry based on virtually imaged phased array (VIPA) modes. By filtering the continuous spectrum of a supercontinuum source through a side-entrance Fabry-Perot etalon configured at two input angles, two groups of VIPA modes are generated. A method based on unmatched dispersion is proposed for non-aliasing reconstruction of the true depth from the interference spectrum under-sampled at two groups of VIPA modes. With the high spectral resolution provided by the VIPA modes instead of the grating-based spectrometer, only a 10 dB falloff in sensitivity over a range of 10 mm was demonstrated. The feasibility of the proposed method was confirmed by topography of a sample of gauge blocks and a model of three-dimensional (3D) printed tooth. The occlusal surface of the tooth model was further quantitatively evaluated, demonstrating its potential application in long range 3D topography.

Investigating the Structure-property Relationships of Two Cd-based Hybrid Multifunctional Compounds with High Tc, Bright Fluorescence and Wide Band-gap.

Organic-inorganic hybrid multifunctional materials have shown significant application in lighting and sensor fields, owing to their prominent performance and diversity structures. Herein, we synthesized two multifunctional compounds: (propyl-quinuclidone)2 CdBr4 (1) and (F-butyl-quinuclidone)2 CdBr4 (2). By introducing light-emitting organic cation with flexible long chain, 1 and 2 exhibit excellent transition properties and bright blue-white fluorescence. Then, combine fluorescence lifetime and first-principal calculation, providing evidence for the electron transfer emission. Subsequently, investigated the impact of substituent carbon chain length (methyl to butyl), structural rigidity (C-C to C-F) and halide framework (Cl to I) on the fluorescence properties. Results indicate that Cd⋅⋅⋅Cd distance and structural rigidity play an important role in fluorescence. Overall, our research provides valuable insight and example for chemical modifications enhance compound performance.

In Silico Prediction of Skin Sensitization for Compounds via Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory.

Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.

AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods.

Skin Corrosion/Irritation (Corr./Irrit.) has long been a health hazard in the Globally Harmonized System (GHS). Several in silico models have been built to predict Skin Corr./Irrit. as an alternative to the increasingly restricted animal testing. However, current studies are limited by data amount/quality and model availability. To address these issues, we compiled a traceable consensus GHS data set comprising 731 Corr., 1283 Irrit., and 1205 negative (Neg.) samples from 6 governmental databases and 2 external data sets. Then, a series of binary classifiers were developed with five machine learning (ML) algorithms and six molecular representations. For 10-fold cross-validation, the best Corr. vs Neg. classifier achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 97.1%, while the best Irrit. vs Neg. classifier achieved an AUC of 84.7%. Compared with existing in silico tools on external validation, our Attentive FP classifiers showed the highest metrics on Corr. vs Neg. and the second highest accuracy on Irrit. vs Neg. The SHapley Additive exPlanation approach was further applied to figure out important molecular features, and the attention weights were visualized to perform interpretable prediction. Structural alerts associated with Skin Corr./Irrit. were also identified. The interpretable Attentive FP classifiers were integrated into the software AttentiveSkin at https://github.com/BeeBeeWong/AttentiveSkin. The conventional ML classifiers are also provided on our platform admetSAR at http://lmmd.ecust.edu.cn/admetsar2/. Considering the data deficiency and the limited model availability of Skin Corr./Irrit., we believe that our data set and models could facilitate chemical safety assessment and relevant studies.

In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods.

The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.