Biomimetic nanocarriers loaded with temozolomide by cloaking brain-targeting peptides for targeting drug delivery system to promote anticancer effects in glioblastoma cells.

Glioma is the leading cancer of the central nervous system (CNS). The efficacy of glioma treatment is significantly hindered by the presence of the blood-brain barrier (BBB) and blood-brain tumour barrier (BBTB), which prevent most drugs from entering the brain and tumours. Hence, we established a novel drug delivery nanosystem of brain tumour-targeting that could self-assemble the method using an amphiphilic Zein protein isolated from corn. Zein's amphiphilicity prompted it to self-assembled into NPs, efficiently containing TMZ. This allowed us to investigate temozolomide (TMZ) for glioblastoma (GBM) treatment. To construct TMZ-encapsulated NPs (TMZ@RVG-Zein NPs), the NPs' Zein was clocked to rabies virus glycoprotein 29 (RVG29). To verify that the NPs could penetrate the BBB and precisely target and kill the GBM cancer cell line, in vitro studies were performed. The process of NPs penetrating cancer cell membranes was investigated using enzyme-linked immunosorbent assays (ELISAs) to measure the expressions of nicotinic acetylcholine receptors (nAChRs) on the U87 cell line. Therefore, effective targeted brain cancer treatment is possible by forming NP clocks, a cell-penetrating natural Zein protein with an RVG29. These NPs can penetrate the blood-brain barrier (BBB) and enter the glioblastoma (U87) cell line to release TMZ. These NPs have a distinct cocktail of biocompatibility and brain-targeting abilities, making them ideal for involving brain diseases.

Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain.

Cytosine DNA methylation is essential in brain development and is implicated in various neurological disorders. Understanding DNA methylation diversity across the entire brain in a spatial context is fundamental for a complete molecular atlas of brain cell types and their gene regulatory landscapes. Here we used single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq)1 technologies to generate 301,626 methylomes and 176,003 chromatin conformation-methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell taxonomy with 4,673 cell groups and 274 cross-modality-annotated subclasses. We identified 2.6 million differentially methylated regions across the genome that represent potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide spatial transcriptomics data validated the association of spatial epigenetic diversity with transcription and improved the anatomical mapping of our epigenetic datasets. Furthermore, chromatin conformation diversities occurred in important neuronal genes and were highly associated with DNA methylation and transcription changes. Brain-wide cell-type comparisons enabled the construction of regulatory networks that incorporate transcription factors, regulatory elements and their potential downstream gene targets. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a whole-brain SMART-seq2 dataset. Our study establishes a brain-wide, single-cell DNA methylome and 3D multi-omic atlas and provides a valuable resource for comprehending the cellular-spatial and regulatory genome diversity of the mouse brain.

Single-cell DNA methylation and 3D genome architecture in the human brain.

Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.

Human Immune Cell Epigenomic Signatures in Response to Infectious Diseases and Chemical Exposures.

Variations in DNA methylation patterns in human tissues have been linked to various environmental exposures and infections. Here, we identified the DNA methylation signatures associated with multiple exposures in nine major immune cell types derived from peripheral blood mononuclear cells (PBMCs) at single-cell resolution. We performed methylome sequencing on 111,180 immune cells obtained from 112 individuals who were exposed to different viruses, bacteria, or chemicals. Our analysis revealed 790,662 differentially methylated regions (DMRs) associated with these exposures, which are mostly individual CpG sites. Additionally, we integrated methylation and ATAC-seq data from same samples and found strong correlations between the two modalities. However, the epigenomic remodeling in these two modalities are complementary. Finally, we identified the minimum set of DMRs that can predict exposures. Overall, our study provides the first comprehensive dataset of single immune cell methylation profiles, along with unique methylation biomarkers for various biological and chemical exposures.

Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain.

Cytosine DNA methylation is essential in brain development and has been implicated in various neurological disorders. A comprehensive understanding of DNA methylation diversity across the entire brain in the context of the brain's 3D spatial organization is essential for building a complete molecular atlas of brain cell types and understanding their gene regulatory landscapes. To this end, we employed optimized single-nucleus methylome (snmC-seq3) and multi-omic (snm3C-seq1) sequencing technologies to generate 301,626 methylomes and 176,003 chromatin conformation/methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell type taxonomy that contains 4,673 cell groups and 261 cross-modality-annotated subclasses. We identified millions of differentially methylated regions (DMRs) across the genome, representing potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide multiplexed error-robust fluorescence in situ hybridization (MERFISH2) data validated the association of this spatial epigenetic diversity with transcription and allowed the mapping of the DNA methylation and topology information into anatomical structures more precisely than our dissections. Furthermore, multi-scale chromatin conformation diversities occur in important neuronal genes, highly associated with DNA methylation and transcription changes. Brain-wide cell type comparison allowed us to build a regulatory model for each gene, linking transcription factors, DMRs, chromatin contacts, and downstream genes to establish regulatory networks. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a companion whole-brain SMART-seq3 dataset. Our study establishes the first brain-wide, single-cell resolution DNA methylome and 3D multi-omic atlas, providing an unparalleled resource for comprehending the mouse brain's cellular-spatial and regulatory genome diversity.

CuSe2 Nanocubes Enabling Efficient Sodium Storage.

As the most promising candidate for lithium-ion batteries (LIBs), the electrochemical performance of sodium-ion batteries (SIBs) is highly dependent on the electrode materials. Copper selenides have established themselves as potential anode materials for SIBs due to their high theoretical capacity and good conductivity. However, the poor rate performance and fast capacity fading are the major challenges to their practical application in SIBs. Herein, single-crystalline CuSe2 nanocubes (CuSe2 NCs) are successfully synthesized via a solvothermal method. As an anode of SIBs, the CuSe2 NCs render an almost 100% initial Coulombic efficiency, an outstanding long cycle life, e.g., 380 mA h g-1 after 1700 cycles at 10 A g-1, and an unprecedented rate performance of 344 mA h g-1 at 50 A g-1. Ex situ X-ray diffraction (XRD) patterns reveal the crystalline transformation of energy-storage materials, and the density functional theory (DFT) conclusion suggests that fast and stable ion diffusion kinetics enhances their electrochemical performance upon sodiation/desodiaton. The investigation into the mechanism provides a theoretical basis for subsequent practical applications.

2D Bismuth@N-Doped Carbon Sheets for Ultrahigh Rate and Stable Potassium Storage.

Metallic Bi, as an alloying-type anode material, has demonstrated tremendous potential for practical application of potassium-ion batteries. However, the giant volume expansion, severe structure pulverization, and sluggish dynamics of Bi-based materials result in unsatisfied rate performance and unstable cycling stability. Here, 2D bismuth@N-doped carbon sheets with BiOC bond and internal void space (2D Bi@NOC) are successfully fabricated via a self-template strategy to address these issues, which own ultrafast electrochemical kinetics and impressive long-term cycling stability for delivering an admirable capacity of 341.7 mAh g-1 after 1000 cycles at 10 A g-1 and impressive rate capability of 220.6 mAh g-1 at 50 A g-1 . Particularly, the in situ transmission electron microscopy observations visualize the real-time alloying/dealloying process and reveal that plastic carbon shell and void space can availably relieve dramatic volume stress and powerfully maintain structural integrity. Density functional theory calculation and ultraviolet photoelectron spectroscopy test certify that the robust BiOC bond is thermodynamically and kinetically beneficial for adsorption/diffusion of K+ . This work will light on designing advanced high-performance energy materials and provide important evidence for understanding the energy storage mechanism of alloy-based materials.

Graphene-Promoted Adhesion-Reduced Expansion of Discontinuous Palladium Nanowires upon Hydrogenation.

Monitoring conductivity changes of discontinuous palladium (Pd) nanostructures upon hydrogenation is becoming one of the most promising approaches toward hydrogen sensing. Development of sensors in this type has long been impeded due to strong ubiquitous interfacial adhesion which could distinctly restrict Pd expansion so as to hinder the closing of a nanogap. Herein, graphene underlayers were applied in the fabrication of nanogap-based hydrogen sensors to promote the lateral expansion of a Pd nanowire upon hydrogenation by reducing the adhesion between the metal and the substrate. In order to clarify details as well as mechanisms underlaid of graphene-enhanced Pd expansion, nanowire samples with serial lengths (6-48 µm) and gaps (0-260 nm in width) were controllably prepared on single-layer graphene (SLG), double-layer graphene (DLG), and quadruple-layer graphene (QLG, DLG × 2) via the combination of electron beam lithography (EBL) and electron beam deposition (EBD) technology. Response features and intrinsic analysis in physical sense of the graphene-based discontinuous Pd circuits upon hydrogen were established, in light of which the effects of underlayers on Pd expansion and on nanogap closing process were investigated. Such graphene-promoted expansion was demonstrated through the achievement of the closure of a large gap threshold (Gt) up to 260 nm as well as the systematical investigation of its influence on the sensing performance.

Safety and Efficacy of Stereotactic Ablative Radiotherapy for Ultra-Central Lung Cancer.

Background: Ultra-central lung cancer (UCLC) is difficult to achieve surgical treatment. Over the past few years, stereotactic ablative radiotherapy (SABR) or stereotactic body radiotherapy (SBRT) obviously improved the clinical efficacy and survival of UCLC patients. However, the adapted scheme of radiation therapy is still controversial. For this, a single arm retrospective analysis was performed on UCLC patients treated with SBRT. Material and Methods: We retrospectively studied primary UCLC patients who were treated with SBRT of 56 Gy/6-8f between 2010 and 2018. UCLC was defined as planning target volume (PTV) touching or overlapping the proximal bronchial tree, trachea, esophagus, heart, pulmonary vein, or pulmonary artery within 2 cm around the bronchial tree in all directions. Results: A total of 58 patients whose median age was 68 years (range, 46-85) were included in our study, 79.3% of whom did not undergo any previous therapy. The median dose of the PTV was 77.8 Gy (range, 43.3-91.8), and the median PTV of tumors was 6.2 cm3 (range, 12.9-265.0). With a median follow-up of 57 months (range, 6-90 months), the median cumulative overall survival (OS) rate was 58 months (range, 2-105). In addition, the 1-year, 2-year and 5-year OS rates were 94.7%, 75.0% and 45.0%, respectively. In our univariable analysis (p=0.020) and multivariate analysis (p=0.004), the OS rate was associated with the PTV. The 5-year OS rates for PTV <53.0 cm3 and PTV ≥53.0 cm3 were 61.6% and 37.4%, respectively. Regarding toxicity after SBRT, there were two cases (3.5%) with grade ≥3 adverse events, of which 1 case died of sudden severe unexplained hemoptysis. Conclusions: Patients with UCLC can benefit from SBRT at a dose of 56 Gy/6-8f. On the other hand, smaller PTV was associated with superior outcomes, and the cure difference needs to be validated by prospective comparative trials.

Online public opinion evaluation through the functional resonance analysis method and deep analysis.

A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that the outbreak of internet public opinion and its negative impacts, such as the occurrence of major security incidents, are a result of coupling and the complex interaction of many factors. The Functional Resonance Analysis Method model is composed of those factors and considers the stages of network information dissemination, the unique propagation rule, and textual sentiment resonance on the internet. Moreover, it is the first public opinion governance method that simultaneously highlights the complex system, functional identification, and functional resonance. It suggests a more effective method to shorten the dissipation time of negative public opinion and is a considerable improvement over previous models for risk-prediction. Based on resonance theory and deep learning, this study establishes public opinion resonance functions, which made it possible to analyze public opinion triggers and build a simulation model to explore the patterns of public opinion development through long-term data capture. The simulation results of the Functional Resonance Analysis Method suggest that the resonance in the model is consistent with the evolution of public opinion in real situations and that the components of the resonance of public opinion can be separated into eleven subjective factors and three objective factors. In addition, managing the subjective factors can significantly accelerate the dissipation of negative opinions.

DNA methylation atlas of the mouse brain at single-cell resolution.

Mammalian brain cells show remarkable diversity in gene expression, anatomy and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Here we carry out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing1,2 to profile 103,982 nuclei (including 95,815 neurons and 8,167 non-neuronal cells) from 45 regions of the mouse cortex, hippocampus, striatum, pallidum and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, we constructed an artificial neural network model that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data3 enabled prediction of high-confidence enhancer-gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments4. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum.

The impact of media reports on energy and environmental efficiency in China: evidence from modified dynamic DEA with undesirable outputs.

BACKGROUND: Rapid economic growth in China has resulted in a commensurate increase in energy consumption, which in turn has seen an increase in environmental pollution problems. Past research has tended to focus on energy and environmental efficiency analyses and has rarely examined the media influence on environmental protection efforts. Further, most studies have used static models and ignored the dynamic changes over time. METHODS: To go some way to filling this research gap, this study developed a modified undesirable Dynamic DEA model that included air quality index (AQI) and CO2 indicators to explore the relationships between energy, the environment, and media report efficiencies in 31 Chinese cities from 2013 to 2016. RESULTS: It was found that: (1) Chongqing, Guangzhou, Nanjing and Shanghai had efficiencies of 1, but Lanzhou, Shijiazhuang, Taiyuan, Xining and Yinchuan needed significant improvements; (2) Chongqing, Guangzhou, Kunming, Nanning and Shanghai had relatively high media efficiencies, but the other cities had low efficiencies and required improvements; (3) the CO2 emissions efficiencies in most cities were better than the air quality index efficiencies; and 4. the media reports in most cities were found to have a more positive impact on the CO2 emissions efficiency than on the AQI efficiency. CONCLUSIONS: Environmental awareness enhances civilian health and promotes economic growth. Therefore, as the news media should be responsible for promoting environmental protection, they need to increase their environmental pollution coverage. It was found that the environmental pollution media report quality and especially air pollution reports needed improvements, and greater media coverage on environmental pollution and awareness was needed.

PHYTOCHROME-INTERACTING FACTORs trigger environmentally responsive chromatin dynamics in plants.

The interplay between light receptors and PHYTOCHROME-INTERACTING FACTORs (PIFs) serves as a regulatory hub that perceives and integrates environmental cues into transcriptional networks of plants1,2. Although occupancy of the histone variant H2A.Z and acetylation of histone H3 have emerged as regulators of environmentally responsive gene networks, how these epigenomic features interface with PIF activity is poorly understood3-7. By taking advantage of rapid and reversible light-mediated manipulation of PIF7 subnuclear localization and phosphorylation, we simultaneously assayed the DNA-binding properties of PIF7, as well as its impact on chromatin dynamics genome wide. We found that PIFs act rapidly to reshape the H2A.Z and H3K9ac epigenetic landscape in response to a change in light quality. Furthermore, we discovered that PIFs achieve H2A.Z removal through direct interaction with EIN6 ENHANCER (EEN), the Arabidopsis thaliana homolog of the chromatin remodeling complex subunit INO80 Subunit 6 (Ies6). Thus, we describe a PIF-INO80 regulatory module that is an intermediate step for allowing plants to change their growth trajectory in response to environmental changes.

Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions.

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

Upregulated lnc­HRK­2:1 prompts nucleus pulposus cell senescence in intervertebral disc degeneration.

Intervertebral disc (IVD) degeneration is a complicated physiological change involving cellular senescence, inflammation and the degradation of the extracellular matrix. Long non­coding RNAs (lncRNAs) have been identified as new players in IVD degeneration. The present study aimed to identify lncRNAs implicated in IVD degeneration via the regulation of cellular senescence. In the present study, nucleus pulposus (NP) cells isolated from moderately degenerated IVD tissues exhibited a senescent phenotype with increased senescence rates, detected by senescence­associated ß­galactosidase (SA­ß­gal) staining, and reduced growth and migratory abilities. Microarray and target prediction analyses identified 353 differentially expressed lncRNAs, and 251 cis­ and 2,170 trans­acting targets in degenerated NP cells. Bioinformatic analyses revealed that these predicted targets were enriched in the regulation of response to DNA damage stimulus, positive regulation of cell cycle processes and interferon­ß production. In addition, a network of the top 10 upregulated and top 10 downregulated lncRNA targets was constructed, and two trans­acting targets, C­C motif chemokine ligand 5 (CCL5) and polyribonucleotide nucleotidyltransferase 1 (PNPT1) involved in aging or senescence, and their corresponding lncRNAs, lnc­ST8SIA5­1:2 and lnc­HRK­2:1, were identified. Reverse transcription­quantitative PCR validation demonstrated that the two targets and two candidate lncRNAs were significantly upregulated in degenerated NP cells. Overexpression of lnc­HRK­2:1, with validated higher expression levels, in normal NP cells induced a senescent phenotype, with enhanced rates of senescence detected by SA­ß­gal staining in cells, decreased growth and migratory abilities and improved expression levels of CCL5 and PNPT1. Collectively, these results suggested that upregulation of lnc­HRK­2:1 prompted NP cell senescence in IVD degeneration, which may be associated with increased expression levels of CCL5 and PNPT1.

Publisher Correction: Integrated multi-omics framework of the plant response to jasmonic acid.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Spatiotemporal DNA methylome dynamics of the developing mouse fetus.

Cytosine DNA methylation is essential for mammalian development but understanding of its spatiotemporal distribution in the developing embryo remains limited1,2. Here, as part of the mouse Encyclopedia of DNA Elements (ENCODE) project, we profiled 168 methylomes from 12 mouse tissues or organs at 9 developmental stages from embryogenesis to adulthood. We identified 1,808,810 genomic regions that showed variations in CG methylation by comparing the methylomes of different tissues or organs from different developmental stages. These DNA elements predominantly lose CG methylation during fetal development, whereas the trend is reversed after birth. During late stages of fetal development, non-CG methylation accumulated within the bodies of key developmental transcription factor genes, coinciding with their transcriptional repression. Integration of genome-wide DNA methylation, histone modification and chromatin accessibility data enabled us to predict 461,141 putative developmental tissue-specific enhancers, the human orthologues of which were enriched for disease-associated genetic variants. These spatiotemporal epigenome maps provide a resource for studies of gene regulation during tissue or organ progression, and a starting point for investigating regulatory elements that are involved in human developmental disorders.

Integrated multi-omics framework of the plant response to jasmonic acid.

Understanding the systems-level actions of transcriptional responses to hormones provides insight into how the genome is reprogrammed in response to environmental stimuli. Here, we investigated the signalling pathway of the hormone jasmonic acid (JA), which controls a plethora of critically important processes in plants and is orchestrated by the transcription factor MYC2 and its closest relatives in Arabidopsis thaliana. We generated an integrated framework of the response to JA, which spans from the activity of master and secondary regulatory transcription factors, through gene expression outputs and alternative splicing, to protein abundance changes, protein phosphorylation and chromatin remodelling. We integrated time-series transcriptome analysis with (phospho)proteomic data to reconstruct gene regulatory network models. These enabled us to predict previously unknown points of crosstalk of JA to other signalling pathways and to identify new components of the JA regulatory mechanism, which we validated through targeted mutant analysis. These results provide a comprehensive understanding of how a plant hormone remodels cellular functions and plant behaviour, the general principles of which provide a framework for analyses of cross-regulation between other hormone and stress signalling pathways.

Impact of Media Reports and Environmental Pollution on Health and Health Expenditure Efficiency.

Over the past few decades, China's rapid economic, energy, and industrial developments have caused serious environmental damage. However, as there are large resource, energy use, economic, and environmental damage differences across Chinese regions, the Chinese government is seeking to reduce city pollution across the country. Most previous analyses have only looked at these issues on a single level; for example, the impact of environmental pollution on health, or energy and environmental efficiency analyses, but there have been few studies that have conducted overall analyses. Further, many of the methods that have been used in previous research have employed one-stage radial or non-radial analyses without considering regional differences. Therefore, this paper developed a meta undesirable two-stage EBM DEA model to analyze the energy, environment, health, and media communication efficiencies in 31 Chinese cities, from which it was found that the productivity efficiency in most cities was better than the health treatment efficiencies, the GDP and fixed asset efficiency improvements were small, the air quality index (AQI) and CO2 efficiencies varied widely between the cities, media report and governance inputs were generally inefficient, the birth rate efficiencies were better than the respiratory disease efficiencies, and the technical gap was best in Guangzhou, Shanghai, and Lhasa. Also, it found that high-income cities have a higher technology gap than upper middle-income cities, and media reports efficiency have a high correlation with respiratory diseases and CO2.