A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset.

Cell-type annotation is a critical step in single-cell data analysis. With the development of numerous cell annotation methods, it is necessary to evaluate these methods to help researchers use them effectively. Reference datasets are essential for evaluation, but currently, the cell labels of reference datasets mainly come from computational methods, which may have computational biases and may not reflect the actual cell-type outcomes. This study first constructed an experimentally labeled immune cell-subtype single-cell dataset of the same batch and systematically evaluated 18 cell annotation methods. We assessed those methods under five scenarios, including intra-dataset validation, immune cell-subtype validation, unsupervised clustering, inter-dataset annotation, and unknown cell-type prediction. Accuracy and ARI were evaluation metrics. The results showed that SVM, scBERT, and scDeepSort were the best-performing supervised methods. Seurat was the best-performing unsupervised clustering method, but it couldn't fully fit the actual cell-type distribution. Our results indicated that experimentally labeled immune cell-subtype datasets revealed the deficiencies of unsupervised clustering methods and provided new dataset support for supervised methods.

TransCDR: a deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion.

BACKGROUND: Accurate and robust drug response prediction is of utmost importance in precision medicine. Although many models have been developed to utilize the representations of drugs and cancer cell lines for predicting cancer drug responses (CDR), their performances can be improved by addressing issues such as insufficient data modality, suboptimal fusion algorithms, and poor generalizability for novel drugs or cell lines. RESULTS: We introduce TransCDR, which uses transfer learning to learn drug representations and fuses multi-modality features of drugs and cell lines by a self-attention mechanism, to predict the IC50 values or sensitive states of drugs on cell lines. We are the first to systematically evaluate the generalization of the CDR prediction model to novel (i.e., never-before-seen) compound scaffolds and cell line clusters. TransCDR shows better generalizability than 8 state-of-the-art models. TransCDR outperforms its 5 variants that train drug encoders (i.e., RNN and AttentiveFP) from scratch under various scenarios. The most critical contributors among multiple drug notations and omics profiles are Extended Connectivity Fingerprint and genetic mutation. Additionally, the attention-based fusion module further enhances the predictive performance of TransCDR. TransCDR, trained on the GDSC dataset, demonstrates strong predictive performance on the external testing set CCLE. It is also utilized to predict missing CDRs on GDSC. Moreover, we investigate the biological mechanisms underlying drug response by classifying 7675 patients from TCGA into drug-sensitive or drug-resistant groups, followed by a Gene Set Enrichment Analysis. CONCLUSIONS: TransCDR emerges as a potent tool with significant potential in drug response prediction.

Single-cell transcriptional atlas of tumor-associated macrophages in breast cancer.

BACKGROUND: The internal heterogeneity of breast cancer, notably the tumor microenvironment (TME) consisting of malignant and non-malignant cells, has been extensively explored in recent years. The cells in this complex cellular ecosystem activate or suppress tumor immunity through phenotypic changes, secretion of metabolites and cell-cell communication networks. Macrophages, as the most abundant immune cells within the TME, are recruited by malignant cells and undergo phenotypic remodeling. Tumor-associated macrophages (TAMs) exhibit a variety of subtypes and functions, playing significant roles in impacting tumor immunity. However, their precise subtype delineation and specific function remain inadequately defined. METHODS: The publicly available single-cell transcriptomes of 49,141 cells from eight breast cancer patients with different molecular subtypes and stages were incorporated into our study. Unsupervised clustering and manual cell annotation were employed to accurately classify TAM subtypes. We then conducted functional analysis and constructed a developmental trajectory for TAM subtypes. Subsequently, the roles of TAM subtypes in cell-cell communication networks within the TME were explored using endothelial cells (ECs) and T cells as key nodes. Finally, analyses were repeated in another independent publish scRNA datasets to validate our findings for TAM characterization. RESULTS: TAMs are accurately classified into 7 subtypes, displaying anti-tumor or pro-tumor roles. For the first time, we identified a new TAM subtype capable of proliferation and expansion in breast cancer-TUBA1B+ TAMs playing a crucial role in TAMs diversity and tumor progression. The developmental trajectory illustrates how TAMs are remodeled within the TME and undergo phenotypic and functional changes, with TUBA1B+ TAMs at the initial point. Notably, the predominant TAM subtypes varied across different molecular subtypes and stages of breast cancer. Additionally, our research on cell-cell communication networks shows that TAMs exert effects by directly modulating intrinsic immunity, indirectly regulating adaptive immunity through T cells, as well as influencing tumor angiogenesis and lymphangiogenesis through ECs. CONCLUSIONS: Our study establishes a precise single-cell atlas of breast cancer TAMs, shedding light on their multifaceted roles in tumor biology and providing resources for targeting TAMs in breast cancer immunotherapy.

MDTips: a multimodal-data-based drug-target interaction prediction system fusing knowledge, gene expression profile, and structural data.

MOTIVATION: Screening new drug-target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based-DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework. RESULTS: We developed MDTips, a multimodal-data-based DTI prediction system, by fusing the knowledge graphs, gene expression profiles, and structural information of drugs/targets. MDTips yielded accurate and robust performance on DTI predictions. We found that multimodal fusion learning can fully consider the importance of each modality and incorporate information from multiple aspects, thus improving model performance. Extensive experimental results demonstrate that deep learning-based encoders (i.e. Attentive FP and Transformer) outperform traditional chemical descriptors/fingerprints, and MDTips outperforms other state-of-the-art prediction models. MDTips is designed to predict the input drugs' candidate targets, side effects, and indications with all available modalities. Via MDTips, we reverse-screened candidate targets of 6766 drugs, which can be used for drug repurposing and discovery. AVAILABILITY AND IMPLEMENTATION: https://github.com/XiaoqiongXia/MDTips and https://doi.org/10.5281/zenodo.7560544.

Enhancing recognition and interpretation of functional phenotypic sequences through fine-tuning pre-trained genomic models.

BACKGROUND: Decoding human genomic sequences requires comprehensive analysis of DNA sequence functionality. Through computational and experimental approaches, researchers have studied the genotype-phenotype relationship and generate important datasets that help unravel complicated genetic blueprints. Thus, the recently developed artificial intelligence methods can be used to interpret the functions of those DNA sequences. METHODS: This study explores the use of deep learning, particularly pre-trained genomic models like DNA_bert_6 and human_gpt2-v1, in interpreting and representing human genome sequences. Initially, we meticulously constructed multiple datasets linking genotypes and phenotypes to fine-tune those models for precise DNA sequence classification. Additionally, we evaluate the influence of sequence length on classification results and analyze the impact of feature extraction in the hidden layers of our model using the HERV dataset. To enhance our understanding of phenotype-specific patterns recognized by the model, we perform enrichment, pathogenicity and conservation analyzes of specific motifs in the human endogenous retrovirus (HERV) sequence with high average local representation weight (ALRW) scores. RESULTS: We have constructed multiple genotype-phenotype datasets displaying commendable classification performance in comparison with random genomic sequences, particularly in the HERV dataset, which achieved binary and multi-classification accuracies and F1 values exceeding 0.935 and 0.888, respectively. Notably, the fine-tuning of the HERV dataset not only improved our ability to identify and distinguish diverse information types within DNA sequences but also successfully identified specific motifs associated with neurological disorders and cancers in regions with high ALRW scores. Subsequent analysis of these motifs shed light on the adaptive responses of species to environmental pressures and their co-evolution with pathogens. CONCLUSIONS: These findings highlight the potential of pre-trained genomic models in learning DNA sequence representations, particularly when utilizing the HERV dataset, and provide valuable insights for future research endeavors. This study represents an innovative strategy that combines pre-trained genomic model representations with classical methods for analyzing the functionality of genome sequences, thereby promoting cross-fertilization between genomics and artificial intelligence.

Adverse events profile associated with intermittent fasting in adults with overweight or obesity: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: There is little evidence to comprehensively summarize the adverse events (AEs) profile of intermittent fasting (IF) despite its widespread use in patients with overweight or obesity. METHODS: We searched the main electronic databases and registry websites to identify eligible randomized controlled trials (RCTs) comparing IF versus control groups. A direct meta-analysis using a fixed-effect model was conducted to pool the risk differences regarding common AEs and dropouts. Study quality was assessed by using the Jadad scale. Pre-specified subgroup and sensitivity analyses were conducted to explore potential heterogeneity. RESULTS: A total of 15 RCTs involving 1,365 adult individuals were included. Findings did not show a significant difference between IF and Control in risk rate of fatigue [0%, 95% confidence interval (CI), -1% to 2%; P = 0.61], headache [0%, 95%CI: -1% to 2%; P = 0.86] and dropout [1%, 95%CI: -2% to 4%; P = 0.51]. However, a numerically higher risk of dizziness was noted among the IF alone subgroup with non-early time restricted eating [3%, 95%CI: -0% to 6%; P = 0.08]. CONCLUSIONS: This meta-analysis suggested that IF was not associated with a greater risk of AEs in adult patients affected by overweight or obesity. Additional large-scale RCTs stratified by key confounders and designed to evaluate the long-term effects of various IF regimens are needed to ascertain these AEs profile.

Multimodal reasoning based on knowledge graph embedding for specific diseases.

MOTIVATION: Knowledge Graph (KG) is becoming increasingly important in the biomedical field. Deriving new and reliable knowledge from existing knowledge by KG embedding technology is a cutting-edge method. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, few works based on the existing biomedical KGs are focused on specific diseases. RESULTS: This work develops a construction and multimodal reasoning process of Specific Disease Knowledge Graphs (SDKGs). We construct SDKG-11, a SDKG set including five cancers, six non-cancer diseases, a combined Cancer5 and a combined Diseases11, aiming to discover new reliable knowledge and provide universal pre-trained knowledge for that specific disease field. SDKG-11 is obtained through original triplet extraction, standard entity set construction, entity linking and relation linking. We implement multimodal reasoning by reverse-hyperplane projection for SDKGs based on structure, category and description embeddings. Multimodal reasoning improves pre-existing models on all SDKGs using entity prediction task as the evaluation protocol. We verify the model's reliability in discovering new knowledge by manually proofreading predicted drug-gene, gene-disease and disease-drug pairs. Using embedding results as initialization parameters for the biomolecular interaction classification, we demonstrate the universality of embedding models. AVAILABILITY AND IMPLEMENTATION: The constructed SDKG-11 and the implementation by TensorFlow are available from https://github.com/ZhuChaoY/SDKG-11. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Polarization-sensitive narrowband infrared photodetection triggered by optical Tamm state engineering.

Polarization-sensitive narrowband photodetection at near-infrared (NIR) has attracted significant interest in optical communication, environmental monitoring, and intelligent recognition system. However, the current narrowband spectroscopy heavily relies on the extra filter or bulk spectrometer, which deviates from the miniaturization of on-chip integration. Recently, topological phenomena, such as the optical Tamm state (OTS), provided a new solution for developing functional photodetection, and we experimentally realized the device based on 2D material (graphene) for the first time to the best of our knowledge. Here, we demonstrate polarization-sensitive narrowband infrared photodetection in OTS coupled graphene devices, which are designed with the aid of the finite-difference time-domain (FDTD) method. The devices show narrowband response at NIR wavelengths empowered by the tunable Tamm state. The full width at half maximum (FWHM) of the response peak reaches ∼100 nm, and it can potentially be improved to ultra-narrow of about 10 nm by increasing the periods of dielectric distributed Bragg reflector (DBR). The responsivity and response time of the device reaches 187 mA/W and ∼290 µs at 1550 nm, respectively. Furthermore, the prominent anisotropic features and high dichroic ratios of ∼4.6 at 1300 nm and ∼2.5 at 1500 nm are achieved by integrating gold metasurfaces.

High-performance nanofiltration concentrate treatment by a five-chamber bioelectrochemical system.

A combination of bioelectrochemical systems and electrodialysis has been considered an effective strategy for removing salts from the nanofiltration (NF) concentrate of electroplating wastewater; however, the recovery efficiency of multivalent metals is low. Herein, a new process based on microbial electrolysis desalination and chemical-production cell with five chambers (MEDCC-FC) has been proposed for the simultaneous desalination and recovery of the multivalent metals from NF concentrate. The MEDCC-FC was found to be significantly superior to the MEDCC with the monovalent selective cation exchange membrane (MEDCC-MSCEM) and MEDCC with the cation exchange membrane (MEDCC-CEM), in terms of the elevated desalination efficiency, multivalent metal recovery efficiency, current density, and coulombic efficiency, and decreased energy consumption and membrane fouling. Within 12 h, the MEDCC-FC provided the desirable outcome, indicated by a maximum current density of 6.88 ± 0.06 A/m2, desalination efficiency of 88 ± 10%, metals recovery efficiency of >58%, and total energy consumption of 1.17 ± 0.11 kWh for the per kg total dissolved solids removal. Mechanistic studies revealed that the integration of CEM and MSCEM in the MEDCC-FC promoted the separation and recovery of multivalent metal. These findings revealed that the proposed MEDCC-FC was promising in treating NF concentrate of electroplating wastewater towards advantages of effectiveness, economic viability, and flexibility.

Large-area long-wave infrared broadband all-dielectric metasurface absorber based on markless laser direct writing lithography.

Scalable and low-cost manufacturing of broadband absorbers for use in the long-wave infrared region are of enormous importance in various applications, such as infrared thermal imaging, radiative cooling, thermal photovoltaics and infrared sensor. In recent years, a plethora of broadband absorption metasurfaces made of metal nano-resonators with plasmon resonance have been synthesized. Still, their disadvantages in terms of complex structure, production equipment, and fabrication throughput, limit their future commercial applications. Here, we propose and experimentally demonstrate a broadband large-area all-dielectric metasurface absorber comprised of silicon (Si) arrys of square resonators and a silicon nitride (Si3N4) film in the long-wave infrared region. The multiple Mie resonance modes generated in a single-size Si resonator are utilized to enhance the absorption of the Si3N4 film to achieve broadband absorption. At the same time, the transversal optical (TO) phonon resonance of Si3N4 and the Si resonator's magnetic dipole resonance are coupled to achieve a resonator size-insensitive absorption peak. The metasurface absorber prepared by using maskless laser direct writing technology displays an average absorption of 90.36% and a peak absorption of 97.55% in the infrared region of 8 to 14 µm, and still maintains an average absorption of 88.27% at a inciedent angle of 40°. The experimentally prepared 2 cm × 3 cm patterned metasurface absorber by markless laser direct writing lithography (MLDWL) exhibits spatially selective absorption and the thermal imaging of the sample shows that the maximum temperature difference of 17.3 °C can exist at the boundary.

Probing mid-infrared surface interface states based on thermal emission.

Probing mid-infrared surface wave radiation remains a big challenge for a long time. The lack of convenient and quick mid-infrared surface wave radiation probing methods limits the development of the integrated mid-infrared materials and devices. In this work, we propose a scheme to construct and probe the mid-infrared surface wave radiation of interface state in the waveguide through thermal emission. A superlattice composed of alternately placed periodic meta-crystals is designed to construct an array of interfaces to realize the interface states through the transverse electrical waveguide modes with a tolerance in structural parameters. By heating the structure, we employ angular resolved thermal emission spectroscopy to directly and quickly verify the dispersion of mid-infrared interface states, which have specific frequencies, angles, and polarizations. Moreover, we establish a thermal imaging microscopy to probe the local waveguide interface state directly for the first time. This proposed infrared probing method based on thermal emission can be generalized to probe the mid-infrared surface wave in other systems, such as surface plasmon waves in graphene or surface phonon waves in two-dimensional materials in the mid-infrared range.

Second-generation antipsychotics induce cardiotoxicity by disrupting spliceosome signaling: Implications from proteomic and transcriptomic analyses.

Second-generation antipsychotics (SGAs) are first-line drugs that are prescribed for mental disorders in clinic. Severe cardiotoxicity has been widely reported and thus limits their clinical application. This study aimed to identify the common mechanism underlying SGAs-induced cardiotoxicity using dual-omics analyses. Balb/C mice were intraperitoneally injected with two representative SGAs, olanzapine (2.5 mg/kg) and clozapine (25 mg/kg), at clinically comparable doses for 0, 7, 14 and 21 days. Our results showed that both SGAs induced cardiomyocyte degeneration, inflammation infiltration, and cardiac fibrosis, all of which worsened with time. Proteomic analysis revelaed that 22 differentially expressed (DE) proteins overlapped in olanzapine and clozapine-treated hearts. These proteins were significantly enriched in muscle contraction, amino acid metabolism and spliceosomal assembly by GO term analysis and spliceosome signaling was among the top enriched pathways by KEGG analysis. Among the 22 DE proteins, three spliceosome signal proteins were validated in a dynamic detection, and their expression significantly correlated with the extent of SGAs-induced cardiac fibrosis. Following the spliceosome signaling dysregulation, RNA sequencing revealed that alternative splicing events in the mouse hearts were markedly enhanced by SGAs treatments, and the production of vast transcript variants resulted in dysregulation of multiple pathways that are critical for cardiomyocytes adaptation and cardiac remodeling. Pladienolide B, a specific inhibitor of mRNA splicing, successfully corrected SGAs-induced alternative splicing and significantly attenuated the secretion of pro-inflammatory factors and cell deaths induced by SGAs exposure. Our study concluded that the spliceosome signaling was a common pathway driving SGAs cardiotoxicity. Pharmacological inhibition of the spliceosome signaling represents a novel therapeutic strategy against SGAs cardiotoxicity.

Computational and Mass Spectrometry-Based Approach Identify Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in JMJD6.

The jumonji domain-containing protein 6 (JMJD6) gene catalyzes the arginine demethylation and lysine hydroxylation of histone and a growing list of its known substrate molecules, including p53 and U2AF65, suggesting a possible role in mRNA splicing and transcription in cancer progression. Mass spectrometry-based technology offers the opportunity to detect SNP variants accurately and effectively. In our study, we conducted a combined computational and filtration workflow to predict the nonsynonymous single nucleotide polymorphisms (nsSNPs) present in JMJD6, followed by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and validation. The computational approaches SIFT, PolyPhen-2, SNAP, I-Mutant 2.0, PhD-SNP, PANTHER, and SNPS&GO were integrated to screen out the predicted damaging/deleterious nsSNPs. Through the three-dimensional structure of JMJD6, H187R (rs1159480887) was selected as a candidate for validation. The validation experiments showed that the mutation of this nsSNP in JMJD6 obviously affected mRNA splicing or the transcription of downstream genes through the reduced lysyl-hydroxylase activity of its substrates, U2AF65 and p53, further indicating the accuracy of this prediction method. This research provides an effective computational workflow for researchers with an opportunity to select prominent deleterious nsSNPs and, thus, remains promising for examining the dysfunction of proteins.

Characterization and validation of long noncoding RNAs as new candidates in prostate cancer.

BACKGROUND: Long noncoding RNAs (lncRNAs) have been proved to be an important regulator in gene expression. In almost all kinds of cancers, lncRNAs participated in the process of pathogenesis, invasion, and metastasis. Meanwhile, compared with the large amounts of patients, there is rare knowledge about the role of lncRNAs in prostate cancer (PCa). MATERIAL/METHOD: In this study, lncRNA expression profiles of prostate cancer were detected by Agilent microarray chip, 5 pairs of case and control specimens were involved in. Differentially expressed lncRNAs were screened out by volcano plot for constructing lncRNA-miRNA-mRNA central network. Then, the top ten up-regulated and down-regulated lncRNAs were validated by qRT-PCR in another 5 tumor specimens and 7 para-cancerous/benign contrasts. Furthermore, we searched for the survival curve of the top 10 upregulated and downregulated lncRNAs. RESULTS: A total of 817 differentially expressed lncRNAs were filtered out by the criteria of fold change (FC) and t-test p < 0.05. Among them, 422 were upregulated, whereas 395 were downregulated in PCa tissues. Gene ontology and KEGG pathway analyses showed that many lncRNAs were implicated in carcinogenesis. lnc-MYL2-4:1 (FC = 0.00141, p = 0.01909) and NR_125857 (FC = 59.27658, p = 0.00128) had the highest magnitude of change. The subsequent qPCR confirmed the expression of NR_125857 was in accordance with the clinical samples. High expression of PCA3, PCAT14 and AP001610.9 led to high hazard ratio while low expression of RP11-279F6.2 led to high hazard ratio. CONCLUSIONS: Our study detected a relatively novel complicated map of lncRNAs in PCa, which may have the potential to investigate for diagnosis, treatment and follow-up in PCa. Our study revealed the expression of NR_125857 in human PCa tissues was most up-regulated. Further studies are needed to investigate to figure out the mechanisms in PCa.

Complement Inhibitor CRIg/FH Ameliorates Renal Ischemia Reperfusion Injury via Activation of PI3K/AKT Signaling.

Complement activation is involved in the pathogenesis of ischemia reperfusion injury (IRI), which is an inevitable process during kidney transplantation. Therefore, complement-targeted therapeutics hold great potential in protecting the allografts from IRI. We observed universal deposition of C3d and membrane attack complex in human renal allografts with delayed graft function or biopsy-proved rejection, which confirmed the involvement of complement in IRI. Using FB-, C3-, C4-, C5-, C5aR1-, C5aR2-, and C6-deficient mice, we found that all components, except C5aR2 deficiency, significantly alleviated renal IRI to varying degrees. These gene deficiencies reduced local (deposition of C3d and membrane attack complex) and systemic (serum levels of C3a and C5a) complement activation, attenuated pathological damage, suppressed apoptosis, and restored the levels of multiple local cytokines (e.g., reduced IL-1ß, IL-9, and IL-12p40 and increased IL-4, IL-5, IL-10, and IL-13) in various gene-deficient mice, which resulted in the eventual recovery of renal function. In addition, we demonstrated that CRIg/FH, which is a targeted complement inhibitor for the classical and primarily alternative pathways, exerted a robust renoprotective effect that was comparable to gene deficiency using similar mechanisms. Further, we revealed that PI3K/AKT activation, predominantly in glomeruli that was remarkably inhibited by IRI, played an essential role in the CRIg/FH renoprotective effect. The specific PI3K antagonist duvelisib almost completely abrogated AKT phosphorylation, thus abolishing the renoprotective role of CRIg/FH. Our findings suggested that complement activation at multiple stages induced renal IRI, and CRIg/FH and/or PI3K/AKT agonists may hold the potential in ameliorating renal IRI.

Tailoring Hydrocarbon Polymers and All-Hydrocarbon Composites for Circular Economy.

The world population will rapidly grow from 7 to 9 billion by 2050 and this will parallel a surging annual plastics consumption from today's 350 million tons to well beyond 1 billion tons. The switch from a linear economy with its throwaway culture to a circular economy with efficient reuse of waste plastics is therefore mandatory. Hydrocarbon polymers, accounting for more than half the world's plastics production, enable closed-loop recycling and effective product-stewardship systems. High-molar-mass hydrocarbons serve as highly versatile, cost-, resource-, eco- and energy-efficient, durable lightweight materials produced by solvent-free, environmentally benign catalytic olefin polymerization. Nanophase separation and alignment of unentangled hydrocarbon polymers afford 100% recyclable self-reinforcing all-hydrocarbon composites without requiring the addition of either alien fibers or hazardous nanoparticles. Recycling of durable hydrocarbons is far superior to biodegradation. The facile thermal degradation enables liquefaction and quantitative recovery of low molar mass hydrocarbon oil and gas. Teamed up with biomass-to-liquid and carbon dioxide-to-fuel conversions, powered by renewable energy, waste hydrocarbons serve as renewable hydrocarbon feedstocks for the synthesis of high molar mass hydrocarbon materials. Herein, an overview is given on how innovations in catalyst and process technology enable tailoring of advanced recyclable hydrocarbon materials meeting the needs of sustainable development and a circular economy.

Tailoring Hexagonal Gibbsite Single Crystal Nanoplatelets for Ethylene Polymerization and Nanocomposite Formation on MAO-Free Heterogeneous Bis(imino)pyridine Iron(II) Catalyst.

Ultrathin single crystal γ-Al(OH)3 (Gibbsite) nanoplatelets with average thickness <20 nm and length <800 nm, pretreated with trimethylaluminum (TMA), represent highly efficient activators and supports bis(imino)pyridine iron (II) (FeBIP) complex to produce high density polyethylene (HDPE) as well as gibbsite/HDPE nanocomposites in exceptionally high yields. Opposite to both methylaluminoxane (MAO)-activated homogeneous FeBIP catalyst and heterogenous silica-supported single site catalysts, no addition of MAO is required. At low TMA/Fe = 50 molar ratio, the superior catalyst activity (up to 6500 kg mol-1 h-1 bar-1 ) of FeBIP@TMA@Gibbsite is paralleled by controlled polyethylene particle growth without encountering reactor-fouling problems typical for homogeneous catalysts. TMA@Gibbsite is compared with other AlR3 @Gibbsite activators. The Al/Fe molar ratio governs catalyst activity as well as molar mass, molar mass distribution, and thermal properties of polyethylene. Moreover, hexagonal gibbsite nanoplatelets are uniformly dispersed in polyethylene to yield agglomerate-free polyethylene/gibbsite nanocomposites.

Proteome-wide Mapping of Endogenous SUMOylation Sites in Mouse Testis.

SUMOylation is a reversible post-translational modification involved in various critical biological processes. To date, there is limited approach for endogenous wild-type SUMO-modified peptides enrichment and SUMOylation sites identification. In this study, we generated a high-affinity SUMO1 antibody to facilitate the enrichment of endogenous SUMO1-modified peptides from Trypsin/Lys-C protease digestion. Following secondary Glu-C protease digestion, we identified 53 high-confidence SUMO1-modified sites from mouse testis by using high-resolution mass spectrometry. Bioinformatics analyses showed that SUMO1-modified proteins were enriched in transcription regulation and DNA repair. Nab1 was validated to be an authentic SUMOylated protein and Lys479 was identified to be the major SUMOylation site. The SUMOylation of Nab1 enhanced its interaction with HDAC2 and maintained its inhibitory effect on EGR1 transcriptional activity. Therefore, we provided a novel approach to investigating endogenous SUMOylation sites in tissue samples.

Controlling Surface Plasmons Through Covariant Transformation of the Spin-Dependent Geometric Phase Between Curved Metamaterials.

General relativity uses curved space-time to describe accelerating frames. The movement of particles in different curved space-times can be regarded as equivalent physical processes based on the covariant transformation between different frames. In this Letter, we use one-dimensional curved metamaterials to mimic accelerating particles in curved space-times. The different curved shapes of structures are used to mimic different accelerating frames. The different geometric phases along the structure are used to mimic different movements in the frame. Using the covariant principle of general relativity, we can obtain equivalent nanostructures based on space-time transformations, such as the Lorentz transformation and conformal transformation. In this way, many covariant structures can be found that produce the same surface plasmon fields when excited by spin photons. A new kind of accelerating beam, the Rindler beam, is obtained based on the Rindler metric in gravity. Very large effective indices can be obtained in such systems based on geometric-phase gradient. This general covariant design method can be extended to many other optical media.

Histone H3K9 demethylase JMJD1A modulates hepatic stellate cells activation and liver fibrosis by epigenetically regulating peroxisome proliferator-activated receptor γ.

As a central event in liver fibrogenesis, hepatic stellate cell (HSC) transdifferentiation involves loss of regulation by adipogenic transcription factors such as peroxisome proliferator-activated receptor γ; (PPARγ), which is epigenetically silenced during HSC activation. We hypothesized that JMJD1A, an H3K9 demethylase involved in adipogenic metabolism, could regulate PPARγ. In human HSC cell line, rat primary HSCs, and carbontetrachloride-induced mouse liver fibrogenesis model, we down-regulated the expression of JMJD1A using small interfering or short hairpin RNAs, and overexpressed its wild-type and mutant. We analyzed the effects of JMJD1A manipulation on the histone di-methyl-H3K9 (H3k9me2) status of PPARγ gene and the expression of PPARγ and fibrosis markers using chromatin immunoprecipitation, real-time quantitative RT-PCR and Western blot, and also investigated the in vitro and in vivo consequences on liver fibrosis and necrosis by Masson or hematoxylin-eosin staining, respectively. JMJD1A knockdown in HSCs correlated with reinforced H3K9me2 in the PPARγ gene promoter, and its down-regulation in both mRNA and protein led to increased expression of fibrosis markers, which could be consistently rescued by JMJD1A overexpression. Jmjd1a knockdown in situ resulted in significantly increased expression of α-smooth muscle actin (P = 0.005) and Col1a (P = 0.036), strengthened production of collagens (P = 0.028), and remarkably enhanced necrosis (P = 0.007) 4 weeks after treatment. This study suggests JMJD1A as a novel epigenetic regulator that modulates HSC activation and liver fibrosis through targeting PPARγ gene expression.