First principles study of a triazine-based covalent organic framework as a high-capacity anode material for Na/K-ion batteries.

The rational design of high-performance anode materials is crucial for the development of rechargeable Na-ion batteries (NIBs) and K-ion batteries (KIBs). In this study, based on density functional theory (DFT) calculations, we have systematically investigated the possibility of a bilayer triazine-based covalent organic framework (bilayer TCOF) as an anode for NIBs and KIBs. The calculation of the electronic band structure shows that the bilayer TCOF is a direct band gap semiconductor with a band gap of 2.01 eV. After the adsorption of Na/K at the most favorable sites, the bilayer TCOF transitions from a semiconductor to a metal state, guaranteeing good electronic conductivity. The low diffusion barriers of the bilayer TCOF are 0.45 and 0.26 eV, respectively, indicating a fast diffusion rate of Na/K ions. In addition, the bilayer TCOF has a theoretical storage capacity of up to 628 mA h g-1. Finally, it is found that the average voltage of the bilayer TCOF for NIBs and KIBs is 0.53 and 0.48 V, respectively. Based on these results, we can conclude that the bilayer TCOF may be a suitable anode material for NIBs and KIBs.

Mechanistic insights into super-enhancer-driven genes as prognostic signatures in patients with glioblastoma.

BACKGROUND: Glioblastoma (GBM) is one of the most common malignant brain tumors in adults and is characterized by high aggressiveness and rapid progression, poor treatment, high recurrence rate, and poor prognosis. Although super-enhancer (SE)-driven genes haven been recognized as prognostic markers for several cancers, whether it can be served as effective prognostic markers for patients with GBM has not been evaluated. METHODS: We first combined histone modification data with transcriptome data to identify SE-driven genes associated with prognosis in patients with GBM. Second, we developed a SE-driven differentially expressed genes (SEDEGs) risk score prognostic model by univariate Cox analysis, KM survival analysis, multivariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression. Its reliability in predicting was verified by two external data sets. Third, through mutation analysis, immune infiltration, we explored the molecular mechanisms of prognostic genes. Next, Genomics of Drug Sensitivity in Cancer (GDSC) and the Connectivity Map (cMap) database were employed to assess different sensitivities to chemotherapeutic agents and small-molecule drug candidates between high- and low-risk patients. Finally, SEanalysis database was chosen to identify SE-driven transcription factors (TFs) regulating prognostic markers which will reveal a potential SE-driven transcriptional regulatory network. RESULTS: First, we developed a 11-gene risk score prognostic model (NCF2, MTHFS, DUSP6, G6PC3, HOXB2, EN2, DLEU1, LBH, ZEB1-AS1, LINC01265, and AGAP2-AS1) selected from 1,154 SEDEGs, which is not only an independent prognostic factor for patients, but also can effectively predict the survival rate of patients. The model can effectively predict 1-, 2- and 3-year survival of patients and was validated in external Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) datasets. Second, the risk score was positively correlated with the infiltration of regulatory T cell, CD4 memory activated T cell, activated NK cell, neutrophil, resting mast cell, M0 macrophage, and memory B cell. Third, we found that high-risk patients showed higher sensitivity than low-risk patients to both 27 chemotherapeutic agents and 4 small-molecule drug candidates which might benefit further precision therapy for GBM patients. Finally, 13 potential SE-driven TFs imply how SE regulates GBM patient's prognosis. CONCLUSION: The SEDEG risk model not only helps to elucidate the impact of SEs on the course of GBM, but also provides a bright future for prognosis determination and choice of treatment for GBM patients.

Design, synthesis and anticancer evaluation of novel oncolytic peptide-chlorambucil conjugates.

Nitrogen mustards (NMs) are an important class of chemotherapeutic drugs and have been widely employed for the treatment of various cancers. However, due to the high reactivity of nitrogen mustard, most NMs react with proteins and phospholipids within the cell membrane. Therefore, only a very small fraction of NMs can reach the reach nucleus, alkylating and cross-linking DNA. To efficiently penetrate the cell membrane barrier, the hybridization of NMs with a membranolytic agent may be an effective strategy. Herein, the chlorambucil (CLB, a kind of NM) hybrids were first designed by conjugation with membranolytic peptide LTX-315. However, although LTX-315 could help large amounts of CLB penetrate the cytomembrane and enter the cytoplasm, CLB still did not readily reach the nucleus. Our previous work demonstrated that the hybrid peptide NTP-385 obtained by covalent conjugation of rhodamine B with LTX-315 could accumulate in the nucleus. Hence, the NTP-385-CLB conjugate, named FXY-3, was then designed and systematically evaluated both in vitro and in vivo. FXY-3 displayed prominent localization in the cancer cell nucleus and induced severe DNA double-strand breaks (DSBs) to trigger cell apoptosis. Especially, compared with CLB and LTX-315, FXY-3 exhibited significantly increased in vitro cytotoxicity against a panel of cancer cell lines. Moreover, FXY-3 showed superior in vivo anticancer efficiency in the mouse cancer model. Collectively, this study established an effective strategy to increase the anticancer activity and the nuclear accumulation of NMs, which will provide a valuable reference for future nucleus-targeting modification of nitrogen mustards.

The Tumor Stemness Indice mRNAsi can Act as Molecular Typing Tool for Lung Adenocarcinoma.

Due to the high heterogeneity, lung adenocarcinoma (LUAD) cannot be distinguished into precise molecular subtypes, thereby resulting in poor therapeutic effect and low 5-year survival rate clinically. Although the tumor stemness score (mRNAsi) has been shown to accurately characterize the similarity index of cancer stem cells (CSCs), whether mRNAsi can serve as an effective molecular typing tool for LUAD isn't reported to date. In this study, we first demonstrate that mRNAsi is significantly correlated with the prognosis and disease degree of LUAD patients, i.e., the higher the mRNAsi, the worse the prognosis and the higher the disease degree. Second, we identify 449 mRNAsi-related genes based on both weighted gene co-expression network analysis (WGCNA) and univariate regression analysis. Third, our results display that 449 mRNAsi-related genes can accurately distinguish the LUAD patients into two molecular subtypes: ms-H subtype (with high mRNAsi) and ms-L subtype (with low mRNAsi), particularly the ms-H subtype has a worse prognosis. Remarkably, significant differences in clinical characteristics, immune microenvironment, and somatic mutation exist between the two molecular subtypes, which might lead to the poorer prognosis of the ms-H subtype patients than that of the ms-L subtype ones. Finally, we establish a prognostic model containing 8 mRNAsi-related genes, which can effectively predict the survival rate of LUAD patients. Taken together, our work provides the first molecular subtype related to mRNAsi in LUAD, and reveals that these two molecular subtypes, the prognostic model and marker genes may have important clinical value for effectively monitoring and treating LUAD patients.

Delving into the Heterogeneity of Different Breast Cancer Subtypes and the Prognostic Models Utilizing scRNA-Seq and Bulk RNA-Seq.

BACKGROUND: Breast cancer (BC) is the most common malignancy in women with high heterogeneity. The heterogeneity of cancer cells from different BC subtypes has not been thoroughly characterized and there is still no valid biomarker for predicting the prognosis of BC patients in clinical practice. METHODS: Cancer cells were identified by calculating single cell copy number variation using the inferCNV algorithm. SCENIC was utilized to infer gene regulatory networks. CellPhoneDB software was used to analyze the intercellular communications in different cell types. Survival analysis, univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were used to construct subtype specific prognostic models. RESULTS: Triple-negative breast cancer (TNBC) has a higher proportion of cancer cells than subtypes of HER2+ BC and luminal BC, and the specifically upregulated genes of the TNBC subtype are associated with antioxidant and chemical stress resistance. Key transcription factors (TFs) of tumor cells for three subtypes varied, and most of the TF-target genes are specifically upregulated in corresponding BC subtypes. The intercellular communications mediated by different receptor-ligand pairs lead to an inflammatory response with different degrees in the three BC subtypes. We establish a prognostic model containing 10 genes (risk genes: ATP6AP1, RNF139, BASP1, ESR1 and TSKU; protective genes: RPL31, PAK1, STARD10, TFPI2 and SIAH2) for luminal BC, seven genes (risk genes: ACTR6 and C2orf76; protective genes: DIO2, DCXR, NDUFA8, SULT1A2 and AQP3) for HER2+ BC, and seven genes (risk genes: HPGD, CDC42 and PGK1; protective genes: SMYD3, LMO4, FABP7 and PRKRA) for TNBC. Three prognostic models can distinguish high-risk patients from low-risk patients and accurately predict patient prognosis. CONCLUSIONS: Comparative analysis of the three BC subtypes based on cancer cell heterogeneity in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy for BC patients.

mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes.

Background: Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown. Methods: Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc. to identify biomarkers and molecular subtypes related to tumor stemness in HCC. Results: We firstly demonstrate that the high mRNAsi is significantly associated with the poor survival and high disease grades in HCC. Secondly, we identify 212 mRNAsi-related genes that can divide HCC into three molecular subtypes: low cancer stemness cell phenotype (CSCP-L), moderate cancer stemness cell phenotype (CSCP-M) and high cancer stemness cell phenotype (CSCP-H), especially over-activated ribosomes, spliceosomes and nucleotide metabolism lead to the worst prognosis for the CSCP-H subtype patients, while activated amino acids, fatty acids and complement systems result in the best prognosis for the CSCP-L subtype. Thirdly, we find that three CSCP subtypes have different mutation characteristics, immune microenvironment and immune checkpoint expression, which may cause the differential prognosis for three subtypes. Finally, we identify 10 robust mRNAsi-related biomarkers that can effectively predict the survival of HCC patients. Conclusions: These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients.

Automated Chemical Reaction Extraction from Scientific Literature.

Access to structured chemical reaction data is of key importance for chemists in performing bench experiments and in modern applications like computer-aided drug design. Existing reaction databases are generally populated by human curators through manual abstraction from published literature (e.g., patents and journals), which is time consuming and labor intensive, especially with the exponential growth of chemical literature in recent years. In this study, we focus on developing automated methods for extracting reactions from chemical literature. We consider journal publications as the target source of information, which are more comprehensive and better represent the latest developments in chemistry compared to patents; however, they are less formulaic in their descriptions of reactions. To implement the reaction extraction system, we first devised a chemical reaction schema, primarily including a central product, and a set of associated reaction roles such as reactants, catalyst, solvent, and so on. We formulate the task as a structure prediction problem and solve it with a two-stage deep learning framework consisting of product extraction and reaction role labeling. Both models are built upon Transformer-based encoders, which are adaptively pretrained using domain and task-relevant unlabeled data. Our models are shown to be both effective and data efficient, achieving an F1 score of 76.2% in product extraction and 78.7% in role extraction, with only hundreds of annotated reactions.

Hybrid peptide NTP-217 triggers ROS-mediated rapid necrosis in liver cancer cells by induction of mitochondrial leakage.

Liver cancer is the third leading cause of cancer-associated mortality globally, and >830,000 patients with liver cancer undergoing treatment succumbed to the disease in 2020, which indicates the urgent need to develop a more effective anti-liver cancer drug. In our previous study, nucleus-targeting hybrid peptides obtained from the fusion of LTX-315 and the rhodamine B group possessed potent anti-adherent cancer cell activity. Hybrid peptides accumulated in the cell nucleus and damaged the nuclear membrane, resulting in the transfer of reactive oxygen species (ROS) from the cytoplasm to the nucleus and the induction of apoptosis. However, the source of the high concentration of ROS within the cytoplasm is unclear. Moreover, although our previous study demonstrated that hybrid peptides possessed potent anticancer activity against adherent cancer cells, their efficacy on liver cancer remained unexplored. The current study found that the hybrid peptide NTP-217 killed liver cancer cells after 4-h treatment with a half-maximal inhibitory concentration of 14.6-45.7 µM. NTP-217 could stably accumulate in the liver tumor tissue and markedly inhibited liver tumor growth in mice. Furthermore, NTP-217 destroyed mitochondria and induced the leakage of mitochondrial contents, resulting in the generation of a substantial quantity of ROS. Unlike the apoptosis induced by 24 h of treatment by NTP-217, 4 h of treatment caused ROS-mediated necrotic cell death. These findings suggested that short-time treatment with hybrid peptides could trigger ROS-mediated rapid necrosis in liver cancer cells, and provided a basis for the future development of hybrid peptides as anti-liver cancer agents.

Peripheral-specific Y1 receptor antagonism increases thermogenesis and protects against diet-induced obesity.

Obesity is caused by an imbalance between food intake and energy expenditure (EE). Here we identify a conserved pathway that links signalling through peripheral Y1 receptors (Y1R) to the control of EE. Selective antagonism of peripheral Y1R, via the non-brain penetrable antagonist BIBO3304, leads to a significant reduction in body weight gain due to enhanced EE thereby reducing fat mass. Specifically thermogenesis in brown adipose tissue (BAT) due to elevated UCP1 is enhanced accompanied by extensive browning of white adipose tissue both in mice and humans. Importantly, selective ablation of Y1R from adipocytes protects against diet-induced obesity. Furthermore, peripheral specific Y1R antagonism also improves glucose homeostasis mainly driven by dynamic changes in Akt activity in BAT. Together, these data suggest that selective peripheral only Y1R antagonism via BIBO3304, or a functional analogue, could be developed as a safer and more effective treatment option to mitigate diet-induced obesity.

Direct Optimization across Computer-Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries.

The synthesis of thousands of candidate compounds in drug discovery and development offers opportunities for computer-aided synthesis planning to simplify the synthesis of molecule libraries by leveraging common starting materials and reaction conditions. We develop an optimization-based method to analyze large organic chemical reaction networks and design overlapping synthesis plans for entire molecule libraries so as to minimize the overall number of unique chemical compounds needed as either starting materials or reaction conditions. We consider multiple objectives, including the number of starting materials, the number of catalysts/solvents/reagents, and the likelihood of success of the overall syntheses plan, to select an optimal reaction network to access the target molecules. The library synthesis planning task was formulated as a network flow optimization problem, and we design an efficient decomposition scheme that reduces solution time by a factor of 5 and scales to instance with 48 target molecules and nearly 8000 intermediate reactions within hours. In four case studies of pharmaceutical compounds, the approach reduces the number of starting materials and catalysts/solvents/reagents needed by 32.2 and 66.0% on average and up to 63.2 and 80.0% in the best cases. The code implementation can be found at https://github.com/Coughy1991/Molecule_library_synthesis.

Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning.

Computer aided synthesis planning of synthetic pathways with green process conditions has become of increasing importance in organic chemistry, but the large search space inherent in synthesis planning and the difficulty in predicting reaction conditions make it a significant challenge. We introduce a new Monte Carlo Tree Search (MCTS) variant that promotes balance between exploration and exploitation across the synthesis space. Together with a value network trained from reinforcement learning and a solvent-prediction neural network, our algorithm is comparable to the best MCTS variant (PUCT, similar to Google's Alpha Go) in finding valid synthesis pathways within a fixed searching time, and superior in identifying shorter routes with greener solvents under the same search conditions. In addition, with the same root compound visit count, our algorithm outperforms the PUCT MCTS by 16% in terms of determining successful routes. Overall the success rate is improved by 19.7% compared to the upper confidence bound applied to trees (UCT) MCTS method. Moreover, we improve 71.4% of the routes proposed by the PUCT MCTS variant in pathway length and choices of green solvents. The approach generally enables including Green Chemistry considerations in computer aided synthesis planning with potential applications in process development for fine chemicals or pharmaceuticals.

A robotic platform for flow synthesis of organic compounds informed by AI planning.

The synthesis of complex organic molecules requires several stages, from ideation to execution, that require time and effort investment from expert chemists. Here, we report a step toward a paradigm of chemical synthesis that relieves chemists from routine tasks, combining artificial intelligence-driven synthesis planning and a robotically controlled experimental platform. Synthetic routes are proposed through generalization of millions of published chemical reactions and validated in silico to maximize their likelihood of success. Additional implementation details are determined by expert chemists and recorded in reusable recipe files, which are executed by a modular continuous-flow platform that is automatically reconfigured by a robotic arm to set up the required unit operations and carry out the reaction. This strategy for computer-augmented chemical synthesis is demonstrated for 15 drug or drug-like substances.

Using Machine Learning To Predict Suitable Conditions for Organic Reactions.

Reaction condition recommendation is an essential element for the realization of computer-assisted synthetic planning. Accurate suggestions of reaction conditions are required for experimental validation and can have a significant effect on the success or failure of an attempted transformation. However, de novo condition recommendation remains a challenging and under-explored problem and relies heavily on chemists' knowledge and experience. In this work, we develop a neural-network model to predict the chemical context (catalyst(s), solvent(s), reagent(s)), as well as the temperature most suitable for any particular organic reaction. Trained on ∼10 million examples from Reaxys, the model is able to propose conditions where a close match to the recorded catalyst, solvent, and reagent is found within the top-10 predictions 69.6% of the time, with top-10 accuracies for individual species reaching 80-90%. Temperature is accurately predicted within ±20 °C from the recorded temperature in 60-70% of test cases, with higher accuracy for cases with correct chemical context predictions. The utility of the model is illustrated through several examples spanning a range of common reaction classes. We also demonstrate that the model implicitly learns a continuous numerical embedding of solvent and reagent species that captures their functional similarity.

[Seasonal Variation and Source Analysis of Water-soluble Inorganic Salts in PM2.5 in the Southern Suburbs of Beijing].

In order to study the variation of water-soluble inorganic ions in the four suburbs of Beijing using the atmospheric fine particulate matter rapid trapping system and chemical composition analysis system (RCFP-IC), we carried out measurements for nine water-soluble inorganic ions (Cl-, NO2-, NO3-, SO42-, Na+, NH4+, K+, Mg2+, Ca2+) in PM2.5 with continuous on-line observations for one year in Beijing's southern suburbs in 2016. The transport process of pollutants and the potential sources of pollutants were evaluated by combining a trajectory clustering method and potential source contribution factor analysis method (PSCF). During the observation period, the total concentration of the nine water-soluble inorganic ions was 38.6 µg ·m-3, and results showed that the concentration in winter and spring was high and in summer and autumn was low. The order of the concentration from high to low was SO42- > NO3- > NH4+ > Ca2+ > NO2- > Cl- > Na+ > K+ > Mg2+. In winter, the SO42-, NO3- and NH4+ accounted for 75.7% of the total measured water-soluble ions, followed by 72.8% in spring and 60.2% in summer. With an increase in air pollution, the concentrations of SO42-, NO3-, and NH4+ increased significantly, indicating that SO42-, NO3-, and NH4+ were closely related to the deterioration of air quality. SO42- was dominant in the formation of secondary ions compared to NO3- and NH4+; and SO42-, NO3-, and NH4+ had significant diurnal variations. The diurnal variation of the SO42- statistic (hours) was bimodal, and the peak values were at about 10:00 and 18:00. The diurnal variation of NO3- and NH4+ had single peaks, with the peak appearing at 10:00. The trend of the diurnal variation for these two ions was similar. Finally, the sources of pollution in the southern suburbs of Beijing mainly included secondary sources, coal-fired sources, and mixed sources of dust and dust. The main potential source of pollution in the southern suburbs was in the southeastern part of the observation site, while the northeastern airflow was favorable for the diffusion and dilution of pollutants.