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At present, most patients with oral squamous cell carcinoma (OSCC) are in the middle or advanced stages at the time of diagnosis. Advanced OSCC patients have a poor prognosis after traditional therapy, and the complex heterogeneity of OSCC has been proven to be one of the main reasons. Single-cell sequencing technology provides a powerful tool for dissecting the heterogeneity of cancer. However, most of the current studies at the single-cell level are static, while the development of cancer is a dynamic process. Thus, understanding the development of cancer from a dynamic perspective and formulating corresponding therapeutic measures for achieving precise treatment are highly necessary, and this is also one of the main study directions in the field of oncology. In this study, we combined the static and dynamic analysis methods based on single-cell RNA-Seq data to comprehensively dissect the complex heterogeneity and evolutionary process of OSCC. Subsequently, for clinical practice, we revealed the association between cancer heterogeneity and the prognosis of patients. More importantly, we pioneered the concept of pseudo-time score of patients, and we quantified the levels of heterogeneity based on the dynamic development process to evaluate the relationship between the score and the survival status at the same stage, finding that it is closely related to the prognostic status. The pseudo-time score of patients could not only reflect the tumor status of patients but also be used as an indicator of the effects of drugs on the patients so that the medication strategy can be adjusted on time. Finally, we identified candidate drugs and proposed precision medication strategies to control the condition of OSCC in two respects: treatment and blocking.

Identifying drug phenotypiceffects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug candidates (NDCs). However, current computational methods for predicting phenotypiceffects of NDCs are mainly based on the overall structure of an NDC or a related target. These approaches often lead to inconsistencies between the structures and functions and limit the prediction space of NDCs. In this study, first, we constructed quantitative associations of substructure-domain, domain-ADR, and domain-ATC through supervised learnings. Then, based on these established associations, substructure-phenotype relationships were constructed which were utilized to quantifying drug-phenotype relationships. Thus, this approach could achieve high-throughput and effective evaluations of the druggability of NDCs by referring to the established substructure-phenotype relationships and structural information of NDCs without additional prior knowledge. In a word, this approach through establishing drug-substructure-phenotype relationships can achieve quantitative prediction of phenotypes for a given NDC or drug without any prior knowledge except its structure information. The way can directly obtain the relationships between substructure and phenotype of a compound, which is more convenient to analyze the phenotypic mechanism of drugs and accelerate the process of rational drug design.

The drivers of variations in leaf nutrient concentrations in cave-dwelling plants remain poorly understood. We aimed to explore the effects of light, soil chemistry and phylogeny on leaf nutrient concentrations in cave-dwelling plants. We quantified light availability and sampled top-soils and leaves of the co-existing herbs and ferns in three caves. We used the traditional and phylogenetic comparative methods to determine the effects of light, soil chemistry and phylogeny on leaf nutrient concentrations and the cross-species correlations between leaf nutrients. Leaf nutrient concentrations differed little among caves due to the non-significant relationships of leaf nutrient concentrations with light availability and soil nutrient concentrations across caves. The phylogenetic signals in leaf nutrient concentrations were significant for Ca, Mg and N but non-significant for the remaining nutrients. The evolutionary rates of leaf nutrient concentrations tended to increase with decreasing phylogenetic signals and were faster in herbs than ferns. These contrasting degrees of phylogenetic conservatism in leaf nutrient concentrations were best generated by Ornstein-Uhlenbeck models, i.e., stabilizing selection towards an optimum across species for P, K, S, Fe, Mn and Zn or higher optimal concentrations in herbs than ferns for Ca, Mg and N. Strong cross-species correlations between leaf nutrient concentrations such as Ca vs Mg and N vs P were found. Leaf nutrient concentrations in cave-dwelling plants showed convergent adaptations to cave environments and presented contrasting degrees of phylogenetic conservatism to produce leaf nutritional diversity for the co-existing herbs and ferns in caves.

Antibiotic tetracycline (TC) is a sort of main contaminates in water, and of adverse effect on ecosystems and human health. The development of simple and efficient methods for both detection and removal of TC in water is highly desirable but remains challenging. Herein, a dual-functional platform for detection and removal of antibiotic tetracycline (TC) is developed by a highly stable luminescent zirconium-based MOF (PCN-128Y). The detection is based on the efficient luminescence quenching of the PCN-128Y toward TC. Theoretical/experimental studies reveal that the luminescence quenching can be attributed to a combined effect of the strong absorption of TC at the excitation wavelength and the photo-induced electron transfer process from the ligand of PCN-128Y to TC. The strong cheating metal-ligand bonding between Zr6 nodes and TC through solvent-assisted ligand incorporation is suggested to mainly account for the high adsorption capability of PCN-128Y toward TC in water. The preconcentration of TC within the pores of PCN-128Y induced by the adsorption process makes TC contact with the framework more sufficient, thus significantly enhances the efficiency of TC sensing. This work is the first example demonstrating that MOF materials can integrate the functions of detection and removal of antibiotic TC in water, which highlights the opportunity of luminescent MOFs in the application of wastewater treatment.

Hepatocellular carcinoma (HCC) is the most frequent type of liver cancer with poor survival rate and high mortality. Despite efforts on the mechanism of HCC, new molecular markers are needed for exact diagnosis, evaluation and treatment. Here, we combined transcriptome of HCC with networks and pathways to identify reliable molecular markers. Through integrating 249 differentially expressed genes with syncretic protein interaction networks, we constructed a HCC-specific network, from which we further extracted 480 pivotal genes. Based on the cross-talk between the enriched pathways of the pivotal genes, we finally identified a HCC signature of 45 genes, which could accurately distinguish HCC patients with normal individuals and reveal the prognosis of HCC patients. Among these 45 genes, 15 showed dysregulated expression patterns and a part have been reported to be associated with HCC and/or other cancers. These findings suggested that our identified 45 gene signature could be potential and valuable molecular markers for diagnosis and evaluation of HCC.

Dipicolinic acid (DPA) can serve as a convenient biomarker for Bacillus anthracis, which is an extremely hazardous pathogen and can be used as a biological weapon. Herein, we demonstrate a ratiometric luminescent sensor of DPA based on a dual-emissive MOF hybrid, which is developed by encapsulation of Tb3+ cations into an anionic MOF through a cation exchange process. The Tb3+@MOF hybrid well inherits the intrinsic ligand emission of the host framework and the Tb3+ emission. The parent framework can function as a host matrix to sensitize and protect the emission of incorporated Tb3+. The Tb3+ emission within the MOF hybrid is significantly enhanced in the presence of DPA molecules owing to the occurrence of antenna sensitization upon the formation of the Tb–DPA complex, while the ligand emission is insensitive to DPA. This unique ratiometric luminescence response of the Tb3+@MOF hybrid towards DPA can be exploited for sensitive self-calibrated detection of DPA. The Tb3+@MOF sensor shows a fast response rate, high sensitivity and selectivity with a limit of detection of 3.6 nM. Besides, the feasibility of this sensor operating in real samples is demonstrated by the good recovery of DPA in human serum. The present study provides a promising luminescent platform for routine analysis of an anthrax biomarker.

Confining metal-halide perovskites within MOF hosts not only protects them from exterior environment stimulation and isolates them from each other without aggregation, but also enables a variety of new features and applications. In this work, a hierarchically porous MOF host was firstly explored for in-situ confined growth of CsPbX3 perovskite nanocrystallites (PNCs). Compared with the previously used microporous MOF matrixes, this hierarchically porous MOF not only enables the impregnation of perovskite precursors in one step, but also allows more facile diffusion of the reactants, thus giving rise to high loading capacities and uniform distribution of CsPbX3 perovskites. The MOF matrix serves as both template to guide the confined growth of monodispersed CsPbX3 PNCs and encapsulation to enhance the stability of CsPbX3 PNCs. The obtained CsPbX3@MOF nanocomposites show bright luminescence and good resistance to ambient moisture, UV light irradiation, and anion-exchange. By integrating the green-emitting CsPbBr2I@MOF and red-emitting CsPbI3@MOF nanocomposites with a commercial blue InGaN LED chip, we fabricate a primary WLED showing a NTSC value of 125% and Rec. 2020 of 93%, which highlights the potential in backlight display.

// Hongbo Xie 1, * , Diheng Zeng 1, * , Xiujie Chen 1, * , Diwei Huo 2, * , Lei Liu 1 , Denan Zhang 1 , Qing Jin 1 , Kehui Ke 1 and Ming Hu 1 1 Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, PR China 2 The 2nd Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, PR China * These authors contributed equally to this work Correspondence to: Xiujie Chen, email: chenxiujie@ems.hrbmu.edu.cn Denan Zhang, email: zhangdenan@ems.hrbmu.edu.cn Keywords: drug-induced liver injury, personalized medicine, homology-modeling, molecular simulation, risk population prediction Received: July 06, 2017 Accepted: September 20, 2017 Published: October 05, 2017 ABSTRACT Idiosyncratic adverse drug reactions are drug reactions that occur rarely and unpredictably among the population. These reactions often occur after a drug is marketed, which means that they are strongly related to the genotype of the population. The prediction of such adverse reactions is a major challenge because of the lack of appropriate test models during the drug development process. In this study, we chose withdrawn drugs because the reasons why they were withdrawn and from which countries or regions is easily obtained. We selected Dilevalol and its chiral drug (Labetalol) as the investigatory drugs, as they have been withdrawn from a European market (Britain) because of serious hepatotoxicity. First, we searched for and obtained the Dilevalol-induced- liver-injury related protein, multidrug resistance protein 1 (MDR1), from the Comparative Toxicogenomics Database (CTD). Then, we searched and extracted 477 non-synonymous single nucleotide polymorphisms (nsSNP) on MDR1 in the dbSNP database. Second, we used the VarMod tool to predict the functional changes of MDR1 induced by these nsSNPs, from which we extracted the nsSNPs that significantly change the functions of this protein. Third, we built the three-dimensional structures of those variant proteins and used AutoDock to perform a docking study, choosing the best model to determine the sites of nsSNPs. Finally, we used the data from the 1000 Genomes Project to verify the dominant population distribution of the risk SNP. We applied the same strategy to the post-marketing drug-induced liver injury drugs to further test the feasibility of our method.

Background The analysis of cancer diversity based on a logical framework of hallmarks has greatly improved our understanding of the occurrence, development and metastasis of various cancers. Methods We designed Cancer Hallmark Genes (CHG) database which focuses on integrating hallmark genes in a systematic, standard way and annotates the potential roles of the hallmark genes in cancer processes. Following the conceptual criteria description of hallmark function the keywords for each hallmark were manually selected from the literature. Candidate hallmark genes collected were derived from 301 pathways of KEGG database by Lucene and manually corrected. Results Based on the variation data, we finally identified the hallmark genes of various types of cancer and constructed CHG. And we also analyzed the relationships among hallmarks and potential characteristics and relationships of hallmark genes based on the topological structures of their networks. We manually confirm the hallmark gene identified by CHG based on literature and database. We also predicted the prognosis of breast cancer, glioblastoma multiforme and kidney papillary cell carcinoma patients based on CHG data. Conclusions In summary, CHG, which was constructed based on a hallmark feature set, provides a new perspective for analyzing the diversity and development of cancers.

An antibody free and signal-on type electrochemiluminescence(ECL) sensor was developed for diethylstilbestrol (DES) detection using magnetic surface magnetic molecular imprinting polymers (MMIPs) - aptamer labeled CdS quantum dots (CdS QDs) conjugated probes. Firstly, the MMIPs were synthesized through employing 4″-hydroxypropiophenone as mimicking template to form imprint sites on the poly-dopamine coating covering core-shell Fe3O4@SiO2 (MMIPs). Secondly, the aptamer which epitope is phenol group of 17β-estradiol (E2), was chosen to label on CdS QDs to form CdS-Apt signal tag and recognize phenol group of DES. When the target, MMIPs and CdS-Apt was incubated together, a sandwich MMIPs-DES-CdS-Apt composite was constructed. It was then adsorbed on the interface of a screen printed carbon electrode (SPCE) by external magnetic field, then emit electrochemical luminescence signal at potential − 1.1 V. The signal intensity was in proportion to the logarithm of DES' concentration from 0.3 to 1.0 × 105 pg ml− 1, with the detection limit (LOD) of 0.1 pg ml− 1 (S/N = 3). Several fish samples were tested by the sensor which showed high selectivity and good recoveries between 80% and 120% with consistent results as that of conventional ELISA. Since there have been no reports of aptamer for DES, we use E2 aptamer to recognize phenol epitope of DES, then MMIPs towards another epitope to form a sandwich complex. Thus a signal-on and sandwich sensor was fabricated. Moreover, no expensive antibody was employed for fabricating the sensor. Its selectivity and sensitivity were higher than the same type of sensor based on sole aptamer or MIPs probe. The assay can be explored to detect other analytes while changing the corresponding aptamer and imprinting template.

// Xiujie Chen 1, * , Lei Liu 1, * , YunFeng Wang 1, * , Bo Liu 1, * , Diheng Zeng 1 , Qing Jin 1 , MengJian Li 1 , DeNan Zhang 1 , Qiuqi Liu 1 , Hongbo Xie 1 1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China * These authors contributed equally to this work Correspondence to: Xiujie Chen, email: chenxiujie@ems.hrbmu.edu.cn Keywords: breast cancer, normal tissue, tumor tissue, recurrence, risk factors Received: September 30, 2015 Accepted: July 26, 2016 Published: August 23, 2016 ABSTRACT The recurrence of breast cancer (BC) is a serious therapeutic problem, and the risk factors for recurrence urgently need to be identified. In this study, we examined the functional pathways in tumor and normal tissues to more comprehensively identify biomarkers for the risk of BC recurrence. We collected tumor and normal tissue gene expression profiles of recurrent BC patients and non-recurrent BC patients from the TCGA database.We derived an expression interval (mean ± 1.96SD) based on non-recurrent patients rather than a single value, such as a mean or median. If the expression of a gene was significantly different from its normal expression interval, it was considered a differentially expressed gene. Eight pathways that significantly distinguished recurrent and non-recurrent BC patients were obtained based on 65% accuracy, and these pathways were all associated with the immune response and sensitivity to drugs. The genes in these eight pathways were also used to analyze survival, and the significance level reached 0.003 in an independent dataset ( p = 0.02 in tumor and p = 0.03 in normal tissue). Our results reveal that the integration of tumor and normal tissue functional analyses can comprehensively enhance the understanding of BC prognosis.

The development of simple, rapid-response sensors for water detection in organic solvents is highly desirable in the chemical industry. Here we demonstrate a unique luminescence water sensor based on a dual-emitting europium-organic framework (Eu-MOF), which is assembled from a purposely selected 2-aminoterephthalic acid ligand with responsive fluorescence inherent in its intramolecular charge transfer (ICT) process. This ICT process can be rapidly switched-on in the presence of water owing to its ability to boost and stabilize the ICT state. In contrast, the Eu3+ emission within the framework is insensitive to water and can serve as a reference, thus enabling highly sensitive water detection in a turn-on and ratiometric way. In addition, the significant ratiometric luminescence response induced by water makes Eu-MOF undergo a distinct change of emitting color from red to blue, which is favorable for visual analysis with the naked eye. Sensitive determination of water content (0.05-10% v/v) in various organic solvents is achieved in multiple readouts including ratiometric emission intensity, emission color, or the Commission Internationale de l'Eclairage (CIE) chromaticity coordinate. The present Eu-MOF sensor featuring high sensitivity and reusability, self-calibration, simple fabrication and operation, and capability for real-time and in situ detection is expected to have practical applications in water analysis for industrial processes.

Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-based and function-based screening) was proposed to identify the SL gene pairs by mining 697 cancer genes and the human signaling network, which had 6306 proteins and 62937 protein-protein interactions. The network-based screening was composed of a stability score constructed using a network information centrality measure (the average shortest path length) and the distance-based screening between the cancer gene and the non-cancer gene. Then, the non-cancer genes were extracted and annotated using drug-target interaction and drug description information to obtain potential anticancer drug targets. Finally, the human SL data in SynLethDB, the existing drug sensitivity data and text-mining were utilized for target validation. We successfully identified 2555 SL gene pairs and 57 potential anticancer drug targets. Among them, CDK1, CDK2, PLK1 and WEE1 were verified by all three aspects and could be preferentially used in specific targeted therapy in the future.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder that affects the elderly. Despite the devastating effects of this disease, therapeutic methods for the treatment of AD remain limited, as many factors contribute to its etiology. In recent decades, many small molecules and bioproducts have been developed for the treatment of AD; however, there are still no drugs that produce efficient disease-modifying effects. Drug repositioning is an alternative methodology for drug discovery because many effective drugs act via the modulation of multiple targets, and this ‘poly-pharmacology’ can be a therapeutic requirement for complex diseases. The multifactorial nature of AD strongly supports the rationale for a drug design strategy centered on drug repositioning. In this study, a computational pipeline based on ligand–protein inverse docking and gene expression data mining were adopted to explore potential drugs for the treatment of AD. We performed a virtual screening of 1553 FDA-approved drugs on seven major AD drug targets. Several drugs showed extremely high binding free energies for the seven targets. Furthermore, gene expression data represent a very rich alternative resource for inferring non-obvious relationships between drugs and genes. The gene expression profiles in response to the identified drugs support the docking results and offer a plausible perspective on the pathways associated with protein responses to drug binding in AD patients. The results have led to the discovery of droperidol, glimepiride, risperidone and other FDA-approved drugs as potential multi-target candidates for the treatment of AD.

Luminescent MOF materials with tunable emissions and energy/charge transfer processes have been extensively explored as ratiometric temperature sensors. However, most of the ratiometric MOF thermometers reported thus far are based on the MOFs containing photoactive lanthanides, which are potentially facing cost issue and serious supply shortage. Here, we present a ratiometric luminescent thermometer based on a dual-emitting lanthanide-free MOF hybrid, which is developed by encapsulation of a fluorescent dye into a robust nanocrystalline zirconium-based MOF through a one-pot synthesis approach. The structure and morphology of the hybrid product was characterized by Powder X-ray diffraction (PXRD), N2 adsorption-desorption measurement and Scanning electron microscopy (SEM). The pore confinement effect well isolates the guest dye molecules and therefore suppresses the nonradiative energy transfer process between dye molecules. The incorporated dye emission is mainly sensitized by the organic linkers within MOF through fluorescence resonance energy transfer. The ratiometric luminescence of the MOF hybrid shows a significant response to temperature due to the thermal-related back energy transfer process from dye molecules and organic linkers, thus can be exploited for self-calibrated temperature sensing. The maximum thermometric sensitivity is 1.19% °C-1 in the physiological temperature range, which is among the highest for the ratiomtric MOF thermometers that operating in 25-45°C. The temperature resolution is better than 0.1°C over the entire operative range (20-60°C). By integrating the advantages of excellent stability, nanoscale nature, and high sensitivity and precision in the physiological temperature range, this dye@MOF hybrid might have potential application in biomedical diagnosis. What' more, this work has expanded the possibility of non-lanthanide luminescent MOF materials for the development of ratiometric temperature sensors.

Glycogen synthase kinase-3 beta (GSK-3β) is involved in multiple signaling pathways. Consistent with its critical roles in normal cells, abnormalities in GSK-3β activity have been implicated in diabetes, heart disease, Parkinson disease and Alzheimer's disease. In this study, a series of new scaffolds of small molecule inhibitors of GSK-3β were identified by virtual screening and bioassay. Candidates that adhere to drug-like criteria from a virtual library of compounds were tested using computational docking studies. Twenty selected compounds were tested, which led to the discovery of two hits. Compound 14 (IC50 = 8.48 μM) and compound 19 (IC50 = 2.19 μM) were identified with high affinity. Molecular dynamics (MD) simulations, in conjunction with molecular mechanics/Poisson-Boltzmann surface area binding free-energy analysis, were employed to gain insight into the binding modes and energetics of GSK-3β inhibitors. The detailed analysis of molecular dynamics results shows that Ile62, Val70, Tyr134 and Leu188 in GSK-3β are key residues responsible to the binding of compound 14 and compound 19. Importantly, our results also validated this combined virtual screening and biophysical technique approach to discovery kinase inhibitors, which may be applied for future inhibitor discovery work for GSK-3β. This article is protected by copyright. All rights reserved

Background Although endocrine therapy impedes estrogen-ER signaling pathway and thus reduces breast cancer mortality, patients remain at continued risk of relapse after tamoxifen or other endocrine therapies. Understanding the mechanisms of endocrine resistance, particularly the role of transcriptional regulation is very important and necessary. Methods We propose a two-step workflow based on linear model to investigate the significant differences between MCF7 and OHT cells stimulated by 17β-estradiol (E2) respect to regulatory transcription factors (TFs) and their interactions. We additionally compared predicted regulatory TFs based on RNA polymerase II (PolII) binding quantity data and gene expression data, which were taken from MCF7/MCF7+E2 and OHT/OHT+E2 cell lines following the same analysis workflow. Enrichment analysis concerning diseases and cell functions and regulatory pattern analysis of different motifs of the same TF also were performed. Results The results showed PolII data could provide more information and predict more recognizably important regulatory TFs. Large differences in TF regulatory mode were found between two cell lines. Through verified through GO annotation, enrichment analysis and related literature regarding these TFs, we found some regulatory TFs such as AP-1, C/EBP, FoxA1, GATA1, Oct-1 and NF-κB, maintained OHT cells through molecular interactions or signaling pathways that were different from the surviving MCF7 cells. From TF regulatory interaction network, we identified E2F, E2F-1 and AP-2 as hub-TFs in MCF7 cells; whereas, in addition to E2F and E2F-1, we identified C/EBP and Oct-1 as hub-TFs in OHT cells. Notably, we found the regulatory patterns of different motifs of the same TF were very different from one another sometimes. Conclusions We inferred some regulatory TFs, such as AP-1 and NF-κB, cooperated with ER through both genomic action and non-genomic action. The TFs that were involved in both protein-protein interactions and signaling pathways could be one of the key resistant mechanisms of endocrine therapy and thus also could be new treatment targets for endocrine resistance. Our flexible workflow could be integrated into an existing analytical framework and guide biologists to further determine underlying mechanisms in human diseases.