Hyaluronic acid/dextran-based polymeric micelles co-delivering ursolic acid and doxorubicin to mitochondria for potentiating chemotherapy in MDR cancer.

Cancer multidrug resistance (MDR) dramatically hindered the efficiency of standard chemotherapy. Mitochondria are highly involved in the occurrence and development of MDR; thus, inducing its malfunction will be an appealing strategy to treat MDR tumors. In this paper, a natural polysaccharides-based nanoplatform (TDTD@UA/HA micelles) with cell and mitochondria dual-targeting ability was facilely fabricated to co-deliver ursolic acid (UA) and doxorubicin (DOX) for combinatorial MDR therapy. TDTD@UA/HA micelles featured a spherical morphology, narrow size distribution (∼140 nm), as well as favorable drug co-loading capacity (DOX: 8.41 %, UA: 9.06 %). After hyaluronic acid (HA)-mediated endocytosis, the lysosomal hyaluronidase promoted the degradation of HA layer and then the positive triphenylphosphine groups were exposed, which significantly enhanced the mitochondria-accumulation of nano micelles. Subsequently, DOX and UA were specifically released into mitochondria under the trigger of endogenous reactive oxygen species (ROS), followed by severe mitochondrial destruction through generating ROS, exhausting mitochondrial membrane potential, and blocking energy supply, etc.; ultimately contributing to the susceptibility restoration of MCF-7/ADR cells to chemotherapeutic agents. Importantly, TDTD@UA/HA micelles performed potent anticancer efficacy without distinct toxicity on the MDR tumor-bearing nude mice model. Overall, the versatile nanomedicine represented a new therapeutic paradigm and held great promise in overcoming MDR-related cancer.

Hemizygous splicing variant in CNKSR2 results in X-linked intellectual developmental disorder.

BACKGROUND: Intellectual disability (ID) refers to a childhood-onset neurodevelopmental disorder with a prevalence of approximately 1%-3%. METHODS: We performed whole exome sequencing for the patient with ID. And the splicing variant we found was validated by minigene assay. RESULTS: Here, we report a boy with ID caused by a variant of CNKSR2. His neurological examination revealed hypsarrhythmia via electroencephalography and a right temporal polar arachnoid cyst via brain magnetic resonance imaging. A novel splicing variant in the CNKSR2 gene (NM_014927.5, c.1657+1G>A) was discovered by exome sequencing. The variant caused a 166 bp intron retention between exons 14 and 15, which was validated by a minigene assay. The variant was not reported in public databases such as gnomAD and the Exome Aggregation Consortium. CONCLUSIONS: The variant was predicted to be damaging to correct the translation of the CNKRS2 protein and was classified as likely pathogenic according to the ACMG guidelines.

A Nanosized Codelivery System Based on Intracellular Stimuli-Triggered Dual-Drug Release for Multilevel Chemotherapy Amplification in Drug-Resistant Breast Cancer.

Lacking nano-systems for precisely codelivering the chemotherapeutics paclitaxel (PTX) and the natural P-glycoprotein (P-gp) inhibitor, quercetin (QU), into cancer cells and controlling their intracellular release extremely decreased the anticancer effects in multidrug resistant (MDR) tumors. To overcome this hurdle, we constructed hybrid polymeric nanoparticles (PNPs) which consist of redox-sensitive PTX/polyethyleneimine-tocopherol hydrogen succinate-dithioglycollic acid PNPs and pH-sensitive hyaluronic acid-QU conjugates. The obtained hybrid PNPs can be internalized into drug-resistant breast cancer cells by the hyaluronic acid/CD44-mediated endocytosis pathway and escape from the lysosome through the "proton sponge effect". Under the trigger of intracellular stimuli, the nanoplatform used the pH/glutathione dual-sensitive disassembly to release QU and PTX. The PTX diffused into microtubules to induce tumor cell apoptosis, while QU promoted PTX retention by down-regulating P-gp expression. Moreover, tocopherol hydrogen succinate and QU disturbed mitochondrial functions by generating excessive reactive oxygen species, decreasing the mitochondrial membrane potential, and releasing cytochrome c into the cytosol which consequently achieved intracellular multilevel chemotherapy amplification in MDR cancers. Importantly, the PNPs substantially suppressed tumors growth with an average volume 2.54-fold lower than that of the control group in the MCF-7/ADR tumor-bearing nude mice model. These presented PNPs would provide a valuable reference for the coadministration of natural compounds and anticarcinogens for satisfactory combination therapy in MDR cancers.

Deciphering transcriptional regulators of banana fruit ripening by regulatory network analysis.

Fruit ripening is a critical phase in the production and marketing of fruits. Previous studies have indicated that fruit ripening is a highly coordinated process, mainly regulated at the transcriptional level, in which transcription factors play essential roles. Thus, identifying key transcription factors regulating fruit ripening as well as their associated regulatory networks promises to contribute to a better understanding of fruit ripening. In this study, temporal gene expression analyses were performed to investigate banana fruit ripening with the aim to discern the global architecture of gene regulatory networks underlying fruit ripening. Eight time points were profiled covering dynamic changes of phenotypes, the associated physiology and levels of known ripening marker genes. Combining results from a weighted gene co-expression network analysis (WGCNA) as well as cis-motif analysis and supported by EMSA, Y1H, tobacco-, banana-transactivation experimental results, the regulatory network of banana fruit ripening was constructed, from which 25 transcription factors were identified as prime candidates to regulate the ripening process by modulating different ripening-related pathways. Our study presents the first global view of the gene regulatory network involved in banana fruit ripening, which may provide the basis for a targeted manipulation of fruit ripening to attain higher banana and loss-reduced banana commercialization.

Neural networks for open and closed Literature-based Discovery.

Literature-based Discovery (LBD) aims to discover new knowledge automatically from large collections of literature. Scientific literature is growing at an exponential rate, making it difficult for researchers to stay current in their discipline and easy to miss knowledge necessary to advance their research. LBD can facilitate hypothesis testing and generation and thus accelerate scientific progress. Neural networks have demonstrated improved performance on LBD-related tasks but are yet to be applied to it. We propose four graph-based, neural network methods to perform open and closed LBD. We compared our methods with those used by the state-of-the-art LION LBD system on the same evaluations to replicate recently published findings in cancer biology. We also applied them to a time-sliced dataset of human-curated peer-reviewed biological interactions. These evaluations and the metrics they employ represent performance on real-world knowledge advances and are thus robust indicators of approach efficacy. In the first experiments, our best methods performed 2-4 times better than the baselines in closed discovery and 2-3 times better in open discovery. In the second, our best methods performed almost 2 times better than the baselines in open discovery. These results are strong indications that neural LBD is potentially a very effective approach for generating new scientific discoveries from existing literature. The code for our models and other information can be found at: https://github.com/cambridgeltl/nn_for_LBD.

Identification of PBMC-expressed miRNAs for rheumatoid arthritis.

Post-transcriptional regulation by miRNAs plays an important role in the pathogenesis of rheumatoid arthritis (RA), however, the roles of specific miRNAs in RA pathogenesis remain largely unclear. This study performed dual-omics (miRNA and mRNA) integration analysis and in-depth cellular and molecular functional exploration to identify novel RA-associated miRNAs and to understand their underlying pathogenic mechanism. Based on the miRNA and mRNA expression profiles in peripheral blood mononuclear cells (PBMCs) from a discovery sample set (25 RA cases and 18 healthy controls), 18 differentially expressed miRNAs (DEMIRs) (|Fold-change|>2 and P < 0.05) were identified and corresponding interaction networks of DEMIRs and mRNA were constructed. After the expression validation of the DEMIRs in a validation sample set (35 RA cases and 35 healthy controls), miR-99b-5p was highlighted. The over-expression of newly discovered miR-99b-5p is able to suppress T cell apoptosis, promote cell proliferation and activation, increase expression of proinflammatory cytokines (IL-2, IL-6, TNF-α, and IFN-γ), and inhibit expression of its target genes mTOR and RASSF4. This study comprehensively identified PBMC-expressed miRNAs along with corresponding regulatory networks significant for RA and discovered miR-99b-5p as a novel post-transcriptional mediator involved in RA pathogenesis. The findings improved our understanding of RA pathogenesis and provided novel insights into the molecular mechanisms underlying RA pathogenesis.

Mendelian randomization analysis revealed potential causal factors for systemic lupus erythematosus.

Genome-wide association studies (GWAS) have identified many loci for systemic lupus erythematosus (SLE). However, identification of functionally relevant genes remains a challenge. The aim of this study was to highlight potential causal genes for SLE in the GWAS loci. By applying Mendelian randomization (MR) methods, such as summary data-based MR (SMR), generalized SMR and MR pleiotropy residual sum and outlier, we identified DNA methylations in 15 loci and mRNA expression of 21 genes that were causally associated with SLE. The identified genes enriched in 14 specific KEGG pathways (e.g. SLE, viral carcinogenesis) and two GO terms (interferon-γ-mediated signaling pathway and innate immune response). Among the identified genes, UBE2L3 and BLK variants were significantly associated with UBE2L3 and BLK methylations and gene expressions, respectively. UBE2L3 was up-regulated in SLE patients in several types of immune cells. Methylations (e.g. cg06850285) and mRNA expression of UBE2L3 were causally associated with SLE. Methylation site cg09528494 and mRNA expression of BLK were causally associated with SLE. BLK single nucleotide polymorphisms that were significantly associated with SLE were strongly associated with plasma cathepsin B level. Deep analysis identified that plasma cathepsin B level was causally associated with SLE. In summary, this study identified hundreds of DNA methylations and genes as potential risk factors for SLE. Genetic variants in UBE2L3 gene might affect SLE by influencing gene expression. Genetic variants in BLK gene might affect SLE by influencing BLK gene expression and plasma cathepsin B protein level.

Integrative analysis revealed potential causal genetic and epigenetic factors for multiple sclerosis.

OBJECTIVE: Many genomic loci have been identified for multiple sclerosis (MS) by genome-wide association studies (GWAS). Discrimination of the most functionally relevant genes in these loci remains challenging. The aim of this study was to highlight potential causal genes for MS. METHODS: We detected potential causal DNA methylations and gene expressions for MS by integrating data from large scale GWAS and quantitative trait locus (QTL) studies using the summary data-based Mendelian randomization method. Potential functional SNPs in the identified genes were searched. RESULTS: We found 178 DNA methylation sites and mRNA expressions of 29 genes that were causally associated with MS. The identified genes enriched in 21 specific KEGG pathways and 80 GO terms (e.g., antigen processing and presentation, interferon gamma mediated signaling pathway). Among the identified non-MHC genes, METTL21B, METTL1 and TSFM were strongly connected. MS-associated SNPs in DDR1 were strongly associated with plasma MHC class I polypeptide-related sequence B (MICB) and Granzyme A levels. And plasma MICB and Granzyme A levels were causally associated with MS. Many SNPs in the causal genes showed QTL effects. The association between m6A-SNPs rs923829 and METTL21B expression level was validated in 40 unrelated Chinese Han individuals. CONCLUSIONS: This study identified many DNA methylations and genes as important risk factors for MS and provided novel evidence on the association between circulating MICB and Granzyme A and MS. We also showed that the interaction among DDR1, MICB and GZMA and interaction among METTL21B, METTL1 and TSFM may participate in the pathogenesis of MS.

MaBZR1/2 act as transcriptional repressors of ethylene biosynthetic genes in banana fruit.

Banana fruit (Musa acuminate L.) ripening is a complex genetical process affected by multiple phytohormones and expression of various genes. However, whether plant hormone brassinosteroid (BR) is involved in this process remains obscure. In this work, three genes that encode BR core signaling components brassinazole resistant (BZR) proteins, namely MaBZR1 to MaBZR3, were characterized from banana fruit. MaBZR1-MaBZR3 exhibited both nuclear and cytoplasmic localization and behaved as transcription inhibitors. Expression analysis showed that MaBZR1/2/3 were continuously decreased as fruit ripening proceeded, indicating their negative roles in banana ripening. Moreover, gel shift and transient expression assays demonstrated that MaBZR1/2 could suppress the transcription of ethylene biosynthetic genes, including MaACS1, MaACO13 and MaACO14, which increased gradually during the banana ripening, via specifically binding to CGTGT/CG sequence in their promoters. Importantly, exogenous application of BRs promotes banana ripening, which is presumably due to the accelerated expression of MaACS1 and MaACO13/14, and consequently the ethylene production. Our study indicates that MaBZR1/2 act as transcriptional repressors of ethylene biosynthetic genes during banana fruit ripening.

Rheumatoid arthritis-associated DNA methylation sites in peripheral blood mononuclear cells.

OBJECTIVES: To identify novel DNA methylation sites significant for rheumatoid arthritis (RA) and comprehensively understand their underlying pathological mechanism. METHODS: We performed (1) genome-wide DNA methylation and mRNA expression profiling in peripheral blood mononuclear cells from RA patients and health controls; (2) correlation analysis and causal inference tests for DNA methylation and mRNA expression data; (3) differential methylation genes regulatory network construction; (4) validation tests of 10 differential methylation positions (DMPs) of interest and corresponding gene expressions; (5) correlation between PARP9 methylation and its mRNA expression level in Jurkat cells and T cells from patients with RA; (6) testing the pathological functions of PARP9 in Jurkat cells. RESULTS: A total of 1046 DNA methylation positions were associated with RA. The identified DMPs have regulatory effects on mRNA expressions. Causal inference tests identified six DNA methylation-mRNA-RA regulatory chains (eg, cg00959259-PARP9-RA). The identified DMPs and genes formed an interferon-inducible gene interaction network (eg, MX1, IFI44L, DTX3L and PARP9). Key DMPs and corresponding genes were validated their differences in additional samples. Methylation of PARP9 was correlated with mRNA level in Jurkat cells and T lymphocytes isolated from patients with RA. The PARP9 gene exerted significant effects on Jurkat cells (eg, cell cycle, cell proliferation, cell activation and expression of inflammatory factor IL-2). CONCLUSIONS: This multistage study identified an interferon-inducible gene interaction network associated with RA and highlighted the importance of PARP9 gene in RA pathogenesis. The results enhanced our understanding of the important role of DNA methylation in pathology of RA.

LION LBD: a literature-based discovery system for cancer biology.

MOTIVATION: The overwhelming size and rapid growth of the biomedical literature make it impossible for scientists to read all studies related to their work, potentially leading to missed connections and wasted time and resources. Literature-based discovery (LBD) aims to alleviate these issues by identifying implicit links between disjoint parts of the literature. While LBD has been studied in depth since its introduction three decades ago, there has been limited work making use of recent advances in biomedical text processing methods in LBD. RESULTS: We present LION LBD, a literature-based discovery system that enables researchers to navigate published information and supports hypothesis generation and testing. The system is built with a particular focus on the molecular biology of cancer using state-of-the-art machine learning and natural language processing methods, including named entity recognition and grounding to domain ontologies covering a wide range of entity types and a novel approach to detecting references to the hallmarks of cancer in text. LION LBD implements a broad selection of co-occurrence based metrics for analyzing the strength of entity associations, and its design allows real-time search to discover indirect associations between entities in a database of tens of millions of publications while preserving the ability of users to explore each mention in its original context in the literature. Evaluations of the system demonstrate its ability to identify undiscovered links and rank relevant concepts highly among potential connections. AVAILABILITY AND IMPLEMENTATION: The LION LBD system is available via a web-based user interface and a programmable API, and all components of the system are made available under open licenses from the project home page http://lbd.lionproject.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cancer Hallmarks Analytics Tool (CHAT): a text mining approach to organize and evaluate scientific literature on cancer.

MOTIVATION: To understand the molecular mechanisms involved in cancer development, significant efforts are being invested in cancer research. This has resulted in millions of scientific articles. An efficient and thorough review of the existing literature is crucially important to drive new research. This time-demanding task can be supported by emerging computational approaches based on text mining which offer a great opportunity to organize and retrieve the desired information efficiently from sizable databases. One way to organize existing knowledge on cancer is to utilize the widely accepted framework of the Hallmarks of Cancer. These hallmarks refer to the alterations in cell behaviour that characterize the cancer cell. RESULTS: We created an extensive Hallmarks of Cancer taxonomy and developed automatic text mining methodology and a tool (CHAT) capable of retrieving and organizing millions of cancer-related references from PubMed into the taxonomy. The efficiency and accuracy of the tool was evaluated intrinsically as well as extrinsically by case studies. The correlations identified by the tool show that it offers a great potential to organize and correctly classify cancer-related literature. Furthermore, the tool can be useful, for example, in identifying hallmarks associated with extrinsic factors, biomarkers and therapeutics targets. AVAILABILITY AND IMPLEMENTATION: CHAT can be accessed at: http://chat.lionproject.net. The corpus of hallmark-annotated PubMed abstracts and the software are available at: http://chat.lionproject.net/about. CONTACT: simon.baker@cl.cam.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The tripartite motif-containing protein 3 on the proliferation and cytokine secretion of rheumatoid arthritis fibroblast-like synoviocytes.

Recent studies have revealed fibroblast-like synoviocytes (FLS) as a pivotal effector cell in the inflamed joint of rheumatoid arthritis (RA) patients. FLS exhibit high proliferation rates and constitutive expression of cytokines, contributing to the pathogenesis of RA. In this study, we found that the expression of tripartite motif-containing protein 3 (TRIM3), a candidate tumor suppressor gene, was lower in synovial tissue samples of RA patients than in that of healthy controls. We then investigated the role of TRIM3 on the proliferation and cytokine secretion of primary cultured FLS from RA patients. Enforced expression of TRIM3 in RA FLS led to significantly decreased cell proliferation as indicated by Cell Counting Kit-8 assay, reduced secretion of tumor necrosis factor-α (TNF)-α, interleukin (IL)-1ß and IL-6 as indicated by enzyme-linked immunosorbent assays, and decreased p38 phosphorylation as assessed by western blot analysis. The proteins promoting cell cycles (cyclin D1 and PCNA) were downregulated and the protein negatively regulating cell cycle progression (p53 and p21) was upregulated after TRIM3 overexpression. Importantly, TRIM3 knockdown had reverse effects on cell proliferation, which was suppressed by the p38-specific inhibitor SB203580. In conclusion, the current results demonstrated the downregulation of TRIM3 expression in RA synovial tissues. Importantly, TRIM3 exerted an anti-proliferation role in RA FLS via p38 signaling pathway.

Grouping chemicals for health risk assessment: A text mining-based case study of polychlorinated biphenyls (PCBs).

As many chemicals act as carcinogens, chemical health risk assessment is critically important. A notoriously time consuming process, risk assessment could be greatly supported by classifying chemicals with similar toxicological profiles so that they can be assessed in groups rather than individually. We have previously developed a text mining (TM)-based tool that can automatically identify the mode of action (MOA) of a carcinogen based on the scientific evidence in literature, and it can measure the MOA similarity between chemicals on the basis of their literature profiles (Korhonen et al., 2009, 2012). A new version of the tool (2.0) was recently released and here we apply this tool for the first time to investigate and identify meaningful groups of chemicals for risk assessment. We used published literature on polychlorinated biphenyls (PCBs)-persistent, widely spread toxic organic compounds comprising of 209 different congeners. Although chemically similar, these compounds are heterogeneous in terms of MOA. We show that our TM tool, when applied to 1648 PubMed abstracts, produces a MOA profile for a subgroup of dioxin-like PCBs (DL-PCBs) which differs clearly from that for the rest of PCBs. This suggests that the tool could be used to effectively identify homogenous groups of chemicals and, when integrated in real-life risk assessment, could help and significantly improve the efficiency of the process.

Automatic semantic classification of scientific literature according to the hallmarks of cancer.

MOTIVATION: The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research. RESULTS: We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future. AVAILABILITY AND IMPLEMENTATION: The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html. CONTACT: simon.baker@cl.cam.ac.uk.

Weakly supervised learning of information structure of scientific abstracts--is it accurate enough to benefit real-world tasks in biomedicine?

MOTIVATION: Many practical tasks in biomedicine require accessing specific types of information in scientific literature; e.g. information about the methods, results or conclusions of the study in question. Several approaches have been developed to identify such information in scientific journal articles. The best of these have yielded promising results and proved useful for biomedical text mining tasks. However, relying on fully supervised machine learning (ml) and a large body of annotated data, existing approaches are expensive to develop and port to different tasks. A potential solution to this problem is to employ weakly supervised learning instead. In this article, we investigate a weakly supervised approach to identifying information structure according to a scheme called Argumentative Zoning (az). We apply four weakly supervised classifiers to biomedical abstracts and evaluate their performance both directly and in a real-life scenario in the context of cancer risk assessment. RESULTS: Our best weakly supervised classifier (based on the combination of active learning and self-training) performs well on the task, outperforming our best supervised classifier: it yields a high accuracy of 81% when just 10% of the labeled data is used for training. When cancer risk assessors are presented with the resulting annotated abstracts, they find relevant information in them significantly faster than when presented with unannotated abstracts. These results suggest that weakly supervised learning could be used to improve the practical usefulness of information structure for real-life tasks in biomedicine.

A comparison and user-based evaluation of models of textual information structure in the context of cancer risk assessment.

BACKGROUND: Many practical tasks in biomedicine require accessing specific types of information in scientific literature; e.g. information about the results or conclusions of the study in question. Several schemes have been developed to characterize such information in scientific journal articles. For example, a simple section-based scheme assigns individual sentences in abstracts under sections such as Objective, Methods, Results and Conclusions. Some schemes of textual information structure have proved useful for biomedical text mining (BIO-TM) tasks (e.g. automatic summarization). However, user-centered evaluation in the context of real-life tasks has been lacking. METHODS: We take three schemes of different type and granularity--those based on section names, Argumentative Zones (AZ) and Core Scientific Concepts (CoreSC)--and evaluate their usefulness for a real-life task which focuses on biomedical abstracts: Cancer Risk Assessment (CRA). We annotate a corpus of CRA abstracts according to each scheme, develop classifiers for automatic identification of the schemes in abstracts, and evaluate both the manual and automatic classifications directly as well as in the context of CRA. RESULTS: Our results show that for each scheme, the majority of categories appear in abstracts, although two of the schemes (AZ and CoreSC) were developed originally for full journal articles. All the schemes can be identified in abstracts relatively reliably using machine learning. Moreover, when cancer risk assessors are presented with scheme annotated abstracts, they find relevant information significantly faster than when presented with unannotated abstracts, even when the annotations are produced using an automatic classifier. Interestingly, in this user-based evaluation the coarse-grained scheme based on section names proved nearly as useful for CRA as the finest-grained CoreSC scheme. CONCLUSIONS: We have shown that existing schemes aimed at capturing information structure of scientific documents can be applied to biomedical abstracts and can be identified in them automatically with an accuracy which is high enough to benefit a real-life task in biomedicine.