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Background: Accumulating evidence points to epigenetic mechanisms as essential in tumorigenesis. Treatment that targets epigenetic regulators is becoming an attractive strategy for cancer therapy. The role of epigenetic therapy in prostate cancer (PCa) remains elusive. Previously we demonstrated a correlation of levels of histone lysine demethylase KDM4B with the appearance of castration resistant prostate cancer (CRPC) and identified a small molecular inhibitor of KDM4B, B3. In this study, we aim to define the role of KDM4B in promoting PCa progression and test the efficacy of B3 using clinically relevant PCa models. Methods: KDM4B was overexpressed in LNCaP cells or knocked down (KD) in 22Rv1 cells. The specificity of B3 was determined in vitro using recombinant KDM proteins and in vivo using 22Rv1 cell lysates. The efficacy of B3 monotherapy or in combination with androgen receptor (AR) antagonist enzalutamide or the mTOR inhibitor rapamycin was tested using xenograft models in castrated mice. Comparative transcriptomic analysis was performed on KDM4B KD and B3-treated 22Rv1 cells to determine the on-target (KDM4B-dependent) and off-target (non-KDM4B-associated) effects of B3.Results: Overexpression of KDM4B in LNCaP cells enhanced its tumorigenicity whereas knockdown of KDM4B in 22Rv1 cells reduced tumor growth in castrated mice. B3 suppressed the growth of both 22Rv1 and VCaP xenografts and sensitized 22Rv1 cells to enzalutamide inhibition. B3 also inhibited 22Rv1 tumor growth synergistically with rapamycin that resulted in cell apoptosis. Mechanistically, B3 inhibited expression of AR-V7 and genes involved in epithelial-to-mesenchymal transition. DNA replication stress marker gH2A.X was upregulated by B3, which is further increased when combined with rapamycin. Based on transcriptomic and biochemical analyses, B3 inhibits both H3K9me3 and H3K27me3 demethylase activity, which is believed to underlie its anti-tumor action. Conclusions: Our studies establish KDM4B as a potent target for CRPC and B3 as a potential therapeutic agent. B3 as monotherapy or in combination with other anti-PCa therapeutics offers proof of principle for the clinical translation of epigenetic therapy targeting KDMSs for CRPC patients.

Recently, identifying robust biomarkers or signatures from gene expression profiling data has attracted much attention in computational biomedicine. The successful discovery of biomarkers for complex diseases such as spontaneous preterm birth (SPTB) and high-grade serous ovarian cancer (HGSOC) will be beneficial to reduce the risk of preterm birth and ovarian cancer among women for early detection and intervention. In this paper, we propose a stable machine learning-recursive feature elimination (StabML-RFE for short) strategy for screening robust biomarkers from high-throughput gene expression data. We employ eight popular machine learning methods, namely AdaBoost (AB), Decision Tree (DT), Gradient Boosted Decision Trees (GBDT), Naive Bayes (NB), Neural Network (NNET), Random Forest (RF), Support Vector Machine (SVM) and XGBoost (XGB), to train on all feature genes of training data, apply recursive feature elimination (RFE) to remove the least important features sequentially, and obtain eight gene subsets with feature importance ranking. Then we select the top-ranking features in each ranked subset as the optimal feature subset. We establish a stability metric aggregated with classification performance on test data to assess the robustness of the eight different feature selection techniques. Finally, StabML-RFE chooses the high-frequent features in the subsets of the combination with maximum stability value as robust biomarkers. Particularly, we verify the screened biomarkers not only via internal validation, functional enrichment analysis and literature check, but also via external validation on two real-world SPTB and HGSOC datasets respectively. Obviously, the proposed StabML-RFE biomarker discovery pipeline easily serves as a model for identifying diagnostic biomarkers for other complex diseases from omics data. The source code and data can be found at https://github.com/zpliulab/StabML-RFE.

Summary Background Identification of high-risk localised renal cell carcinoma is key for the selection of patients for adjuvant treatment who are at truly higher risk of reccurrence. We developed a classifier based on single-nucleotide polymorphisms (SNPs) to improve the predictive accuracy for renal cell carcinoma recurrence and investigated whether intratumour heterogeneity affected the precision of the classifier. Methods In this retrospective analysis and multicentre validation study, we used paraffin-embedded specimens from the training set of 227 patients from Sun Yat-sen University (Guangzhou, Guangdong, China) with localised clear cell renal cell carcinoma to examine 44 potential recurrence-associated SNPs, which were identified by exploratory bioinformatics analyses of a genome-wide association study from The Cancer Genome Atlas (TCGA) Kidney Renal Clear Cell Carcinoma (KIRC) dataset (n=114, 906 600 SNPs). We developed a six-SNP-based classifier by use of LASSO Cox regression, based on the association between SNP status and patients' recurrence-free survival. Intratumour heterogeneity was investigated from two other regions within the same tumours in the training set. The six-SNP-based classifier was validated in the internal testing set (n=226), the independent validation set (Chinese multicentre study; 428 patients treated between Jan 1, 2004 and Dec 31, 2012, at three hospitals in China), and TCGA set (441 retrospectively identified patients who underwent resection between 1998 and 2010 for localised clear cell renal cell carcinoma in the USA). The main outcome was recurrence-free survival; the secondary outcome was overall survival. Findings Although intratumour heterogeneity was found in 48 (23%) of 206 cases in the internal testing set with complete SNP information, the predictive accuracy of the six-SNP-based classifier was similar in the three different regions of the training set (areas under the curve [AUC] at 5 years: 0·749 [95% CI 0·660–0·826] in region 1, 0·734 [0·651–0·814] in region 2, and 0·736 [0·649–0·824] in region 3). The six-SNP-based classifier precisely predicted recurrence-free survival of patients in three validation sets (hazard ratio [HR] 5·32 [95% CI 2·81–10·07] in the internal testing set, 5·39 [3·38–8·59] in the independent validation set, and 4·62 [2·48–8·61] in the TCGA set; all p Interpretation Our six-SNP-based classifier could be a practical and reliable predictor that can complement the existing staging system for prediction of localised renal cell carcinoma recurrence after surgery, which might enable physicians to make more informed treatment decisions about adjuvant therapy. Intratumour heterogeneity does not seem to hamper the accuracy of the six-SNP-based classifier as a reliable predictor of recurrence. The classifier has the potential to guide treatment decisions for patients at differing risks of recurrence. Funding National Key Research and Development Program of China, National Natural Science Foundation of China, Guangdong Provincial Science and Technology Foundation of China, and Guangzhou Science and Technology Foundation of China.

Gene expression profiling techniques measure the transcriptional dynamics of thousands of genes in parallel manners. The available high-throughput transcriptomic datasets provide unprecedented opportunities of detecting biomarkers or signatures of complex diseases such as diabetes. In this work, we propose a computational method based on differential entropy to identify diabetic pathway biomarkers in rats from gene expression profiling data. We first collect the knowledgebase-documented pathways and map them with the corresponding gene expressions in control and disease samples, respectively. The pathway entropies are defined to evaluate their dysfunction-related activities and implications during the development and progression of type 2 diabetes. We rank these pathways via their differential status of entropy dynamics in the time series. The pathway biomarkers are then screened out by their classification ability of distinguishing diabetes from controls. The comparative studies with the other alternative methods demonstrate the effectiveness and advantage of our proposed strategy of biomarker identification. The classification performances on independent datasets further validate the diagnosis applicability of these identified pathway biomarkers. The functional enrichment analyses of these pathway biomarkers also indicate the pathogenesis of diabetes.

Immunoglobulin heavy chain (IgH) locus-associated G-rich long noncoding RNA (SμGLT) is important for physiological and pathological B cell DNA recombination. We demonstrate that the METTL3 enzyme-catalyzed N6-methyladenosine (m6A) RNA modification drives recognition and 3' end processing of SμGLT by the RNA exosome, promoting class switch recombination (CSR) and suppressing chromosomal translocations. The recognition is driven by interaction of the MPP6 adaptor protein with nuclear m6A reader YTHDC1. MPP6 and YTHDC1 promote CSR by recruiting AID and the RNA exosome to actively transcribe SμGLT. Direct suppression of m6A modification of SμGLT or of m6A reader YTHDC1 reduces CSR. Moreover, METTL3, an essential gene for B cell development in the bone marrow and germinal center, suppresses IgH-associated aberrant DNA breaks and prevents genomic instability. Taken together, we propose coordinated and central roles for MPP6, m6A modification, and m6A reader proteins in controlling long noncoding RNA processing, DNA recombination, and development in B cells.

Electrocardiogram (ECG) and phonocardiogram (PCG) play important roles in early prevention and diagnosis of cardiovascular diseases (CVDs). As the development of machine learning techniques, detection of CVDs by them from ECG and PCG has attracted much attention. However, current available methods are mostly based on single source data. It is desirable to develop efficient multi-modal machine learning methods to predict and diagnose CVDs. In this study, we propose a novel multi-modal method for predicting CVDs based both on ECG and PCG features. By building up conventional neural networks, we extract ECG and PCG deep-coding features respectively. The genetic algorithm is used to screen the combined features and obtain the best feature subset. Then we employ a support vector machine to implement classifications. Experimental results demonstrate the performance of our method is superior to those of single modal methods and alternatives. Our method reaches an AUC value of 0.936 when we use multi-modal features of ECG and PCG.

Harmful algal blooms are a major type of marine disaster that endangers the marine ecological environment and human health. Since the algal bloom is a complex nonlinear process with time characteristics, traditional statistical methods often cannot provide good predictions. In this paper, we propose a method based on machine learning with the aim to predict the occurrence of algal blooms by environmental parameters. The features related to algal bloom growth have been experimented for achieving a good prediction of algal concentrations by a combination strategy. We validate the prediction performance on two real datasets from two locations in US and China, i.e., Scripps Pier, California and Sishili Bay, Shandong, respectively. The models and feature subsets have been selected to complete the missing data and predict the phytoplankton concentration. The results demonstrate the efficiency of our method in the short-term prediction of concentrations by selecting appropriate features. The comparison studies prove the advantage of our developed machine learning method. The importance of every features for the prediction performance reveals crucial factors for the outbreak of harmful algal blooms.

Protein-RNA interactions play critical roles in numerous biological processes such as posttranscriptional regulation and protein synthesis. However, experimental screening of protein-RNA interactions is usually laborious and time-consuming. It is therefore desirable to develop efficient bioinformatics methods to predict protein-RNA interactions, which can provide valuable hints for future experimental design and advance our understanding of the interaction mechanisms. In this study, we propose a novel method for predicting protein-RNA interactions based on both sequence and structure descriptors of protein and RNA (e.g., the sequence-based physicochemical features, the secondary and three-dimensional structure-based features). We train and compare several classifiers using these descriptors on several benchmark datasets, and the random forest method is selected to build an efficient predictor of protein-RNA interactions. We conduct further cross-validation and case studies, and the results clearly suggest the efficacy of the proposed method. A novel computational method is proposed for predicting protein-RNA interactions.The efficiency and advantage are shown in multiple benchmarks and comparison studies.Case studies in protein-miRNA/lncRNA interactions demonstrate its powerful prediction ability.

SummaryWe have ablated the cellular RNA degradation machinery in differentiated B cells and pluripotent embryonic stem cells (ESCs) by conditional mutagenesis of core (Exosc3) and nuclear RNase (Exosc10) components of RNA exosome and identified a vast number of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) with emergent functionality. Unexpectedly, eRNA-expressing regions accumulate R-loop structures upon RNA exosome ablation, thus demonstrating the role of RNA exosome in resolving deleterious DNA/RNA hybrids arising from active enhancers. We have uncovered a distal divergent eRNA-expressing element (lncRNA-CSR) engaged in long-range DNA interactions and regulating IgH 3′ regulatory region super-enhancer function. CRISPR-Cas9-mediated ablation of lncRNA-CSR transcription decreases its chromosomal looping-mediated association with the IgH 3′ regulatory region super-enhancer and leads to decreased class switch recombination efficiency. We propose that the RNA exosome protects divergently transcribed lncRNA expressing enhancers by resolving deleterious transcription-coupled secondary DNA structures, while also regulating long-range super-enhancer chromosomal interactions important for cellular function.

Protein-RNA interactions play critical roles in numerous biological processes such as posttranscriptional regulation and protein synthesis. However, experimental screening of protein-RNA interactions is usually laborious and time-consuming. It is therefore desirable to develop efficient bioinformatics methods to predict protein-RNA interactions, which can provide valuable hints for future experimental design and advance our understanding of the interaction mechanisms. In this study, we propose a novel method for predicting protein-RNA interactions based on both sequence and structure descriptors of protein and RNA (e.g., the sequence-based physicochemical features, the secondary and three-dimensional structure-based features). We train and compare several classifiers using these descriptors on several benchmark datasets, and the random forest method is selected to build an efficient predictor of protein-RNA interactions. We conduct further cross-validations and the results clearly suggest the efficacy of the proposed method.

To improve the accuracy and genetic progress of blue fox breeding, the relationships between genetic polymorphisms and growth and reproductive traits of the blue fox were investigated. MC4R, MC3R, INHA and INHBA were selected as candidate genes for molecular evolution and statistical analyses. Single-factor variance analyses showed that the MC4R (g.267C T, g.423C T, and g.731C A) and MC3R (g.677C T) genotypes had significant impacts on body weight, chest circumference, abdominal perimeter and body mass index (BMI) (P 0.05) in blue fox. The MC4R and MC3R combined genotypes had significant effects on the body weight and abdominal circumference. The different genotypes of INHA g.75G A had significant effects on female fecundity, whereas the different genotypes of INHBA g.404G T and g.467G T and the INHA and INHBA combined genotypes had significant effects on male fecundity. The proteins encoded by the open reading frames (ORFs) of different polymorphic loci were predicted and analysed. The aims of this study were to identify genetic markers related to growth and reproduction in the blue fox and to provide an efficient, economical and accurate theoretical approach for auxiliary fox breeding.

Optimal allocation of emergency resources is a crucial content of emergency management. It is a key step in emergency rescue and assistance. Multiple resources and potential secondary disasters are often neglected in the existing methods, which desperately need to be improved. In this paper, we formulate the emergency resource allocation problem with constraints of multiple resources and possible secondary disasters, and model the multiple resources and multiple emergency response depots problem considering multiple secondary disasters by an integer mathematical programming. For the complexity, a heuristic algorithm is designed to efficiently solve it based on linear programming and network optimization. The algorithm modifies the solutions of the linear programming by setting a priority of preference for each location where the secondary disasters will take place with certain possibilities. The numerical simulation provides evidence for its effectiveness and efficiency. Our method and algorithm can also be implemented in the practical applications with large-scale scenario.

Pederin belongs to a group of antitumor compounds found in terrestrial beetles and marine sponges. It is apparently used by some members of the rove beetle Paederus as a chemical defense against predators. A recent cluster analysis of the putative pederin biosynthesis gene (ped) strongly suggests that pederin is produced by bacterial symbionts. This paper reviewed the criteria for proving symbiontic origin of bioactive metabolite, indirect and molecular evidences for pederin bacterial origin, as well as three sets of ped clusters and putative biosynthesis process of pederin.

Homeodomain-only protein/not expressed in choriocarcinoma clone 1 (HOP/NECC1) is a newly identified gene that modifies the expression of cardiac-specific genes and thereby regulates heart development. More recently, HOP/NECC1 was reported to be a suppressor of choriocarcinogenesis. Here, we examined the temporal expression profile of HOP/NECC1 in wild-type mouse placenta. We found that E8.5-E9.5 wild-type placenta expressed HOP/NECC1 in the giant cell and spongiotrophoblast layers. HOP/NECC1 (-/-) placenta exhibited marked propagation of giant cell layers and, in turn reduction of spongiotrophoblast formation. We demonstrated SRF transcriptional activity increased in the differentiating trophoblasts and forced expression of SRF in a trophoblast stem (TS) cell line induces the differentiation into giant cells. Negative regulation of SRF (serum response factor) by the binding of HOP/NECC1 protein contributed at least in part to the generation of these placental defects. Gradual induction of HOP/NECC1 in response to differentiation stimuli may result in the decision to differentiate into a particular type of trophoblastic cell lineage and result in non-lethal defects shown by the HOP/NECC1 (-/-) placentas.

A novel miniaturized microstrip-line filter with a pair of meandering resonators on lithium niobate LiNbO 3 substrate with a high dielectric constant is proposed. A stepped-impedance structure is integrated into this new filter simultaneously to reduce dimensions and suppress the spurious responses. A substrate with a high dielectric constant is also effective in reducing the filter size because it has a high coupling efficiency. The cross-coupling effect can be achieved in this designed filter. A copper (Cu) interconnection was also used in this investigation to improve further the quality factor and the insertion loss of this filter. The filter has two transmission zeros one on each side of the passband. The experimental results in the K-band verify the design.

We have discovered an unusual homeodomain protein, called HOP, which is comprised simply of a homeodomain. HOP is highly expressed in the developing heart where its expression is dependent on the cardiac-restricted homeodomain protein Nkx2.5. HOP does not bind DNA and acts as an antagonist of serum response factor (SRF), which regulates the opposing processes of proliferation and myogenesis. Mice homozygous for a HOP null allele segregate into two phenotypic classes characterized by an excess or deficiency of cardiac myocytes. We propose that HOP modulates SRF activity during heart development; its absence results in an imbalance between cardiomyocyte proliferation and differentiation with consequent abnormalities in cardiac morphogenesis.

Addition of a small amount of ganglioside GM(1) to phosphatidylserine (PS) liposomes, a gradual increase of protein kinase C (PKC) activity was recorded up to about 2 mol% GM(1) where the maximal enzyme activity was obtained. Then the activity of PKC began to decline and even turned to be inhibited with the further increase of GM(1) content. It was also indicated that GM(1)/PS binary liposomes had the highest membrane fluidity and very low spatial density of lipid headgroups which was demonstrated in the MC-540 studies due to the interposition of GM(1) when the liposomes contained about 2 mol% GM(1). Besides, the liposomes containing about 2 mol% GM(1) provided a more hydrophobic environment for PKC than the liposomes containing less or more GM(1) which was indicated in the Acrylodan experiments. These factors commonly induced PKC to be stimulated maximally. Whether at the lower or higher GM(1) content, the membrane structure was not the most suitable to support the activity of PKC, which declined as a consequence.