Constructing lncRNA-miRNA-mRNA networks specific to individual cancer patients and finding prognostic biomarkers.

BACKGROUND: The competitive endogenous RNA (ceRNA) hypothesis suggests that microRNAs (miRNAs) mediate a regulatory relation between long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) which share similar miRNA response elements (MREs) to bind to the same miRNA. Since the ceRNA hypothesis was proposed, several studies have been conducted to construct a network of lncRNAs, miRNAs and mRNAs in cancer. However, most cancer-related ceRNA networks are intended for representing a general relation of RNAs in cancer rather than for a patient-specific relation. Due to the heterogeneous nature of cancer, lncRNA-miRNA-mRNA interactions can vary in different patients. RESULTS: We have developed a new method for constructing a ceRNA network of lncRNAs, miRNAs and mRNAs, which is specific to an individual cancer patient and for finding prognostic biomarkers consisting of lncRNA-miRNA-mRNA triplets. We tested our method on extensive data sets of three types of cancer (breast cancer, liver cancer, and lung cancer) and obtained potential prognostic lncRNA-miRNA-mRNA triplets for each type of cancer. CONCLUSIONS: Analysis of expression patterns of the RNAs involved in the triplets and survival rates of cancer patients revealed several interesting findings. First, even for the same cancer type, prognostic lncRNA-miRNA-mRNA triplets can be different depending on whether lncRNA and mRNA show opposite or similar expression patterns. Second, prognostic lncRNA-miRNA-mRNA triplets are often more predictive of survival rates than RNA pairs or individual RNAs. Our approach will be useful for constructing patient-specific lncRNA-miRNA-mRNA networks and for finding prognostic biomarkers from the networks.

The Inhibition Activity of Natural Methoxyflavonoid from Inula britannica on Soluble Epoxide Hydrolase and NO Production in RAW264.7 Cells.

Soluble epoxide hydrolase (sEH) is an enzyme targeted for the treatment of inflammation and cardiovascular diseases. Activated inflammatory cells produce nitric oxide (NO), which induces oxidative stress and exacerbates inflammation. We identify an inhibitor able to suppress sEH and thus NO production. Five flavonoids 1-5 isolated from Inula britannica flowers were evaluated for their abilities to inhibit sEH with IC50 values of 12.1 ± 0.1 to 62.8 ± 1.8 µM and for their effects on enzyme kinetics. A simulation study using computational chemistry was conducted as well. Furthermore, five inhibitors (1-5) were confirmed to suppress NO levels at 10 µM. The results showed that flavonoids 1-5 exhibited inhibitory activity in all tests, with compound 3 exhibiting the most significant efficacy. Thus, in the development of anti-inflammatory inhibitors, compound 3 is a promising natural candidate.

Comparison of Quality, Antioxidant Capacity, and Anti-Inflammatory Activity of Adlay [Coix lacryma-jobi L. var. ma-yuen (Rom. Caill.) Stapf.] Sprout at Several Harvest Time.

Recently, there has been a growing interest in the consumption of plant-based foods such as vegetables and grains for the purpose of disease prevention and treatment. Adlay seeds contain physiologically active substances, including coixol, coixenolide, and lactams. In this study, adlay sprouts were cultivated and harvested at various time points, specifically at 3, 5, 7, 9, and 11 days after sowing. The antioxidant activity of the extracts was evaluated using assays such as DPPH radical scavenging, ABTS radical scavenging, reducing power, and total polyphenol contents. The toxicity of the extracts was assessed using cell culture and the WST-1 assay. The aboveground components of the sprouts demonstrated a significant increase in length, ranging from 2.75 cm to 21.87 cm, weight, ranging from 0.05 g to 0.32 g, and biomass, ranging from 161.4 g to 1319.1 g, as the number of days after sowing advanced, reaching its peak coixol content of 39.38 mg/g on the third day after sowing. Notably, the antioxidant enzyme activity was highest between the third and fifth days after sowing. Regarding anti-inflammatory activity, the inhibition of cyclooxygenase 2 (COX-2) expression was most prominent in samples harvested from the ninth to eleventh days after sowing, corresponding to the later stage of growth. While the overall production mass increased with the number of days after sowing, considering factors such as yield increase index per unit area, turnover rate, and antioxidant activity, harvesting at the early growth stage, specifically between the fifth and seventh days after sowing, was found to be economically advantageous. Thus, the quality, antioxidant capacity, and anti-inflammatory activity of adlay sprouts varied depending on the harvest time, highlighting the importance of determining the appropriate harvest time based on the production objectives. This study demonstrates the changes in the growth and quality of adlay sprouts in relation to the harvest time, emphasizing the potential for developing a market for adlay sprouts as a new food product.

Prediction of Cancer Metastasis Using Correlations Between miRNAs and Competing Endogenous RNAs.

Cancer metastasis is a complex process which involves the spread of tumor cells from the primary site to other parts of the body. Metastasis is the major cause of cancer mortality, accounting for about 90% of cancer deaths. Metastasis is primarily diagnosed by clinical examinations and imaging techniques, but such a diagnosis is made after metastasis has occurred. Prediction or early detection of metastasis is important for treatment planning since it has an impact on the survival of patients. Recently a few methods have been developed to predict lymph node metastasis, but few methods are available for predicting distant metastasis. Motivated by a gene regulation mechanism involving miRNAs, we have developed a new method for predicting both lymph node metastasis and distant metastasis. We have derived differential correlations of miRNAs and their target RNAs in cancer, and built prediction models using the differential correlations. Testing the method on several types of cancer showed that differential correlations of miRNAs and target RNAs are much more powerful and stable than expressions of known metastasis predictive genes in predicting distant metastasis as well as lymph node metastasis. The method developed in this study will be useful in predicting metastasis and thereby in determining treatment options for cancer patients.

Finding miRNA-RNA Network Biomarkers for Predicting Metastasis and Prognosis in Cancer.

Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, we derived miRNA-RNA pairs with substantially different correlations from those in normal tissue samples. Using the differential miRNA-RNA correlations, we constructed models for predicting metastasis. A comparison of our model to other models with the same data sets of solid cancer showed that our model is much better than the others in both lymph node metastasis and distant metastasis. The miRNA-RNA correlations were also used in finding prognostic network biomarkers in cancer patients. The results of our study showed that miRNA-RNA correlations and networks consisting of miRNA-RNA pairs were more powerful in predicting prognosis as well as metastasis. Our method and the biomarkers obtained using the method will be useful for predicting metastasis and prognosis, which in turn will help select treatment options for cancer patients and targets of anti-cancer drug discovery.

Constructing a Cancer Patient-Specific Network Based on Second-Order Partial Correlations of Gene Expression and DNA Methylation.

Typically patient-specific gene networks are constructed with gene expression data only. Such networks cannot distinguish direct gene interactions from indirect interactions via others such as the effect of epigenetic events to gene activity. There is an increasing evidence of inter-individual variations not only in gene expression but also in epigenetic events such as DNA methylation. In this paper we propose a new method for constructing a cancer patient-specific gene correlation network using both gene expression and DNA methylation data. We derive a patient-specific network from differential second-order partial correlations of gene expression and DNA methylation between normal samples and the patient sample. The network represents direct interactions between genes by controlling the effect of DNA methylation. Using this method, we constructed 4,000 patient-specific networks for 10 types of cancer. The networks are highly effective in classifying different types of cancer and in deriving potential prognostic gene pairs. In particular, potential prognostic gene pairs derived from the networks were powerful in predicting the survival time of cancer patients. This approach will help identify patient-specific gene correlations and predict prognosis of cancer patients.

Constructing Integrative ceRNA Networks and Finding Prognostic Biomarkers in Renal Cell Carcinoma.

Inspired by a newly discovered gene regulation mechanism known as competing endogenous RNA (ceRNA) interactions, several computational methods have been proposed to generate ceRNA networks. However, most of these methods have focused on deriving restricted types of ceRNA interactions such as lncRNA-miRNA-mRNA interactions. Competition for miRNA-binding occurs not only between lncRNAs and mRNAs but also between lncRNAs or between mRNAs. Furthermore, a large number of pseudogenes also act as ceRNAs, thereby regulate other genes. In this study, we developed a general method for constructing integrative networks of all possible interactions of ceRNAs in renal cell carcinoma (RCC). From the ceRNA networks we derived potential prognostic biomarkers, each of which is a triplet of two ceRNAs and miRNA (i.e., ceRNA-miRNA-ceRNA). Interestingly, some prognostic ceRNA triplets do not include mRNA at all, and consist of two non-coding RNAs and miRNA, which have been rarely known so far. Comparison of the prognostic ceRNA triplets to known prognostic genes in RCC showed that the triplets have a better predictive power of survival rates than the known prognostic genes. Our approach will help us construct integrative networks of ceRNAs of all types and find new potential prognostic biomarkers in cancer.

DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi-C contact maps. Some available orthogonal data ChIA-PET/HiChIP and Capture Hi-C were used to generate positive samples with a wider contact matrix which provides the possibility to find more potential genome-wide chromatin loops. The experimental results demonstrate that DLoopCaller effectively improves the accuracy of predicting genome-wide chromatin loops compared to the state-of-the-art method Peakachu. Moreover, compared to two of most popular loop callers, such as HiCCUPS and Fit-Hi-C, DLoopCaller identifies some unique interactions. We conclude that a combination of chromatin landscapes on the one-dimensional genome contributes to understanding the 3D genome organization, and the identified chromatin loops reveal cell-type specificity and transcription factor motif co-enrichment across different cell lines and species.

Chemical Profiles and Antiobesity Effect of a Mixture of Astragalus membranaceus and Lithospermum erythrorhizon Extract in High Fat Diet Fed Mice.

The present study aimed to evaluate the antiobesity potential and synergistic effects of ALM16, a mixture of Astragalus membranaceus (AM) and Lithospermum erythrorhizon (LE) extracts, in HFD-induced obese mice. C57BL/6 mice were fed a normal diet (ND), high-fat diet (HFD), HFD + AM, HFD + LE or HFD + ALM16 (50, 100, and 200 mg/kg) daily for 5 weeks. Compared to the ND group, HFD-fed mice showed significant increases in body weight, food efficiency ratio, weights of white adipose tissues, adipocytes size, liver weight, and hepatic steatosis grade. However, ALM16 significantly reduced those increases induced by HFD. Moreover, as compared to the HFD group, the ALM16 group significantly ameliorated serum levels of lipid profiles (TG, TC, HDL, and LDL), adipokines (leptin and adiponectin), and liver damage markers (AST and ALT levels). Notably, ALM16 was more effective than AM or LE alone and had a similar or more potent effect than Garcinia cambogia extracts, as a positive control, at the same dose. These results demonstrate that ALM16 synergistically exerts anti-obesity effects based on complementary interactions between each component. Also, metabolic profiling between each extract and the ALM16 was confirmed by UPLC-QTOF/MS, and the difference was confirmed by relative quantification.

Comparative Analysis of Gene Correlation Networks of Breast Cancer Patients Based on Mutations in TP53.

Breast cancer is one of the most prevalent cancers in females, with more than 450,000 deaths each year worldwide. Among the subtypes of breast cancer, basal-like breast cancer, also known as triple-negative breast cancer, shows the lowest survival rate and does not have effective treatments yet. Somatic mutations in the TP53 gene frequently occur across all breast cancer subtypes, but comparative analysis of gene correlations with respect to mutations in TP53 has not been done so far. The primary goal of this study is to identify gene correlations in two groups of breast cancer patients and to derive potential prognostic gene pairs for breast cancer. We partitioned breast cancer patients into two groups: one group with a mutated TP53 gene (mTP53) and the other with a wild-type TP53 gene (wtTP53). For every gene pair, we computed the hazard ratio using the Cox proportional hazard model and constructed gene correlation networks (GCNs) enriched with prognostic information. Our GCN is more informative than typical GCNs in the sense that it indicates the type of correlation between genes, the concordance index, and the prognostic type of a gene. Comparative analysis of correlation patterns and survival time of the two groups revealed several interesting findings. First, we found several new gene pairs with opposite correlations in the two GCNs and the difference in their correlation patterns was the most prominent in the basal-like subtype of breast cancer. Second, we obtained potential prognostic genes for breast cancer patients with a wild-type TP53 gene. From a comparative analysis of GCNs of mTP53 and wtTP53, we found several gene pairs that show significantly different correlation patterns in the basal-like breast cancer subtype and obtained prognostic genes for patients with a wild-type TP53 gene. The GCNs and prognostic genes identified in this study will be informative for the prognosis of survival and for selecting a drug target for breast cancer, in particular for basal-like breast cancer. To the best of our knowledge, this is the first attempt to construct GCNs for breast cancer patients with or without mutations in the TP53 gene and to find prognostic genes accordingly.

Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions.

BACKGROUND: Lymph node metastasis is usually detected based on the images obtained from clinical examinations. Detecting lymph node metastasis from clinical examinations is a direct way of diagnosing metastasis, but the diagnosis is done after lymph node metastasis occurs. RESULTS: We developed a new method for predicting lymph node metastasis based on differential correlations of miRNA-mediated RNA interactions in cancer. The types of RNAs considered in this study include mRNAs, lncRNAs, miRNAs, and pseudogenes. We constructed cancer patient-specific networks of miRNA mediated RNA interactions and identified key miRNA-RNA pairs from the network. A prediction model using differential correlations of the miRNA-RNA pairs of a patient as features showed a much higher performance than other methods which use gene expression data. The key miRNA-RNA pairs were also powerful in predicting prognosis of an individual patient in several types of cancer. CONCLUSIONS: Differential correlations of miRNA-RNA pairs identified from patient-specific networks of miRNA mediated RNA interactions are powerful in predicting lymph node metastasis in cancer patients. The key miRNA-RNA pairs were also powerful in predicting prognosis of an individual patient of solid cancer.

A Deep Learning Model for RNA-Protein Binding Preference Prediction Based on Hierarchical LSTM and Attention Network.

Attention mechanism has the ability to find important information in the sequence. The regions of the RNA sequence that can bind to proteins are more important than those that cannot bind to proteins. Neither conventional methods nor deep learning-based methods, they are not good at learning this information. In this study, LSTM is used to extract the correlation features between different sites in RNA sequence. We also use attention mechanism to evaluate the importance of different sites in RNA sequence. We get the optimal combination of k-mer length, k-mer stride window, k-mer sentence length, k-mer sentence stride window, and optimization function through hyper-parm experiments. The results show that the performance of our method is better than other methods. We tested the effects of changes in k-mer vector length on model performance. We show model performance changes under various k-mer related parameter settings. Furthermore, we investigate the effect of attention mechanism and RNA structure data on model performance.

A New Approach to Deriving Prognostic Gene Pairs From Cancer Patient-Specific Gene Correlation Networks.

Many of the known prognostic gene signatures for cancer are individual genes or combination of genes, found by the analysis of microarray data. However, many of the known cancer signatures are less predictive than random gene expression signatures, and such random signatures are significantly associated with proliferation genes. With the availability of RNA-seq gene expression data for thousands of human cancer patients, we have analyzed RNA-seq and clinical data of cancer patients and constructed gene correlation networks specific to individual cancer patients. From the patient-specific gene correlation networks, we derived prognostic gene pairs for three types of cancer. In this paper, we propose a new method for inferring prognostic gene pairs from patient-specific gene correlation networks. The main difference of our method from previous ones includes (1) it is focused on finding prognostic gene pairs rather than prognostic genes, (2) it can identify prognostic gene pairs from RNA-seq data even when no significant prognostic genes exist, and (3) prognostic gene pairs can serve as robust prognostic biomarkers in the sense that most prognostic gene pairs show little association with proliferation genes, the major boosting factor of the predictive power of random gene signatures. Evaluation of our method with extensive data of three types of cancer (liver cancer, pancreatic cancer, and stomach cancer) showed that our approach is general and that gene pairs can serve as more reliable prognostic signatures for cancer than genes. Analysis of patient-specific gene networks suggests that prognosis of individual cancer patients is affected by the existence of prognostic gene pairs in the patient-specific network and by the size of the patient-specific network. Although preliminary, our approach will be useful for finding gene pairs to predict survival time of patients and to tailor treatments to individual characteristics. The program for dynamically constructing patient-specific gene networks and for finding prognostic gene pairs is available at http://bclab.inha.ac.kr/LPS.

GeneCoNet: A web application server for constructing cancer patient-specific gene correlation networks with prognostic gene pairs.

BACKGROUND AND OBJECTIVE: Most prognostic gene signatures that have been known for cancer are either individual genes or combination of genes. Both individual genes and combination of genes do not provide information on gene-gene relations, and often have less prognostic significance than random genes associated with cell proliferation. Several methods for generating sample-specific gene networks have been proposed, but programs implementing the methods are not publicly available. METHODS: We have developed a method that builds gene correlation networks specific to individual cancer patients and derives prognostic gene correlations from the networks. A gene correlation network specific to a patient is constructed by identifying gene-gene relations that are significantly different from normal samples. Prognostic gene pairs are obtained by carrying out the Cox proportional hazards regression and the log-rank test for every gene pair. RESULTS: We built a web application server called GeneCoNet with thousands of tumor samples in TCGA. Given a tumor sample ID of TCGA, GeneCoNet dynamically constructs a gene correlation network specific to the sample as output. As an additional output, it provides information on prognostic gene correlations in the network. GeneCoNet found several prognostic gene correlations for six types of cancer, but there were no prognostic gene pairs common to multiple cancer types. CONCLUSION: Extensive analysis of patient-specific gene correlation networks suggests that patients with a larger subnetwork of prognostic gene pairs have shorter survival time than the others and that patients with a subnetwork that contains more genes participating in prognostic gene pairs have shorter survival time than the others. GeneCoNet can be used as a valuable resource for generating gene correlation networks specific to individual patients and for identifying prognostic gene correlations. It is freely accessible at http://geneconet.inha.ac.kr.

Multi-Scale Capsule Network for Predicting DNA-Protein Binding Sites.

Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analysis of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) have been introduced to motif discovery task and have achieved state-of-art performance. However, due to the limitations of CNN, motif discovery methods based on CNN do not take full advantage of large-scale sequencing data generated by high-throughput sequencing technology. Hence, in this paper we propose multi-scale capsule network architecture (MSC) integrating multi-scale CNN, a variant of CNN able to extract motif features of different lengths, and capsule network, a novel type of artificial neural network architecture aimed at improving CNN. The proposed method is tested on real ChIP-seq datasets and the experimental results show a considerable improvement compared with two well-tested deep learning-based sequence model, DeepBind and Deepsea.

Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme.

The rapid development of high-throughput sequencing technology provides unique opportunities for studying of transcription factor binding sites, but also brings new computational challenges. Recently, a series of discriminative motif discovery (DMD) methods have been proposed and offer promising solutions for addressing these challenges. However, because of the huge computation cost, most of them have to choose approximate schemes that either sacrifice the accuracy of motif representation or tune motif parameter indirectly. In this paper, we propose a bag-based classifier combined with a multi-fold learning scheme (BCMF) to discover motifs from ChIP-seq datasets. First, BCMF formulates input sequences as a labeled bag naturally. Then, a bag-based classifier, combining with a bag feature extracting strategy, is applied to construct the objective function, and a multi-fold learning scheme is used to solve it. Compared with the existing DMD tools, BCMF features three improvements: 1) Learning position weight matrix (PWM) directly in a continuous space; 2) Proposing to represent a positive bag with a feature fused by its k "most positive" patterns. 3) Applying a more advanced learning scheme. The experimental results on 134 ChIP-seq datasets show that BCMF substantially outperforms existing DMD methods (including DREME, HOMER, XXmotif, motifRG, EDCOD and our previous work).

Serum Metabonomic Research of the Anti-Hypertensive Effects of Ogaja on Spontaneously Hypertensive Rats.

Our previous studies have shown that Ogaja Acanthopanax sessiliflorus has an important role in decreasing blood pressure, but its biochemical change characteristic has not been clarified completely at the metabolic level. Therefore, in this study, a combination method of nuclear magnetic resonance (NMR) spectroscopy-based metabonomics and multivariate statistical analyses was employed to explore the metabolic changes of serum samples from spontaneously hypertensive rats treated with Ogaja extracts. In the results of multivariate statistical analysis, the spontaneously hypertensive rat (SHR) groups treated with Ogaja were separated from the SHR group. The group of SHR treated with 200 mg/kg Ogaja was clustered with the positive control (captopril) group, and the 400 and 600 mg/kg Ogaja treatment SHR groups were clustered together. Quantified metabolites were statistically analyzed to find the metabolites showing the effects of Ogaja. Succinate and betaine had variable importance in projection (VIP) scores over 2.0. Succinate, which is related to renin release, and betaine, which is related to lowering blood pressure, increased dose-dependently.

First Report of Verticillium Wilt Caused by Verticillium dahliae Infection on Chinese Cabbage in Korea.

Chinese cabbage (Brassica rapa L.) is one of the most important vegetables in Korea due to its role as the main ingredient for the making of Kimchi. In June 2014, disease symptoms of leaves wilt, dry, and drop off on Chinese cabbage were observed in a Chinese cabbage farm located at Taebeak (37°26'50.7"N 128°95'50.0"E), Gangwon province, Korea. This disease was observed on approximately 35% of the plants in the field, causing an almost 10% decrease in total production. At the early stage of infection, the color at the edge of the plant foliage changed from green to yellow. As the disease progressed, infected leaves wilted, dried off, and detached from the plant. Soft rot that occurred at the base of the leaf stem and root tissues caused the infected leaves to dry and fell off the plant. To identify the causal agent, a small piece of infected leaf tissues was sterilized with 1% sodium hypochlorite solution for 1 min and rinsed with sterile water before it was transferred onto potato dextrose agar (PDA) media. The plates were then incubated at 25°C for 10 days in the dark. Fungal colonies grown on PDA media were of white-creamy in color with an abundance of mycelia and later develop into black color due to the formation of microsclerotia embedded in the media. Microscopic examination showed conidiophores and phialides were both appeared in a verticillate arrangement, whereas conidia were hyaline, smooth-walled, and ellipsoidal to oval with average size 5.4×2.5 µm (n=100). Microsclerotia appeared in elongate to an irregularly spherical shape and greatly variable in size. The morphological attributes of the fungal isolate described above were comparable to the characteristics of Verticillium dahliae Kleb. (V. dahliae) described by Hawksworth and Talboys (1970), and V. dahliae isolated from Chinese cabbage in Japan reported in Kishi (1998). Pathogenicity test was performed by soaking twelve individual Chinese cabbage seedlings for 15 min into fungal pathogen conidial suspension (1x106 conidium/ml) before transferred into soil tray. The same number of non-inoculated seedlings on the soil tray was used as a control. Inoculated and control plants were then covered with a plastic bag for 24 hours to maintain high humidity before transferred into the greenhouse (25°C). Seven days post-inoculation (dpi), treated plant leaves turned yellow, and soft rot was observed. At 10-dpi, plant leaf tissues dried off and severe soft rot occurred. Pathogenicity test was repeated three times and consistent results were obtained. The re-isolated fungal pathogen from the inoculated plants showed identical morphological characteristics to the original isolates, thus fulfilling Koch's postulates. For further identification, PCR amplification targeting Internal Transcribed Spacer (ITS) and RNA polymerase II gene (RPB2) regions were performed (Liu et al., 1999; White et al., 1990). Each PCR product was sequenced and deposited in the GenBank under the accession LC549667 and LC061275, respectively. Sequence analysis using BLAST showed that the nucleotide sequences of ITS and RPB2 DNA fragments are 99-100% identical to the reference strain of V. dahliae available in the NCBI database (MG585719, HE972023, XM_009652520 and DQ522468, respectively). Therefore, based on the results of morphological and molecular analyses, the fungal pathogen isolated from Chinese cabbage in this study was identified as V. dahliae and deposited in the National Institute of Horticultural and Herbal Science germplasm collection (NIHHS 13-252). Recently, due to high demand and a more competitive price, more Chrysanthemum farmers in Korea switch their crops to Chinese cabbage. Interestingly, the occurrence of V. dahliae infection was also reported to occur in Chrysanthemum plants in Korea (Han et al. 2007), which indicates a serious problem ahead to these farmers. Therefore, in this current study, the identification of V. dahliae pathogenic to Chinese cabbage will provide vital knowledge for the development of disease management strategies to minimize the loss of crop production. To our knowledge, this is the first report that V. dahliae causes Verticillium wilt disease on Chinese cabbage in Korea.

Constructing cancer patient-specific and group-specific gene networks with multi-omics data.

BACKGROUND: Cancer is a complex and heterogeneous disease with many possible genetic and environmental causes. The same treatment for patients of the same cancer type often results in different outcomes in terms of efficacy and side effects of the treatment. Thus, the molecular characterization of individual cancer patients is increasingly important to find an effective treatment. Recently a few methods have been developed to construct cancer sample-specific gene networks based on the difference in the mRNA expression levels between the cancer sample and reference samples. METHODS: We constructed a patient-specific network with multi-omics data based on the difference between a reference network and a perturbed reference network by the patient. A network specific to a group of patients was obtained using the average change in correlation coefficients and node degree of patient-specific networks of the group. RESULTS: In this paper, we present a new method for constructing cancer patient-specific and group-specific gene networks with multi-omics data. The main differences of our method from previous ones are as follows: (1) networks are constructed with multi-omics (mRNA expression, copy number variation, DNA methylation and microRNA expression) data rather than with mRNA expression data alone, (2) background networks are constructed with both normal samples and cancer samples of the specified type to extract cancer-specific gene correlations, and (3) both patient individual-specific networks and patient group-specific networks can be constructed. The results of evaluating our method with several types of cancer show that it constructs more informative and accurate gene networks than previous methods. CONCLUSIONS: The results of evaluating our method with extensive data of seven cancer types show that the difference of gene correlations between the reference samples and a patient sample is a more predictive feature than mRNA expression levels and that gene networks constructed with multi-omics data show a better performance than those with single omics data in predicting cancer for most cancer types. Our approach will be useful for finding genes and gene pairs to tailor treatments to individual characteristics.

Discovering protein-binding RNA motifs with a generative model of RNA sequences.

Recent advances in high-throughput experimental technologies have generated a huge amount of data on interactions between proteins and nucleic acids. Motivated by the big experimental data, several computational methods have been developed either to predict binding sites in a sequence or to determine if an interaction exists between protein and nucleic acid sequences. However, most of the methods cannot be used to discover new nucleic acid sequences that bind to a target protein because they are classifiers rather than generators. In this paper we propose a generative model for constructing protein-binding RNA sequences and motifs using a long short-term memory (LSTM) neural network. Testing the model for several target proteins showed that RNA sequences generated by the model have high binding affinity and specificity for their target proteins and that the protein-binding motifs derived from the generated RNA sequences are comparable to the motifs from experimentally validated protein-binding RNA sequences. The results are promising and we believe this approach will help design more efficient in vitro or in vivo experiments by suggesting potential RNA aptamers for a target protein.