Information maximization-based clustering of histopathology images using deep learning.

Pancreatic cancer is one of the most adverse diseases and it is very difficult to treat because the cancer cells formed in the pancreas intertwine themselves with nearby blood vessels and connective tissue. Hence, the surgical procedure of treatment becomes complicated and it does not always lead to a cure. Histopathological diagnosis is the usual approach for cancer diagnosis. However, the pancreas remains so deep inside the body that experts sometimes struggle to detect cancer in it. Computer-aided diagnosis can come to the aid of pathologists in this scenario. It assists experts by supporting their diagnostic decisions. In this research, we carried out a deep learning-based approach to analyze histopathology images. We collected whole-slide images of KPC mice to implement this work. The pancreatic abnormalities observed in KPC mice develop similar histological features to human beings. We created random patches from whole-slide images. Then, a convolutional autoencoder framework was used to embed these patches into an integrated latent space. We applied 'information maximization', a deep learning clustering technique to cluster the identical patches in an unsupervised manner since our dataset does not have annotation. Moreover, Uniform manifold approximation and projection, a nonlinear dimension reduction technique was utilized to visualize the embedded patches in a 2-dimensional space. Finally, we calculated a few internal cluster validation metrics to determine the optimal cluster set. Our work concentrated on patch-based anomaly detection in the whole slide histopathology images of KPC mice.

Ensemble Machine Learning Approaches Based on Molecular Descriptors and Graph Convolutional Networks for Predicting the Efflux Activities of MDR1 and BCRP Transporters.

Multidrug resistance (MDR1) and breast cancer resistance protein (BCRP) play important roles in drug absorption and distribution. Computational prediction of substrates for both transporters can help reduce time in drug discovery. This study aimed to predict the efflux activity of MDR1 and BCRP using multiple machine learning approaches with molecular descriptors and graph convolutional networks (GCNs). In vitro efflux activity was determined using MDR1- and BCRP-expressing cells. Predictive performance was assessed using an in-house dataset with a chronological split and an external dataset. CatBoost and support vector regression showed the best predictive performance for MDR1 and BCRP efflux activities, respectively, of the 25 descriptor-based machine learning methods based on the coefficient of determination (R2). The single-task GCN showed a slightly lower performance than descriptor-based prediction in the in-house dataset. In both approaches, the percentage of compounds predicted within twofold of the observed values in the external dataset was lower than that in the in-house dataset. Multi-task GCN did not show any improvements, whereas multimodal GCN increased the predictive performance of BCRP efflux activity compared with single-task GCN. Furthermore, the ensemble approach of descriptor-based machine learning and GCN achieved the highest predictive performance with R2 values of 0.706 and 0.587 in MDR1 and BCRP, respectively, in time-split test sets. This result suggests that two different approaches to represent molecular structures complement each other in terms of molecular characteristics. Our study demonstrated that predictive models using advanced machine learning approaches are beneficial for identifying potential substrate liability of both MDR1 and BCRP.

On QSAR-based cardiotoxicity modeling with the expressiveness-enhanced graph learning model and dual-threshold scheme.

Introduction: Given the direct association with malignant ventricular arrhythmias, cardiotoxicity is a major concern in drug design. In the past decades, computational models based on the quantitative structure-activity relationship have been proposed to screen out cardiotoxic compounds and have shown promising results. The combination of molecular fingerprint and the machine learning model shows stable performance for a wide spectrum of problems; however, not long after the advent of the graph neural network (GNN) deep learning model and its variant (e.g., graph transformer), it has become the principal way of quantitative structure-activity relationship-based modeling for its high flexibility in feature extraction and decision rule generation. Despite all these progresses, the expressiveness (the ability of a program to identify non-isomorphic graph structures) of the GNN model is bounded by the WL isomorphism test, and a suitable thresholding scheme that relates directly to the sensitivity and credibility of a model is still an open question. Methods: In this research, we further improved the expressiveness of the GNN model by introducing the substructure-aware bias by the graph subgraph transformer network model. Moreover, to propose the most appropriate thresholding scheme, a comprehensive comparison of the thresholding schemes was conducted. Results: Based on these improvements, the best model attains performance with 90.4% precision, 90.4% recall, and 90.5% F1-score with a dual-threshold scheme (active: <1µM; non-active: >30µM). The improved pipeline (graph subgraph transformer network model and thresholding scheme) also shows its advantages in terms of the activity cliff problem and model interpretability.

Learning vector quantized representation for cancer subtypes identification.

BACKGROUND AND OBJECTIVE: Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recalibrated as a result of our deepened understanding. During this recalibration, researchers often rely on clustering of cancer data to provide an intuitive visual reference that could reveal the intrinsic characteristics of subtypes. The data being clustered are often omics data such as transcriptomics that have strong correlations to the underlying biological mechanism. However, while existing studies have shown promising results, they suffer from issues associated with omics data: sample scarcity and high dimensionality while they impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations. METHODS: This paper proposes to leverage a recent strong generative model, Vector-Quantized Variational AutoEncoder, to tackle the data issues and extract discrete representations that are crucial to the quality of subsequent clustering by retaining only information relevant to reconstructing the input. RESULTS: Extensive experiments and medical analysis on multiple datasets comprising 10 distinct cancers demonstrate the proposed clustering results can significantly and robustly improve prognosis over prevalent subtyping systems. CONCLUSION: Our proposal does not impose strict assumptions on data distribution; while, its latent features are better representations of the transcriptomic data in different cancer subtypes, capable of yielding superior clustering performance with any mainstream clustering method.

Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data.

Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and classification of cancer subtypes are indispensable for effective clinical treatment. Promising results on automatic cancer subtyping systems have been published recently with the emergence of various deep learning methods. However, such automatic systems often overfit the data due to the high dimensionality and scarcity. In this paper, we propose to investigate automatic subtyping from an unsupervised learning perspective by directly constructing the underlying data distribution itself, hence sufficient data can be generated to alleviate the issue of overfitting. Specifically, we bypass the strong Gaussianity assumption that typically exists but fails in the unsupervised learning subtyping literature due to small-sized samples by vector quantization. Our proposed method better captures the latent space features and models the cancer subtype manifestation on a molecular basis, as demonstrated by the extensive experimental results.

Evaluation of [18F]F-DPA PET for Detecting Microglial Activation in the Spinal Cord of a Rat Model of Neuropathic Pain.

PURPOSE: Recent studies have linked activated spinal glia to neuropathic pain. Here, using a positron emission tomography (PET) scanner with high spatial resolution and sensitivity, we evaluated the feasibility and sensitivity of N,N-diethyl-2-(2-(4-([18F]fluoro)phenyl)-5,7-dimethylpyrazolo[1,5-a] pyrimidin-3-yl)acetamide ([18F]F-DPA) imaging for detecting spinal cord microglial activation after partial sciatic nerve ligation (PSNL) in rats. PROCEDURES: Neuropathic pain was induced in rats (n = 20) by PSNL, and pain sensation tests were conducted before surgery and 3 and 7 days post-injury. On day 7, in vivo PET imaging and ex vivo autoradiography were performed using [18F]F-DPA or [11C]PK11195. Ex vivo biodistribution and PET imaging of the removed spinal cord were carried out with [18F]F-DPA. Sham-operated and PK11195-pretreated animals were also examined. RESULTS: Mechanical allodynia was confirmed in the PSNL rats from day 3 through day 7. Ex vivo autoradiography showed a higher lesion-to-background uptake with [18F]F-DPA compared with [11C]PK11195. Ex vivo PET imaging of the removed spinal cord showed [18F]F-DPA accumulation in the inflammation site, which was immunohistochemically confirmed to coincide with microglia activation. Pretreatment with PK11195 eliminated the uptake. The SUV values of in vivo [18F]F-DPA and [11C]PK11195 PET were not significantly increased in the lesion compared with the reference region, and were fivefold higher than the values obtained from the ex vivo data. Ex vivo biodistribution revealed a twofold higher [18F]F-DPA uptake in the vertebral body compared to that seen in the bone from the skull. CONCLUSIONS: [18F]F-DPA aided visualization of the spinal cord inflammation site in PSNL rats on ex vivo autoradiography and was superior to [11C]PK11195. In vivo [18F]F-DPA PET did not allow for visualization of tracer accumulation even using a high-spatial-resolution PET scanner. The main reason for this result was due to insufficient SUVs in the spinal cord region as compared with the background noise, in addition to a spillover from the vertebral body.

Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach.

BACKGROUND: Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset. METHODS: We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher's exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher's exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters. RESULTS: The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

The feasibility of predicting impending malignant ventricular arrhythmias by using nonlinear features of short heartbeat intervals.

BACKGROUND AND OBJECTIVE: Malignant ventricular arrhythmias (MAs) occur unpredictably and lead to emergencies. A new approach that uses a timely tracking device e.g., photoplethysmogram (PPG) solely to predict MAs would be irreplaceably valuable and it is natural to expect the approach can predict the occurrence as early as possible. METHOD: We assumed that with an appropriate metric based on signal complexity, the heartbeat interval time series (HbIs) can be used to manifest the intrinsic characteristics of the period immediately precedes the MAs (preMAs). The approach first characterizes the patterns of preMAs by a new complexity metric (the refined composite multi-scale entropy). The MAs detector is then constructed by checking the discriminability of the MAs against the sinus rhythm and other prevalent arrhythmias (atrial fibrillation and premature ventricular contraction) of three machine-learning models (SVM, Random Forest, and XGboost). RESULTS: Two specifications are of interest: the length of the HbIs needed to delineate the preMAs patterns sufficiently (lspec) and how long before the occurrence of MAs will the HbIs manifest specific patterns that are distinct enough to predict the impending MAs (tspec). Our experimental results confirmed the best performance came from a Random-Forest model with an average precision of 99.99% and recall of 88.98% using a HbIs of 800 heartbeats (the lspec), 108 seconds (the tspec) before the occurrence of MAs. CONCLUSION: By experimental validation of the unique pattern of the preMAs in HbIs and using it in the machine learning model, we showed the high possibility of MAs prediction in a broader circumstance, which may cover daily healthcare using the alternative sensor in HbIs monitoring. Therefore, this research is theoretically and practically significant in cardiac arrest prevention.

Examination of the regression model to quantify the degree of low back pain and lower limb symptoms in patients with lumbar disc herniation by the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire (JOABPEQ).

The Japanese Orthopedic Association Back Pain Evaluation Questionnaire (JOABPEQ) was created to evaluate specific treatment outcomes in terms of physical functioning, social ability, and mental health in patients with back pain-related diseases. In this study, we investigated whether the JOABPEQ could be used to construct a regression model to quantify low back pain and lower limb symptoms in patients with lumbar disc herniation (LDH). We reviewed 114 patients with LDH scheduled to undergo surgery at our hospital. We measured the degrees of 1) lower back pain, 2) lower limb pain, and 3) lower limb numbness using the visual analog scale before the surgery. All answers and physical function data were subjected to partial least squares regression analysis. The degrees of lower back and lower limb pain could be used as a regression model from the JOABPEQ and had a significant causal relationship with them. However, the degree of lower limb numbness could not be used for the same. Based on our results, the questions of the JOABPEQ can be used to multilaterally understand the degree of lower back pain and lower limb pain in patients with LDH. However, the degree of lower limb numbness has no causal relationship, so actual measurement is essential.

Graph cluster approach in identifying novel proteins and significant pathways involved in polycystic ovary syndrome.

RESEARCH QUESTION: Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with diverse clinical implications, such as infertility, metabolic disorders, cardiovascular diseases and psychological problems among others. The heterogeneity of conditions found in PCOS contribute to its various phenotypes, leading to difficulties in identifying proteins involved in this abnormality. Several studies, however, have shown the feasibility in identifying molecular evidence underlying other diseases using graph cluster analysis. Therefore, is it possible to identify proteins and pathways related to PCOS using the same approach? METHODS: Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways. RESULTS: The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity. CONCLUSIONS: These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.

Curcumin Derivatives Verify the Essentiality of ROS Upregulation in Tumor Suppression.

BACKGROUND: Curcumin has been shown to exert pleiotropic biological effects, including anti-tumorigenic activity. We previously showed that curcumin controls reactive oxygen species (ROS) levels through the ROS metabolic enzymes, to prevent tumor cell growth. In this study, we synthesized 39 novel curcumin derivatives and examined their anti-proliferative and anti-tumorigenic properties. METHODS AND RESULTS: Thirty-nine derivatives exhibited anti-proliferative activity toward human cancer cell lines, including CML-derived K562 leukemic cells, in a manner sensitive to an antioxidant, N-acetyl-cysteine (NAC). Some compounds exhibited lower GI50 values than curcumin, some efficiently induced cell senescence, and others markedly increased ROS levels, efficiently induced cell death and suppressed tumor formation in a xenograft mouse model, without any detectable side effects. A clustering analysis of the selected compounds and their measurement variables revealed that anti-tumorigenic activity was most well-correlated with an increase in ROS levels. Pulldown assays and a molecular docking analysis showed that curcumin derivatives competed with co-enzymes to bind to the respective ROS metabolic enzymes and inhibited their enzymatic activities. CONCLUSIONS: The analysis of novel curcumin derivatives established the importance of ROS upregulation in suppression of tumorigenesis, and these compounds are potentially useful for the development of an anti-cancer drug with few side effects.

Pentagamavunon-1 (PGV-1) inhibits ROS metabolic enzymes and suppresses tumor cell growth by inducing M phase (prometaphase) arrest and cell senescence.

We previously showed that curcumin, a phytopolyphenol found in turmeric (Curcuma longa), targets a series of enzymes in the ROS metabolic pathway, induces irreversible growth arrest, and causes apoptosis. In this study, we tested Pentagamavunon-1 (PGV-1), a molecule related to curcumin, for its inhibitory activity on tumor cells in vitro and in vivo. PGV-1 exhibited 60 times lower GI50 compared to that of curcumin in K562 cells, and inhibited the proliferation of cell lines derived from leukemia, breast adenocarcinoma, cervical cancer, uterine cancer, and pancreatic cancer. The inhibition of growth by PGV-1 remained after its removal from the medium, which suggests that PGV-1 irreversibly prevents proliferation. PGV-1 specifically induced prometaphase arrest in the M phase of the cell cycle, and efficiently induced cell senescence and cell death by increasing intracellular ROS levels through inhibition of ROS-metabolic enzymes. In a xenograft mouse model, PGV-1 had marked anti-tumor activity with little side effects by oral administration, whereas curcumin rarely inhibited tumor formation by this administration. Therefore, PGV-1 is a potential therapeutic to induce tumor cell apoptosis with few side effects and low risk of relapse.

The Feasibility of Arrhythmias Detection from A Capacitive ECG Measurement Using Convolutional Neural Network.

Capacitive ECG (cECG) can measure the cardiac electrical signal via capacitive coupling between electrodes and skin. This unconstrained measurement is suitable for personal heart monitoring; however, the instability in the quality of the signal hinders a further use of the signal. To use the cECG for heart monitoring, an adapted framework that could automatically classify the signal into clear cECG, arrhythmias and noise signal is a prerequisite. In view of this problem, the conventional quality estimation method using predefined features based on R-peak detection is not suitable for this unconstrained measurement of cECG. In this study, we examine the feasibility of arrhythmias detection from the cECG measurement using a convolutional neural network (CNN) model. The malignant ventricular tachycardia (VT) and ventricular fibrillation (VF) do not have the Q-R-S waveforms and therefore may be easily classified as the noise. Hence, in this study, we used the cECG signals that have 3 classes in quality (C1: clear signal; C2: blurry signal with significant R peak and N: noise) and the arrhythmias signals (VT, VF, and atrial fibrillation) from open databases to train the classification model. 13 subjects were recruited in an experiment for the cECG data collection in the Nara Institute of Science and Technology. As a result, the CNN model could recognize C1 and AF signal with over 0.98 recalls and precisions; whereas the recall and precision of VT and VF are lower scores and the lower scores were caused mainly by the similarity between VT and VF. Given the results of the CNN model, this CNN-based framework can accurately label the C1, AF, and malignant ventricular arrhythmias (VT and VF) signals. Further stratification of the C2, VT, and VF will further enhance the use of the cECG measurement.

Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks.

PURPOSE: Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this paper, we developed a method for classifying subtypes of lung adenocarcinoma from pathological images using neural network whose that can evaluate phenotypic features from wider area to consider cellular distributions. METHODS: In order to recognize the types of tumors, we need not only to detail features of cells, but also to incorporate statistical distribution of the different types of cells. Variants of autoencoders as building blocks of pre-trained convolutional layers of neural networks are implemented. A sparse deep autoencoder which minimizes local information entropy on the encoding layer is then proposed and applied to images of size [Formula: see text]. We applied this model for feature extraction from pathological images of lung adenocarcinoma, which is comprised of three transcriptome subtypes previously defined by the Cancer Genome Atlas network. Since the tumor tissue is composed of heterogeneous cell populations, recognition of tumor transcriptome subtypes requires more information than local pattern of cells. The parameters extracted using this approach will then be used in multiple reduction stages to perform classification on larger images. RESULTS: We were able to demonstrate that these networks successfully recognize morphological features of lung adenocarcinoma. We also performed classification and reconstruction experiments to compare the outputs of the variants. The results showed that the larger input image that covers a certain area of the tissue is required to recognize transcriptome subtypes. The sparse autoencoder network with [Formula: see text] input provides a 98.9% classification accuracy. CONCLUSION: This study shows the potential of autoencoders as a feature extraction paradigm and paves the way for a whole slide image analysis tool to predict molecular subtypes of tumors from pathological features.

Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer.

The dynamic balance of import and export of zinc in Escherichia coli suggests a heterogeneous population response to stress.

Zinc is essential for life, but toxic in excess. Thus all cells must control their internal zinc concentration. We used a systems approach, alternating rounds of experiments and models, to further elucidate the zinc control systems in Escherichia coli. We measured the response to zinc of the main specific zinc import and export systems in the wild-type, and a series of deletion mutant strains. We interpreted these data with a detailed mathematical model and Bayesian model fitting routines. There are three key findings: first, that alternate, non-inducible importers and exporters are important. Second, that an internal zinc reservoir is essential for maintaining the internal zinc concentration. Third, our data fitting led us to propose that the cells mount a heterogeneous response to zinc: some respond effectively, while others die or stop growing. In a further round of experiments, we demonstrated lower viable cell counts in the mutant strain tested exposed to excess zinc, consistent with this hypothesis. A stochastic model simulation demonstrated considerable fluctuations in the cellular levels of the ZntA exporter protein, reinforcing this proposal. We hypothesize that maintaining population heterogeneity could be a bet-hedging response allowing a population of cells to survive in varied and fluctuating environments.

Integration of residue attributes for sequence diversity characterization of terpenoid enzymes.

Progress in the "omics" fields such as genomics, transcriptomics, proteomics, and metabolomics has engendered a need for innovative analytical techniques to derive meaningful information from the ever increasing molecular data. KNApSAcK motorcycle DB is a popular database for enzymes related to secondary metabolic pathways in plants. One of the challenges in analyses of protein sequence data in such repositories is the standard notation of sequences as strings of alphabetical characters. This has created lack of a natural underlying metric that eases amenability to computation. In view of this requirement, we applied novel integration of selected biochemical and physical attributes of amino acids derived from the amino acid index and quantified in numerical scale, to examine diversity of peptide sequences of terpenoid synthases accumulated in KNApSAcK motorcycle DB. We initially generated a reduced amino acid index table. This is a set of biochemical and physical properties obtained by random forest feature selection of important indices from the amino acid index. Principal component analysis was then applied for characterization of enzymes involved in synthesis of terpenoids. The variance explained was increased by incorporation of residue attributes for analyses.

KNApSAcK Metabolite Activity Database for retrieving the relationships between metabolites and biological activities.

Databases (DBs) are required by various omics fields because the volume of molecular biology data is increasing rapidly. In this study, we provide instructions for users and describe the current status of our metabolite activity DB. To facilitate a comprehensive understanding of the interactions between the metabolites of organisms and the chemical-level contribution of metabolites to human health, we constructed a metabolite activity DB known as the KNApSAcK Metabolite Activity DB. It comprises 9,584 triplet relationships (metabolite-biological activity-target species), including 2,356 metabolites, 140 activity categories, 2,963 specific descriptions of biological activities and 778 target species. Approximately 46% of the activities described in the DB are related to chemical ecology, most of which are attributed to antimicrobial agents and plant growth regulators. The majority of the metabolites with antimicrobial activities are flavonoids and phenylpropanoids. The metabolites with plant growth regulatory effects include plant hormones. Over half of the DB contents are related to human health care and medicine. The five largest groups are toxins, anticancer agents, nervous system agents, cardiovascular agents and non-therapeutic agents, such as flavors and fragrances. The KNApSAcK Metabolite Activity DB is integrated within the KNApSAcK Family DBs to facilitate further systematized research in various omics fields, especially metabolomics, nutrigenomics and foodomics. The KNApSAcK Metabolite Activity DB could also be utilized for developing novel drugs and materials, as well as for identifying viable drug resources and other useful compounds.

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database.

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

Comparative analysis of argK-tox clusters and their flanking regions in phaseolotoxin-producing Pseudomonas syringae pathovars.

DNA fragments containing argK-tox clusters and their flanking regions were cloned from the chromosomes of Pseudomonas syringae pathovar (pv.) actinidiae strain KW-11 (ACT) and P. syringae pv. phaseolicola strain MAFF 302282 (PHA), and then their sequences were determined. Comparative analysis of these sequences and the sequences of P. syringae pv. tomato DC3000 (TOM) (Buell et al., Proc Natl Acad Sci USA 100:10181-10186, 2003) and pv. syringae B728a (SYR) (Feil et al., Proc Natl Acad Sci USA 102:11064-11069, 2005) revealed that the chromosomal backbone regions of ACT and TOM shared a high similarity to each other but presented a low similarity to those of PHA and SYR. Nevertheless, almost-identical DNA regions of about 38 kb were confirmed to be present on the chromosomes of both ACT and PHA, which we named "tox islands." The facts that the GC content of such tox islands was 6% lower than that of the chromosomal backbone regions of P. syringae, and that argK-tox clusters, which are considered to be of exogenous origin based on our previous studies (Sawada et al., J Mol Evol 54:437-457, 2002), were confirmed to be contained within the tox islands, suggested that the tox islands were an exogenous, mobile genetic element inserted into the chromosomes of P. syringae strains. It was also predicted that the tox islands integrated site-specifically into the homologous sites of the chromosomes of ACT and PHA in the same direction, respectively, wherein 34 common gene coding sequences (CDSs) existed. Furthermore, at the left end of the tox islands were three CDSs, which encoded polypeptides and had similarities to the members of the tyrosine recombinase family, suggesting that these putative site-specific recombinases were involved in the recent horizontal transfer of tox islands.