Application of a Two-Dimensional Mapping-Based Visualization Technique: Nutrient-Value-Based Food Grouping.

Worldwide, several food-based dietary guidelines, with diverse food-grouping methods in various countries, have been developed to maintain and promote public health. However, standardized international food-grouping methods are scarce. In this study, we used two-dimensional mapping to classify foods based on their nutrient composition. The Standard Tables of Food Composition in Japan were used for mapping with a novel technique-t-distributed stochastic neighbor embedding-to visualize high-dimensional data. The mapping results showed that most foods formed food group-based clusters in the Standard Tables of Food Composition in Japan. However, the beverages did not form large clusters and demonstrated scattered distribution on the map. Green tea, black tea, and coffee are located within or near the vegetable cluster whereas cocoa is near the pulse cluster. These results were ensured by the k-nearest neighbors. Thus, beverages made from natural materials can be categorized based on their origin. Visualization of food composition could enable an enhanced comprehensive understanding of the nutrients in foods, which could lead to novel aspects of nutrient-value-based food classifications.

On finding natural antibiotics based on TCM formulae.

CONTEXT: Novel kinds of antibiotics are needed to combat the emergence of antibacterial resistance. Natural products (NPs) have shown potential as antibiotic candidates. Current experimental methods are not yet capable of exploring the massive, redundant, and noise-involved chemical space of NPs. In silico approaches are needed to select NPs as antibiotic candidates. OBJECTIVE: This study screens out NPs with antibacterial efficacy guided by both TCM and modern medicine and constructed a dataset aiming to serve the new antibiotic design. METHOD: A knowledge-based network is proposed in this study involving NPs, herbs, the concepts of TCM, and the treatment protocols (or etiologies) of infectious in modern medicine. Using this network, the NPs candidates are screened out and compose the dataset. Feature selection of machine learning approaches is conducted to evaluate the constructed dataset and statistically validate the im- portance of all NPs candidates for different antibiotics by a classification task. RESULTS: The extensive experiments prove the constructed dataset reaches a convincing classification performance with a 0.9421 weighted accuracy, 0.9324 recall, and 0.9409 precision. The further visu- alizations of sample importance prove the comprehensive evaluation for model interpretation based on medical value considerations.

Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds.

The use of herbal medicines in recent decades has increased because their side effects are considered lower than conventional medicine. Unani herbal medicines are often used in Southern Asia. These herbal medicines are usually composed of several types of medicinal plants to treat various diseases. Research on herbal medicine usually focuses on insight into the composition of plants used as ingredients. However, in the present study, we extended to the level of metabolites that exist in the medicinal plants. This study aimed to develop a predictive model of the Unani therapeutic usage based on its constituent metabolites using deep learning and data-intensive science approaches. Furthermore, the best prediction model was then utilized to extract important metabolites for each therapeutic usage of Unani. In this study, it was observed that the deep neural network approach provided a much better prediction model than other algorithms including random forest and support vector machine. Moreover, according to the best prediction model using the deep neural network, we identified 118 important metabolites for nine therapeutic usages of Unani.

Prediction of Potential Natural Antibiotics Plants Based on Jamu Formula Using Random Forest Classifier.

Jamu is the traditional Indonesian herbal medicine system that is considered to have many benefits such as serving as a cure for diseases or maintaining sound health. A Jamu medicine is generally made from a mixture of several herbs. Natural antibiotics can provide a way to handle the problem of antibiotic resistance. This research aims to discover the potential of herbal plants as natural antibiotic candidates based on a machine learning approach. Our input data consists of a list of herbal formulas with plants as their constituents. The target class corresponds to bacterial diseases that can be cured by herbal formulas. The best model has been observed by implementing the Random Forest (RF) algorithm. For 10-fold cross-validations, the maximum accuracy, recall, and precision are 91.10%, 91.10%, and 90.54% with standard deviations 1.05, 1.05, and 1.48, respectively, which imply that the model obtained is good and robust. This study has shown that 14 plants can be potentially used as natural antibiotic candidates. Furthermore, according to scientific journals, 10 of the 14 selected plants have direct or indirect antibacterial activity.

Evaluating cepharanthine analogues as natural drugs against SARS-CoV-2.

Cepharanthine (CEP) is a natural biscoclaurine alkaloid of plant origin and was recently demonstrated to have anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) activity. In this study, we evaluated whether natural analogues of CEP may act as potential anti-coronavirus disease 2019 drugs. A total of 24 compounds resembling CEP were extracted from the KNApSAcK database, and their binding affinities to target proteins, including the spike protein and main protease of SARS-CoV-2, NPC1 and TPC2 in humans, were predicted via molecular docking simulations. Selected analogues were further evaluated by a cell-based SARS-CoV-2 infection assay. In addition, the efficacies of CEP and its analogue tetrandrine were assessed. A comparison of the docking conformations of these compounds suggested that the diphenyl ester moiety of the molecules was a putative pharmacophore of the CEP analogues.

Cheminformatics modeling of the correlation between Bupleurum Root-formula medicines and Excess and Deficiency pattern in the diagnostic criteria of Sho in Kampo (traditional Japanese medicine) by non-targeted direct infusion mass spectrometry with machine learning.

Kampo is a form of traditional Japanese medicine, and its therapeutic strategy has been validated empirically over millennia, mainly in Asia. Kampo therapy aims to holistically prevent and treat disease based on the specific diagnosis Sho (in Japanese), in contrast with modern medical treatment which focuses on a patient's affected parts and local conditions. The medicines formulated using crude drugs derived from natural sources (Kampo formulas) are prescribed for patients according to their specific Sho, and thus the Kampo medication system is very complex. However, our previous study strongly suggested that Kampo medication theory could be explained by chemometrics and informatic approaches [Okada et al. in J Nat Med 70:107-114, 2016]. Here, we studied a group of seven formulas with Bupleurum Root and Scutellaria Root as the principal crude drugs. First, decoctions of the formulas were prepared and their supernatants were analyzed by non-targeted direct infusion mass spectrometry (MS) and principal component analysis, which is a type of unsupervised machine learning. Next, supervised machine learning was used to perform partial least squares modeling of the MS data matrix trained on the patients' constitution of Excess, Deficiency, or Medium between these two states (EDM) in Sho. The results showed that the correlation between the chemical fingerprints obtained by MS analysis and EDM could be modeled well using this approach. This cheminformatics modeling approach successfully interpreted part of the complex Kampo medication system studied using the fingerprints of formulas obtained by MS analysis and was consistent with the predicted Sho.

Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach.

BACKGROUND: We performed in silico prediction of the interactions between compounds of Jamu herbs and human proteins by utilizing data-intensive science and machine learning methods. Verifying the proteins that are targeted by compounds of natural herbs will be helpful to select natural herb-based drug candidates. METHODS: Initially, data related to compounds, target proteins, and interactions between them were collected from open access databases. Compounds are represented by molecular fingerprints, whereas amino acid sequences are represented by numerical protein descriptors. Then, prediction models that predict the interactions between compounds and target proteins were constructed using support vector machine and random forest. RESULTS: A random forest model constructed based on MACCS fingerprint and amino acid composition obtained the highest accuracy. We used the best model to predict target proteins for 94 important Jamu compounds and assessed the results by supporting evidence from published literature and other sources. There are 27 compounds that can be validated by professional doctors, and those compounds belong to seven efficacy groups. CONCLUSION: By comparing the efficacy of predicted compounds and the relations of the targeted proteins with diseases, we found that some compounds might be considered as drug candidates.

Oviposition inhibitor in umbelliferous medicinal plants for the common yellow swallowtail (Papilio machaon).

Umbelliferous medicinal plants, such as Angelica acutiloba Kitagawa and Angelica dahurica Bentham et Hooker filius ex Franchet et Savatier, account for a large percentage of crude drug consumption in Japan. The most serious problem in the cultivation of umbelliferous medicinal plants is the feeding damage caused by the common yellow swallowtail (Papilio machaon hippocrates C. & R. Felder, 1864). When we compared the numbers of eggs laid by P. machaon on six umbelliferous medicinal plants, the eggs on A. acutiloba, A. dahurica, and Glehnia littoralis Fr. Schmidt ex Miquel were the most numerous, those on Saposhnikovia divaricata Schischkin and Cnidium officinale Makino were rare, and Bupleurum falcatum Linné was not oviposited at all. To identify oviposition inhibitors for P. machaon in B. falcatum, S. divaricata, and C. officinale, the volatile chemical constituents of these umbelliferous medicinal plants were compared with GC-MS. We carried out multivariate analysis of gas chromatographic data and concluded that germacrene D, α-humulene, and trans-caryophyllene play important roles in protecting plants from oviposition by P. machaon. Their oviposition repellent activity was confirmed by the fact that the number of eggs laid on the leaves around a repellent device containing a mixture of germacrene D, α-humulene, and trans-caryophyllene was reduced by 40% compared to a control.

Metabolomic Studies of Indonesian Jamu Medicines: Prediction of Jamu Efficacy and Identification of Important Metabolites.

In order to obtain a better understanding why some Jamu formulas can be used to treat a specific disease, we performed metabolomic studies of Jamu by taking into consideration the biologically active compounds existing in plants used as Jamu ingredients. A thorough integration of information from omics is expected to provide solid evidence-based scientific rationales for the development of modern phytomedicines. This study focused on prediction of Jamu efficacy based on its component metabolites and also identification of important metabolites related to each efficacy group. Initially, we compared the performance of Support Vector Machines and Random Forest to predict the Jamu efficacy with three different data pre-processing approaches, such as no filtering, Single Filtering algorithm, and a combination of Single Filtering algorithm and feature selection using Regularized Random Forest. Both classifiers performed very well and according to 5-fold cross-validation results, the mean accuracy of Support Vector Machine with linear kernel was slightly better than Random Forest. It can be concluded that machine learning methods can successfully relate Jamu efficacy with metabolites. In addition, we extended our analysis by identifying important metabolites from the Random Forest model. The inTrees framework was used to extract the rules and to select important metabolites for each efficacy group. Overall, we identified 94 significant metabolites associated to 12 efficacy groups and many of them were validated by published literature and KNApSAcK Metabolite Activity database.

Finding an appropriate equation to measure similarity between binary vectors: case studies on Indonesian and Japanese herbal medicines.

BACKGROUND: The binary similarity and dissimilarity measures have critical roles in the processing of data consisting of binary vectors in various fields including bioinformatics and chemometrics. These metrics express the similarity and dissimilarity values between two binary vectors in terms of the positive matches, absence mismatches or negative matches. To our knowledge, there is no published work presenting a systematic way of finding an appropriate equation to measure binary similarity that performs well for certain data type or application. A proper method to select a suitable binary similarity or dissimilarity measure is needed to obtain better classification results. RESULTS: In this study, we proposed a novel approach to select binary similarity and dissimilarity measures. We collected 79 binary similarity and dissimilarity equations by extensive literature search and implemented those equations as an R package called bmeasures. We applied these metrics to quantify the similarity and dissimilarity between herbal medicine formulas belonging to the Indonesian Jamu and Japanese Kampo separately. We assessed the capability of binary equations to classify herbal medicine pairs into match and mismatch efficacies based on their similarity or dissimilarity coefficients using the Receiver Operating Characteristic (ROC) curve analysis. According to the area under the ROC curve results, we found Indonesian Jamu and Japanese Kampo datasets obtained different ranking of binary similarity and dissimilarity measures. Out of all the equations, the Forbes-2 similarity and the Variant of Correlation similarity measures are recommended for studying the relationship between Jamu formulas and Kampo formulas, respectively. CONCLUSIONS: The selection of binary similarity and dissimilarity measures for multivariate analysis is data dependent. The proposed method can be used to find the most suitable binary similarity and dissimilarity equation wisely for a particular data. Our finding suggests that all four types of matching quantities in the Operational Taxonomic Unit (OTU) table are important to calculate the similarity and dissimilarity coefficients between herbal medicine formulas. Also, the binary similarity and dissimilarity measures that include the negative match quantity d achieve better capability to separate herbal medicine pairs compared to equations that exclude d.

Informatics framework of traditional Sino-Japanese medicine (Kampo) unveiled by factor analysis.

Kampo, an empirically validated system of traditional Sino-Japanese medicine, aims to treat patients holistically. This is in contrast to modern medicine, which focuses in principle on treating the affected parts of the body of the patient. Kampo medicines formulated as combinations of crude drugs are prescribed based on a Kampo-specific diagnosis called Sho (in Japanese), defined as the holistic condition of each patient. Therefore, the medication system is very complex and is not well understood from a modern scientific perspective. Here, we show the informatics framework of Kampo medication by multivariate factor analysis of the elements constituting Kampo medication. First, the variation of Kampo formulas projected by principal component analysis (PCA) indicated that the combination patterns of crude drugs were highly correlated with Sho diagnoses of Deficiency and Excess. In an opposite way, partial least squares projection to latent structures (PLS) regression analysis could also predict Deficiency/Excess only from the composed crude drugs. Secondly, to chemically verify the correlation between Deficiency/Excess and crude drugs, we performed mass spectrometry (MS)-based metabolome analysis of Kampo prescriptions. PCA and PLS regression analysis of the metabolome data also suggested that Deficiency/Excess could be theoretically explained based on the variation in chemical fingerprints of Kampo medicines. Our results show that factor analysis of Kampo concepts and of the metabolomes of Kampo medicines enables interpretation of the complex system of Kampo. This study will theoretically form the basis for establishing traditionally and empirically based medications worldwide, leading to systematically personalized medicine.

Metabolomic characterization of a low phytic acid and high anti-oxidative cultivar of turmeric.

Turmeric, the rhizome of Curcuma longa, has a long history of use as a spice and also as a traditional medicine in many Asian countries. To reveal unique morphological features of a newly registered Curcuma cultivar, C. longa cv. Okinawa Ougon (Ougon), non-targeted LC-MS and GC-MS analyses were conducted. The analysis revealed its distinctive chemical properties: lower amount of phytic acid and inorganic metals such as Fe, Mn, and Al, as well as higher concentrations of reduced derivatives of curcuminoids, such as dihydrobisdemethoxycurcumin, tetrahydrobisdemethoxycurcumin, dihydrodemethoxycurcumin, and tetrahydrodemethoxycurcumin. In addition, germacrane-type sesquiterpenes were almost absent although α-humulene and ß-caryophyllene, generated by the same biosynthetic route, were present. Presumably the alternation of the metal ion content, serving as a cofactor of sesquiterpene synthase, modulates the resulting variation of the sesquiterpenes. In summary, the cultivar Ougon is considered a promising candidate for functional food additives.

Supervised clustering based on DPClusO: prediction of plant-disease relations using Jamu formulas of KNApSAcK database.

Indonesia has the largest medicinal plant species in the world and these plants are used as Jamu medicines. Jamu medicines are popular traditional medicines from Indonesia and we need to systemize the formulation of Jamu and develop basic scientific principles of Jamu to meet the requirement of Indonesian Healthcare System. We propose a new approach to predict the relation between plant and disease using network analysis and supervised clustering. At the preliminary step, we assigned 3138 Jamu formulas to 116 diseases of International Classification of Diseases (ver. 10) which belong to 18 classes of disease from National Center for Biotechnology Information. The correlation measures between Jamu pairs were determined based on their ingredient similarity. Networks are constructed and analyzed by selecting highly correlated Jamu pairs. Clusters were then generated by using the network clustering algorithm DPClusO. By using matching score of a cluster, the dominant disease and high frequency plant associated to the cluster are determined. The plant to disease relations predicted by our method were evaluated in the context of previously published results and were found to produce around 90% successful predictions.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology.

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology.

Efficacy prediction of jamu formulations by PLS modeling.

Indonesian herbal medicines made from mixtures of several plants are called "Jamu." The efficacy of a particular Jamu is determined by its ingredients i.e. the composition of the plants. Thus, we modeled the ingredients of Jamu formulas using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. Utilizing response predictions obtained from PLS-DA, we predicted the efficacies of Jamu formulations using two methods: maximum response prediction and maximum probability. In predictions of Jamu efficacy, the maximum response prediction method produced a smaller error than that the maximum probability method. Furthermore, utilizing the PLS-DA coefficient matrix, we determined the efficacy for which a plant is most useful, based on its largest coefficients.

KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research.

A database (DB) describing the relationships between species and their metabolites would be useful for metabolomics research, because it targets systematic analysis of enormous numbers of organic compounds with known or unknown structures in metabolomics. We constructed an extensive species-metabolite DB for plants, the KNApSAcK Core DB, which contains 101,500 species-metabolite relationships encompassing 20,741 species and 50,048 metabolites. We also developed a search engine within the KNApSAcK Core DB for use in metabolomics research, making it possible to search for metabolites based on an accurate mass, molecular formula, metabolite name or mass spectra in several ionization modes. We also have developed databases for retrieving metabolites related to plants used for a range of purposes. In our multifaceted plant usage DB, medicinal/edible plants are related to the geographic zones (GZs) where the plants are used, their biological activities, and formulae of Japanese and Indonesian traditional medicines (Kampo and Jamu, respectively). These data are connected to the species-metabolites relationship DB within the KNApSAcK Core DB, keyed via the species names. All databases can be accessed via the website http://kanaya.naist.jp/KNApSAcK_Family/. KNApSAcK WorldMap DB comprises 41,548 GZ-plant pair entries, including 222 GZs and 15,240 medicinal/edible plants. The KAMPO DB consists of 336 formulae encompassing 278 medicinal plants; the JAMU DB consists of 5,310 formulae encompassing 550 medicinal plants. The Biological Activity DB consists of 2,418 biological activities and 33,706 pairwise relationships between medicinal plants and their biological activities. Current statistics of the binary relationships between individual databases were characterized by the degree distribution analysis, leading to a prediction of at least 1,060,000 metabolites within all plants. In the future, the study of metabolomics will need to take this huge number of metabolites into consideration.

Systems biology in a commercial quality study of the Japanese Angelica radix: toward an understanding of traditional medicinal plants.

The commercial quality of Japanese Angelica radices -- Angelica acutiloba Kitagawa (Yamato-toki) and A. acutiloba Kitagawa var. sugiyama Hikino (Hokkai-toki) -- used in Kampo traditional herbal medicines, was studied by use of omics technologies. Complementary and alternative medical providers have observed in their clinical experience that differences in radix commercial quality reflect the differences in pharmacological responses; however, there has been little scientific examination of this phenomenon. The approach of omics, including metabolomics, transcriptomics, genomics, and informatics revealed a distinction between the radix-quality grades based on their metabolites, gene expression in human subjects, and plant genome sequences. Systems biology, constructing a network of omics data used to analyze this complex system, is expected to be a powerful tool for enhancing the study of radix quality and furthering a comprehensive understanding of all medicinal plants.

Database for crude drugs and Kampo medicine.

A wiki-based repository for crude drugs and Kampo medicine is introduced. It provides taxonomic and chemical information for 158 crude drugs and 348 prescriptions of the traditional Kampo medicine in Japan, which is a variation of ancient Chinese medicine. The system is built on MediaWiki with extensions for inline page search and for sending user-input elements to the server. These functions together realize implementation of word checks and data integration at the user-level. In this scheme, any user can participate in creating an integrated database with controlled vocabularies on the wiki system. Our implementation and data are accessible at http://metabolomics.jp/wiki/.

Metabolomics of medicinal plants: the importance of multivariate analysis of analytical chemistry data.

Metabolomics, the comprehensive and global analysis of diverse metabolites produced in cells and organisms, has greatly expanded metabolite fingerprinting and profiling as well as the selection and identification of marker metabolites. The methodology typically employs multivariate analysis to statistically process the massive amount of analytical chemistry data resulting from high-throughput and simultaneous metabolite analysis. Although the technology of plant metabolomics has mainly developed with other post-genomics in systems biology and functional genomics, it is independently applied to the evaluation of the qualities of medicinal plants, based on the diversity of metabolite fingerprints resulting from multivariate analysis of non-targeted or widely targeted metabolite analysis. One advantage of applying metabolomics is that medicinal plants are evaluated based not only on the limited number of metabolites that are pharmacologically important chemicals, but also on the fingerprints of minor metabolites and bioactive chemicals. In particular, score plot and loading plot analyses e.g. principal component analysis (PCA), partial-least-squares discriminant analysis (PLS-DA), and discrimination map analysis such as batch-learning self-organizing map (BL-SOM) analysis, are often employed for the reduction of a metabolite fingerprint and the classification of analyzed samples. Based on recent studies, we now understand that metabolomics can be an effective approach for comprehensive evaluation of the qualities of medicinal plants. In this review, we describe practical cases in which metabolomic study was performed on medicinal plants, and discuss the utility of metabolomics for this research field, with focus on multivariate analysis.

Metabolome analysis of Ephedra plants with different contents of ephedrine alkaloids by using UPLC-Q-TOF-MS.

Metabolome analysis of four varieties of Ephedra plants, which contain different amounts of ephedrine alkaloids, was demonstrated in this study. The metabolites were comprehensively analyzed by using ultra performance liquid chromatography (UPLC) coupled with quadrupole time-of-flight mass spectrometry (Q-TOF-MS) and the ephedrine alkaloids were also profiled. Subsequently, multivariate analyses of principal component analysis (PCA) and batch-learning self-organizing mapping (BL-SOM) analysis were applied to the raw data of the total ion chromatogram (TIC). PCA was performed to visualize the fingerprints characteristic for each Ephedra variant and the independent metabolome clusters were formed. The metabolite fingerprints were also visualized by BL-SOM analysis and were displayed as a lattice of colored individual cells which was characteristic for each Ephedra variant. BL-SOM analysis was also used for identification of chemical marker peaks because the information assigned to a cell represented either increases or decreases in peak intensities. Using this analysis, ephedrine alkaloids were successfully selected from the TICs as chemical markers for each Ephedra variant and this result suggested that BL-SOM analysis was an effective method for the selection of marker metabolites. We report our study here as a practical case of metabolomic study on medicinal resources.