Development of method using language processing techniques for extracting information on drug-health food product interactions.

AIMS: Health food products (HFPs) are foods and products related to maintaining and promoting health. HFPs may sometimes cause unforeseen adverse health effects by interacting with drugs. Considering the importance of information on the interactions between HFPs and drugs, this study aimed to establish a workflow to extract information on Drug-HFP Interactions (DHIs) from open resources. METHODS: First, Information on drugs, enzymes, their interactions, and known DHIs was collected from multiple public databases and literature sources. Next, a network consisted of enzymes, HFP, and drugs was constructed, assuming enzymes as candidates for hubs in Drug-HFP interactions (Method 1). Furthermore, we developed methods to analyze the biomedical context of each drug and HFP to predict potential DHIs out of the DHIs obtained in Method 1 by applying BioWordVec, a widely used biomedical terminology quantifier (Method 2-1 and 2-2). RESULTS: 44,965 DHIs (30% known) were identified in Method 1, including 38 metabolic enzymes, 157 HFPs, and 1256 drugs. Method 2-1 selected 7401 DHIs (17% known) from the DHIs of Method 1, while Method 2-2 chose 2819 DHIs (30% known). Based on the different assumptions in these methods where Method 2-1 specifically selects HFPs interacting with specific enzymes and Method 2-2 specifically selects HFPs with similar function with drugs, the propsed methods resulted in extracting a wide variety of DHIs. CONCLUSIONS: By integrating the results of language processing techniques with those of the network analysis, a workflow to efficiently extract unknown and known DHIs was constructed.

Development of a Machine Learning Model for Classifying Cooking Recipes According to Dietary Styles.

To complement classical methods for identifying Japanese, Chinese, and Western dietary styles, this study aimed to develop a machine learning model. This study utilized 604 features from 8183 cooking recipes based on a Japanese recipe site. The data were randomly divided into training, validation, and test sets for each dietary style at a 60:20:20 ratio. Six machine learning models were developed in this study to effectively classify cooking recipes according to dietary styles. The evaluation indicators were above 0.8 for all models in each dietary style. The top ten features were extracted from each model, and the features common to three or more models were employed as the best predictive features. Five well-predicted features were indicated for the following seasonings: soy sauce, miso (fermented soy beans), and mirin (sweet cooking rice wine) in the Japanese diet; oyster sauce and doubanjiang (chili bean sauce) in the Chinese diet; and olive oil in the Western diet. Predictions by broth were indicated in each diet, such as dashi in the Japanese diet, chicken soup in the Chinese diet, and consommé in the Western diet. The prediction model suggested that seasonings and broths could be used to predict dietary styles.

EnzymeNet: residual neural networks model for Enzyme Commission number prediction.

Motivation: Enzymes are key targets to biosynthesize functional substances in metabolic engineering. Therefore, various machine learning models have been developed to predict Enzyme Commission (EC) numbers, one of the enzyme annotations. However, the previously reported models might predict the sequences with numerous consecutive identical amino acids, which are found within unannotated sequences, as enzymes. Results: Here, we propose EnzymeNet for prediction of complete EC numbers using residual neural networks. EnzymeNet can exclude the exceptional sequences described above. Several EnzymeNet models were built and optimized to explore the best conditions for removing such sequences. As a result, the models exhibited higher prediction accuracy with macro F1 score up to 0.850 than previously reported models. Moreover, even the enzyme sequences with low similarity to training data, which were difficult to predict using the reported models, could be predicted extensively using EnzymeNet models. The robustness of EnzymeNet models will lead to discover novel enzymes for biosynthesis of functional compounds using microorganisms. Availability and implementation: The source code of EnzymeNet models is freely available at https://github.com/nwatanbe/enzymenet.

Extracting the latent needs of dementia patients and caregivers from transcribed interviews in japanese: an initial assessment of the availability of morpheme selection as input data with Z-scores in machine learning.

BACKGROUND: Given the increasing number of dementia patients worldwide, a new method was developed for machine learning models to identify the 'latent needs' of patients and caregivers to facilitate patient/public involvement in societal decision making. METHODS: Japanese transcribed interviews with 53 dementia patients and caregivers were used. A new morpheme selection method using Z-scores was developed to identify trends in describing the latent needs. F-measures with and without the new method were compared using three machine learning models. RESULTS: The F-measures with the new method were higher for the support vector machine (SVM) (F-measure of 0.81 with the new method and F-measure of 0.79 without the new method for patients) and Naive Bayes (F-measure of 0.69 with the new method and F-measure of 0.67 without the new method for caregivers and F-measure of 0.75 with the new method and F-measure of 0.73 without the new method for patients). CONCLUSION: A new scheme based on Z-score adaptation for machine learning models was developed to predict the latent needs of dementia patients and their caregivers by extracting data from interviews in Japanese. However, this study alone cannot be used to assign significance to the adaptation of the new method because of no enough size of sample dataset. Such pre-selection with Z-score adaptation from text data in machine learning models should be considered with more modified suitable methods in the near future.

Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables.

Cardiovascular disease (CVD) is one of the primary causes of death around the world. This study aimed to identify risk factors associated with CVD mortality using data from the National Health and Nutrition Examination Survey (NHANES). We created three models focusing on dietary data, non-diet-related health data, and a combination of both. Machine learning (ML) models, particularly the random forest algorithm, demonstrated robust consistency across health, nutrition, and mixed categories in predicting death from CVD. Shapley additive explanation (SHAP) values showed age, systolic blood pressure, and several other health factors as crucial variables, while fiber, calcium, and vitamin E, among others, were significant nutritional variables. Our research emphasizes the importance of comprehensive health evaluation and dietary intake in predicting CVD mortality. The inclusion of nutrition variables improved the performance of our models, underscoring the utility of dietary intake in ML-based data analysis. Further investigation using large datasets with recurring dietary recalls is necessary to enhance the effectiveness and interpretability of such models.

Ramen Consumption and Gut Microbiota Diversity in Japanese Women: Cross-Sectional Data from the NEXIS Cohort Study.

A cross-sectional study involving 224 healthy Japanese adult females explored the relationship between ramen intake, gut microbiota diversity, and blood biochemistry. Using a stepwise regression model, ramen intake was inversely associated with gut microbiome alpha diversity after adjusting for related factors, including diets, Age, BMI, and stool habits (ß = -0.018; r = -0.15 for Shannon index). The intake group of ramen was inversely associated with dietary nutrients and dietary fiber compared with the no-intake group of ramen. Sugar intake, Dorea as a short-chain fatty acid (SCFA)-producing gut microbiota, and γ-glutamyl transferase as a liver function marker were directly associated with ramen intake after adjustment for related factors including diets, gut microbiota, and blood chemistry using a stepwise logistic regression model, whereas Dorea is inconsistently less abundant in the ramen group. In conclusion, the increased ramen was associated with decreased gut bacterial diversity accompanying a perturbation of Dorea through the dietary nutrients, gut microbiota, and blood chemistry, while the methodological limitations existed in a cross-sectional study. People with frequent ramen eating habits need to take measures to consume various nutrients to maintain and improve their health, and dietary management can be applied to the dietary feature in ramen consumption.

Prediction of Metabolic Flux Distribution by Flux Sampling: As a Case Study, Acetate Production from Glucose in Escherichia coli.

Omics data was acquired, and the development and research of metabolic simulation and analysis methods using them were also actively carried out. However, it was a laborious task to acquire such data each time the medium composition, culture conditions, and target organism changed. Therefore, in this study, we aimed to extract and estimate important variables and necessary numbers for predicting metabolic flux distribution as the state of cell metabolism by flux sampling using a genome-scale metabolic model (GSM) and its analysis. Acetic acid production from glucose in Escherichia coli with GSM iJO1366 was used as a case study. Flux sampling obtained by OptGP using 1000 pattern constraints on substrate, product, and growth fluxes produced a wider sample than the default case. The analysis also suggested that the fluxes of iron ions, O2, CO2, and NH4+, were important for predicting the metabolic flux distribution. Additionally, the comparison with the literature value of 13C-MFA using CO2 emission flux as an example of an important flux suggested that the important flux obtained by this method was valid for the prediction of flux distribution. In this way, the method of this research was useful for extracting variables that were important for predicting flux distribution, and as a result, the possibility of contributing to the reduction of measurement variables in experiments was suggested.

Different Recognition of Protein Features Depending on Deep Learning Models: A Case Study of Aromatic Decarboxylase UbiD.

The number of unannotated protein sequences is explosively increasing due to genome sequence technology. A more comprehensive understanding of protein functions for protein annotation requires the discovery of new features that cannot be captured from conventional methods. Deep learning can extract important features from input data and predict protein functions based on the features. Here, protein feature vectors generated by 3 deep learning models are analyzed using Integrated Gradients to explore important features of amino acid sites. As a case study, prediction and feature extraction models for UbiD enzymes were built using these models. The important amino acid residues extracted from the models were different from secondary structures, conserved regions and active sites of known UbiD information. Interestingly, the different amino acid residues within UbiD sequences were regarded as important factors depending on the type of models and sequences. The Transformer models focused on more specific regions than the other models. These results suggest that each deep learning model understands protein features with different aspects from existing knowledge and has the potential to discover new laws of protein functions. This study will help to extract new protein features for the other protein annotations.

Predicting Overweight and Obesity Status Among Malaysian Working Adults With Machine Learning or Logistic Regression: Retrospective Comparison Study.

BACKGROUND: Overweight or obesity is a primary health concern that leads to a significant burden of noncommunicable disease and threatens national productivity and economic growth. Given the complexity of the etiology of overweight or obesity, machine learning (ML) algorithms offer a promising alternative approach in disentangling interdependent factors for predicting overweight or obesity status. OBJECTIVE: This study examined the performance of 3 ML algorithms in comparison with logistic regression (LR) to predict overweight or obesity status among working adults in Malaysia. METHODS: Using data from 16,860 participants (mean age 34.2, SD 9.0 years; n=6904, 41% male; n=7048, 41.8% with overweight or obesity) in the Malaysia's Healthiest Workplace by AIA Vitality 2019 survey, predictor variables, including sociodemographic characteristics, job characteristics, health and weight perceptions, and lifestyle-related factors, were modeled using the extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) algorithms, as well as LR, to predict overweight or obesity status based on a BMI cutoff of 25 kg/m2. RESULTS: The area under the receiver operating characteristic curve was 0.81 (95% CI 0.79-0.82), 0.80 (95% CI 0.79-0.81), 0.80 (95% CI 0.78-0.81), and 0.78 (95% CI 0.77-0.80) for the XGBoost, RF, SVM, and LR models, respectively. Weight satisfaction was the top predictor, and ethnicity, age, and gender were also consistent predictor variables of overweight or obesity status in all models. CONCLUSIONS: Based on multi-domain online workplace survey data, this study produced predictive models that identified overweight or obesity status with moderate to high accuracy. The performance of both ML-based and logistic regression models were comparable when predicting obesity among working adults in Malaysia.

Comprehensive Machine Learning Prediction of Extensive Enzymatic Reactions.

New enzyme functions exist within the increasing number of unannotated protein sequences. Novel enzyme discovery is necessary to expand the pathways that can be accessed by metabolic engineering for the biosynthesis of functional compounds. Accordingly, various machine learning models have been developed to predict enzymatic reactions. However, the ability to predict unknown reactions that are not included in the training data has not been clarified. In order to cover uncertain and unknown reactions, a wider range of reaction types must be demonstrated by the models. Here, we establish 16 expanded enzymatic reaction prediction models developed using various machine learning algorithms, including deep neural network. Improvements in prediction performances over that of our previous study indicate that the updated methods are more effective for the prediction of enzymatic reactions. Overall, the deep neural network model trained with combined substrate-enzyme-product information exhibits the highest prediction accuracy with Macro F1 scores up to 0.966 and with robust prediction of unknown enzymatic reactions that are not included in the training data. This model can predict more extensive enzymatic reactions in comparison to previously reported models. This study will facilitate the discovery of new enzymes for the production of useful substances.

Machine learning discovery of missing links that mediate alternative branches to plant alkaloids.

Engineering the microbial production of secondary metabolites is limited by the known reactions of correctly annotated enzymes. Therefore, the machine learning discovery of specialized enzymes offers great potential to expand the range of biosynthesis pathways. Benzylisoquinoline alkaloid production is a model example of metabolic engineering with potential to revolutionize the paradigm of sustainable biomanufacturing. Existing bacterial studies utilize a norlaudanosoline pathway, whereas plants contain a more stable norcoclaurine pathway, which is exploited in yeast. However, committed aromatic precursors are still produced using microbial enzymes that remain elusive in plants, and additional downstream missing links remain hidden within highly duplicated plant gene families. In the current study, machine learning is applied to predict and select plant missing link enzymes from homologous candidate sequences. Metabolomics-based characterization of the selected sequences reveals potential aromatic acetaldehyde synthases and phenylpyruvate decarboxylases in reconstructed plant gene-only benzylisoquinoline alkaloid pathways from tyrosine. Synergistic application of the aryl acetaldehyde producing enzymes results in enhanced benzylisoquinoline alkaloid production through hybrid norcoclaurine and norlaudanosoline pathways.

Mangrove crab intestine and habitat sediment microbiomes cooperatively work on carbon and nitrogen cycling.

Mangrove ecosystems, where litter and organic components are degraded and converted into detrital materials, support rich coastal fisheries resources. Sesarmid (Grapsidae) crabs, which feed on mangrove litter, play a crucial role in material flow in carbon-rich and nitrogen-limited mangrove ecosystems; however, the process of assimilation and conversion into detritus has not been well studied. In this study, we performed microbiome analyses of intestinal bacteria from three species of mangrove crab and five sediment positions in the mud lobster mounds, including the crab burrow wall, to study the interactive roles of crabs and sediment in metabolism. Metagenome analysis revealed species-dependent intestinal profiles, especially in Neosarmatium smithi, while the sediment microbiome was similar in all positions, albeit with some regional dependency. The microbiome profiles of crab intestines and sediments were significantly different in the MDS analysis based on OTU similarity; however, 579 OTUs (about 70% of reads in the crab intestinal microbiome) were identical between the intestinal and sediment bacteria. In the phenotype prediction, cellulose degradation was observed in the crab intestine. Cellulase activity was detected in both crab intestine and sediment. This could be mainly ascribed to Demequinaceae, which was predominantly found in the crab intestines and burrow walls. Nitrogen fixation was also enriched in both the crab intestines and sediments, and was supported by the nitrogenase assay. Similar to earlier reports, sulfur-related families were highly enriched in the sediment, presumably degrading organic compounds as terminal electron acceptors under anaerobic conditions. These results suggest that mangrove crabs and habitat sediment both contribute to carbon and nitrogen cycling in the mangrove ecosystem via these two key reactions.

Pathway engineering for high-yield production of lutein in Escherichia coli.

Lutein is an industrially important carotenoid pigment, which is essential for photoprotection and photosynthesis in plants. Lutein is crucial for maintaining human health due to its protective ability from ocular diseases. However, its pathway engineering research has scarcely been performed for microbial production using heterologous hosts, such as Escherichia coli, since the engineering of multiple genes is required. These genes, which include tricky key carotenoid biosynthesis genes typically derived from plants, encode two sorts of cyclases (lycopene ε- and ß-cyclase) and cytochrome P450 CYP97C. In this study, upstream genes effective for the increase in carotenoid amounts, such as isopentenyl diphosphate isomerase (IDI) gene, were integrated into the E. coli JM101 (DE3) genome. The most efficient set of the key genes (MpLCYe, MpLCYb and MpCYP97C) was selected from among the corresponding genes derived from various plant (or bacterial) species using E. coli that had accumulated carotenoid substrates. Furthermore, to optimize the production of lutein in E. coli, we introduced several sorts of plasmids that contained some of the multiple genes into the genome-inserted strain and compared lutein productivity. Finally, we achieved 11 mg/l as lutein yield using a mini jar. Here, the high-yield production of lutein was successfully performed using E. coli through approaches of pathway engineering. The findings obtained here should be a base reference for substantial lutein production with microorganisms in the future.

Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering.

Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.

History-Driven Genetic Modification Design Technique Using a Domain-Specific Lexical Model for the Acceleration of DBTL Cycles for Microbial Cell Factories.

The development of microbes for conducting bioprocessing via synthetic biology involves design-build-test-learn (DBTL) cycles. To aid the designing step, we developed a computational technique that suggests next genetic modifications on the basis of relatedness to the user's design history of genetic modifications accumulated through former DBTL cycles conducted by the user. This technique, which comprehensively retrieves well-known designs related to the history, involves searching text for previous literature and then mining genes that frequently co-occur in the literature with those modified genes. We further developed a domain-specific lexical model that weights literature that is more related to the domain of metabolic engineering to emphasize genes modified for bioprocessing. Our technique made a suggestion by using a history of creating a Corynebacterium glutamicum strain producing shikimic acid that had 18 genetic modifications. Inspired by the suggestion, eight genes were considered by biologists for further modification, and modifying four of these genes proved experimentally efficient in increasing the production of shikimic acid. These results indicated that our proposed technique successfully utilized the former cycles to suggest relevant designs that biologists considered worth testing. Comprehensive retrieval of well-tested designs will help less-experienced researchers overcome the entry barrier as well as inspire experienced researchers to formulate design concepts that have been overlooked or suspended. This technique will aid DBTL cycles by feeding histories back to the next genetic design, thereby complementing the designing step.

Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli.

Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.

Exploration and Evaluation of Machine Learning-Based Models for Predicting Enzymatic Reactions.

Unannotated gene sequences in databases are increasing due to sequencing advances. Therefore, computational methods to predict functions of unannotated genes are needed. Moreover, novel enzyme discovery for metabolic engineering applications further encourages annotation of sequences. Here, enzyme functions are predicted using two general approaches, each including several machine learning algorithms. First, Enzyme-models (E-models) predict Enzyme Commission (EC) numbers from amino acid sequence information. Second, Substrate-Enzyme models (SE-models) are built to predict substrates of enzymatic reactions together with EC numbers, and Substrate-Enzyme-Product models (SEP-models) are built to predict substrates, products, and EC numbers. While accuracy of E-models is not optimal, SE-models and SEP-models predict EC numbers and reactions with high accuracy using all tested machine learning-based methods. For example, a single Random Forests-based SEP-model predicts EC first digits with an Average AUC score of over 0.94. Various metrics indicate that the current strategy of combining sequence and chemical structure information is effective at improving enzyme reaction prediction.

Reconstruction of metabolic pathway for isobutanol production in Escherichia coli.

BACKGROUND: The microbial production of useful fuels and chemicals has been widely studied. In several cases, glucose is used as the raw material, and almost all microbes adopt the Embden-Meyerhof (EM) pathway to degrade glucose into compounds of interest. Recently, the Entner-Doudoroff (ED) pathway has been gaining attention as an alternative strategy for microbial production. RESULTS: In the present study, we attempted to apply the ED pathway for isobutanol production in Escherichia coli because of the complete redox balance involved. First, we generated ED pathway-dependent isobutanol-producing E. coli. Thereafter, the inactivation of the genes concerning organic acids as the byproducts was performed to improve the carbon flux to isobutanol from glucose. Finally, the expression of the genes concerning the ED pathway was modified. CONCLUSIONS: The optimized isobutanol-producing E. coli produced 15.0 g/L of isobutanol as the final titer, and the yield from glucose was 0.37 g/g (g-glucose/g-isobutanol).

Author Correction: Mechanism-based tuning of insect 3,4-dihydroxyphenylacetaldehyde synthase for synthetic bioproduction of benzylisoquinoline alkaloids.

In the original version of this Article, the abbreviation of 3,4-dihydroxyphenylacetaldehyde synthase presented in the first paragraph of the Discussion section was given incorrectly as DYPAA. The correct abbreviation for this enzyme is DHPAAS. This error has been corrected in both the PDF and HTML versions of the Article.

Mechanism-based tuning of insect 3,4-dihydroxyphenylacetaldehyde synthase for synthetic bioproduction of benzylisoquinoline alkaloids.

Previous studies have utilized monoamine oxidase (MAO) and L-3,4-dihydroxyphenylalanine decarboxylase (DDC) for microbe-based production of tetrahydropapaveroline (THP), a benzylisoquinoline alkaloid (BIA) precursor to opioid analgesics. In the current study, a phylogenetically distinct Bombyx mori 3,4-dihydroxyphenylacetaldehyde synthase (DHPAAS) is identified to bypass MAO and DDC for direct production of 3,4-dihydroxyphenylacetaldehyde (DHPAA) from L-3,4-dihydroxyphenylalanine (L-DOPA). Structure-based enzyme engineering of DHPAAS results in bifunctional switching between aldehyde synthase and decarboxylase activities. Output of dopamine and DHPAA products is fine-tuned by engineered DHPAAS variants with Phe79Tyr, Tyr80Phe and Asn192His catalytic substitutions. Balance of dopamine and DHPAA products enables improved THP biosynthesis via a symmetrical pathway in Escherichia coli. Rationally engineered insect DHPAAS produces (R,S)-THP in a single enzyme system directly from L-DOPA both in vitro and in vivo, at higher yields than that of the wild-type enzyme. However, DHPAAS-mediated downstream BIA production requires further improvement.