Application of Market Basket Analysis for the Visualization of Transaction Data Based on Human Lifestyle and Spectroscopic Measurements

Bayesian network highlights the contributing factors for efficient arsenic phytoextraction by Pteris vittata in a contaminated field

Phytoextraction is a low-cost and eco-friendly method for removing pollutants, such as arsenic (As), from contaminated soil. One of the most studied As hyperaccumulators for soil remediation include Pteris vittata. Although phytoextraction using plant-assisted microbes has been considered a promising soil remediation method, microbial harnessing has not been achieved due to the complex and difficult to understand interactions between microbes and plants. This problem can possibly be addressed with a multi-omics approach using a Bayesian network. However, limited studies have used Bayesian networks to analyze plant-microbe interactions. Therefore, to understand this complex interaction and to facilitate efficient As phytoextraction using microbial inoculants, we conducted field cultivation experiments at two sites with different total As contents (62 and 8.9 mg/kg). Metabolome and microbiome data were obtained from rhizosphere soil samples using nuclear magnetic resonance and high-throughput sequencing, respectively, and a Bayesian network was applied to the obtained multi-omics data. In a highly As-contaminated site, inoculation with Pseudomonas sp. strain m307, which is an arsenite-oxidizing microbe having multiple copies of the arsenite oxidase gene, increased As concentration in the shoots of P. vittata to 157.5 mg/kg under this treatment; this was 1.5-fold higher than that of the other treatments. Bayesian network demonstrated that strain m307 contributed to As accumulation in P. vittata. Furthermore, the network showed that microbes belonging to the MND1 order positively contributed to As accumulation in P. vittata. Based on the ecological characteristics of MND1, it was suggested that the rhizosphere of P. vittata inoculated with strain m307 was under low-nitrogen conditions. Strain m307 may have induced low-nitrogen conditions via arsenite oxidation accompanied by nitrate reduction, potentially resulting in microbial iron reduction or the prevention of microbial iron oxidation. These conditions may have enhanced the bioavailability of arsenate, leading to increased As accumulation in P. vittata.

Large-scale omics dataset of polymer degradation provides robust interpretation for microbial niche and succession on different plastisphere

While biodegradable polymers have received increased attention due to the recent marine plastic problem, few studies have compared microbiomes and their degradation processes among biodegradable polymers. In this study, we set up prompt evaluation systems for polymer degradation, allowing us to collect 418 microbiome and 125 metabolome samples to clarify the microbiome and metabolome differences according to degradation progress and polymer material (polycaprolactone [PCL], polybutylene succinate-co-adipate [PBSA], polybutylene succinate [PBS], polybutylene adipate-co-terephthalate [PBAT], and poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) [PHBH]). The microbial community compositions were converged to each polymer material, and the largest differences were observed between PHBH and other polymers. Such gaps were probably formed primarily by the presence of specific hydrolase genes (i.e., 3HB depolymerase, lipase, and cutinase) in the microorganisms. Time-series sampling suggested several steps for microbial succession: (1) initial microbes decrease abruptly after incubation starts; (2) microbes, including polymer degraders, increase soon after the start of incubation and show an intermediate peak; (3) microbes, including biofilm constructers, increase their abundance gradually. Metagenome prediction showed functional changes, where free-swimming microbes with flagella adhered stochastically onto the polymer, and certain microbes started to construct a biofilm. Our large-dataset-based results provide robust interpretations for biodegradable polymer degradation.

Computational estimation of sediment symbiotic bacterial structures of seagrasses overgrowing downstream of onshore aquaculture

Coastal seagrass meadows are essential in blue carbon and aquatic ecosystem services. However, this ecosystem has suffered severe eutrophication and destruction due to the expansion of aquaculture. Therefore, methods for the flourishing of seagrass are still being explored. Here, data from 49 public coastal surveys on the distribution of seagrass and seaweed around the onshore aquaculture facilities are revalidated, and an exceptional area where the seagrass Zostera marina thrives was found near the shore downstream of the onshore aquaculture facility. To evaluate the characteristics of the sediment for growing seagrass, physicochemical properties and bacterial ecological evaluations of the sediment were conducted. Evaluation of chemical properties in seagrass sediments confirmed a significant increase in total carbon and a decrease in zinc content. Association analysis and linear discriminant analysis refined bacterial candidates specified in seagrass overgrown- and nonovergrown-sediment. Energy landscape analysis indicated that the symbiotic bacterial groups of seagrass sediment were strongly affected by the distance close to the seagrass-growing aquaculture facility despite their bacterial population appearing to fluctuate seasonally. The bacterial population there showed an apparent decrease in the pathogen candidates belonging to the order Flavobacteriales. Moreover, structure equation modeling and a linear non-Gaussian acyclic model based on the machine learning data estimated an optimal sediment symbiotic bacterial group candidate for seagrass growth as follows: the Lachnospiraceae and Ruminococcaceae families as gut-inhabitant bacteria, Rhodobacteraceae as photosynthetic bacteria, and Desulfobulbaceae as cable bacteria modulating oxygen or nitrate reduction and oxidation of sulfide. These observations confer a novel perspective on the sediment symbiotic bacterial structures critical for blue carbon and low-pathogenic marine ecosystems in aquaculture.

A potential network structure of symbiotic bacteria involved in carbon and nitrogen metabolism of wood-utilizing insect larvae

Effective biological utilization of wood biomass is necessary worldwide. Since several insect larvae can use wood biomass as a nutrient source, studies on their digestive microbial structures are expected to reveal a novel rule underlying wood biomass processing. Here, structural inferences for inhabitant bacteria involved in carbon and nitrogen metabolism for beetle larvae, an insect model, were performed to explore the potential rules. Bacterial analysis of larval feces showed enrichment of the phyla Chroloflexi, Gemmatimonadetes, and Planctomycetes, and the genera Bradyrhizobium, Chonella, Corallococcus, Gemmata, Hyphomicrobium, Lutibacterium, Paenibacillus, and Rhodoplanes, as bacteria potential involved in plant growth promotion, nitrogen cycle modulation, and/or environmental protection. The fecal abundances of these bacteria were not necessarily positively correlated with their abundances in the habitat, indicating that they were selectively enriched in the feces of the larvae. Correlation and association analyses predicted that common fecal bacteria might affect carbon and nitrogen metabolism. Based on these hypotheses, structural equation modeling (SEM) statistically estimated that inhabitant bacterial groups involved in carbon and nitrogen metabolism were composed of the phylum Gemmatimonadetes and Planctomycetes, and the genera Bradyrhizobium, Corallococcus, Gemmata, and Paenibacillus, which were among the fecal-enriched bacteria. Nevertheless, the selected common bacteria, i.e., the phyla Acidobacteria, Armatimonadetes, and Bacteroidetes and the genera Candidatus Solibacter, Devosia, Fimbriimonas, Gemmatimonas Opitutus, Sphingobium, and Methanobacterium, were necessary to obtain good fit indices in the SEM. In addition, the composition of the bacterial groups differed depending upon metabolic targets, carbon and nitrogen, and their stable isotopes, δ¹³C and δ¹⁵N, respectively. Thus, the statistically derived causal structural models highlighted that the larval fecal-enriched bacteria and common symbiotic bacteria might selectively play a role in wood biomass carbon and nitrogen metabolism. This information could confer a new perspective that helps us use wood biomass more efficiently and might stimulate innovation in environmental industries in the future.

Materials informatics approach using domain modelling for exploring structure-property relationships of polymers

In the development of polymer materials, it is an important issue to explore the complex relationships between domain structure and physical properties. In the domain structure analysis of polymer materials, ¹H-static solid-state NMR (ssNMR) spectra can provide information on mobile, rigid, and intermediate domains. But estimation of domain structure from its analysis is difficult due to the wide overlap of spectra from multiple domains. Therefore, we have developed a materials informatics approach that combines the domain modeling (http://dmar.riken.jp/matrigica/) and the integrated analysis of meta-information (the elements, functional groups, additives, and physical properties) in polymer materials. Firstly, the ¹H-static ssNMR data of 120 polymer materials were subjected to a short-time Fourier transform to obtain frequency, intensity, and T₂ relaxation time for domains with different mobility. The average T₂ relaxation time of each domain is 0.96 ms for Mobile, 0.55 ms for Intermediate (Mobile), 0.32 ms for Intermediate (Rigid), and 0.11 ms for Rigid. Secondly, the estimated domain proportions were integrated with meta-information such as elements, functional group and thermophysical properties and was analyzed using a self-organization map and market basket analysis. This proposed method can contribute to explore structure–property relationships of polymer materials with multiple domains.

Identifying a Correlation among Qualitative Non-Numeric Parameters in Natural Fish Microbe Dataset Using Machine Learning

Recent technical innovations and developments in computer-based technology have enabled bioscience researchers to acquire comprehensive datasets and identify unique parameters within experimental datasets. However, field researchers may face the challenge that datasets exhibit few associations among any measurement results (e.g., from analytical instruments, phenotype observations as well as field environmental data), and may contain non-numerical, qualitative parameters, which make statistical analyses difficult. Here, we propose an advanced analysis scheme that combines two machine learning steps to mine association rules between non-numerical parameters. The aim of this analysis is to identify relationships between variables and enable the visualization of association rules from data of samples collected in the field, which have less correlations between genetic, physical, and non-numerical qualitative parameters. The analysis scheme presented here may increase the potential to identify important characteristics of big datasets.

Oral Pathobiont-Induced Changes in Gut Microbiota Aggravate the Pathology of Nonalcoholic Fatty Liver Disease in Mice

Background & Aims

Periodontitis increases the risk of nonalcoholic fatty liver disease (NAFLD); however, the underlying mechanisms are unclear. Here, we show that gut dysbiosis induced by oral administration of Porphyromonas gingivalis, a representative periodontopathic bacterium, is involved in the aggravation of NAFLD pathology.

Methods

C57BL/6N mice were administered either vehicle, P. gingivalis, or Prevotella intermedia, another periodontopathic bacterium with weaker periodontal pathogenicity, followed by feeding on a choline-deficient, l-amino acid-defined, high-fat diet with 60 kcal% fat and 0.1% methionine (CDAHFD60). The gut microbial communities were analyzed by pyrosequencing the 16S ribosomal RNA genes. Metagenomic analysis was used to determine the relative abundance of the Kyoto Encyclopedia of Genes and Genomes pathways encoded in the gut microbiota. Serum metabolites were analyzed using nuclear magnetic resonance-based metabolomics coupled with multivariate statistical analyses. Hepatic gene expression profiles were analyzed via DNA microarray and quantitative polymerase chain reaction.

Results

CDAHFD60 feeding induced hepatic steatosis, and in combination with bacterial administration, it further aggravated NAFLD pathology, thereby increasing fibrosis. Gene expression analysis of liver samples revealed that genes involved in NAFLD pathology were perturbed, and the two bacteria induced distinct expression profiles. This might be due to quantitative and qualitative differences in the influx of bacterial products in the gut because the serum endotoxin levels, compositions of the gut microbiota, and serum metabolite profiles induced by the ingested P. intermedia and P. gingivalis were different.

Conclusions

Swallowed periodontopathic bacteria aggravate NAFLD pathology, likely due to dysregulation of gene expression by inducing gut dysbiosis and subsequent influx of gut bacteria and/or bacterial products.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

Biomarkers of meat and seafood intake: an extensive literature review

Meat, including fish and shellfish, represents a valuable constituent of most balanced diets. Consumption of different types of meat and fish has been associated with both beneficial and adverse health effects. While white meats and fish are generally associated with positive health outcomes, red and especially processed meats have been associated with colorectal cancer and other diseases. The contribution of these foods to the development or prevention of chronic diseases is still not fully elucidated. One of the main problems is the difficulty in properly evaluating meat intake, as the existing self-reporting tools for dietary assessment may be imprecise and therefore affected by systematic and random errors. Dietary biomarkers measured in biological fluids have been proposed as possible objective measurements of the actual intake of specific foods and as a support for classical assessment methods. Good biomarkers for meat intake should reflect total dietary intake of meat, independent of source or processing and should be able to differentiate meat consumption from that of other protein-rich foods; alternatively, meat intake biomarkers should be specific to each of the different meat sources (e.g., red vs. white; fish, bird, or mammal) and/or cooking methods. In this paper, we present a systematic investigation of the scientific literature while providing a comprehensive overview of the possible biomarker(s) for the intake of different types of meat, including fish and shellfish, and processed and heated meats according to published guidelines for biomarker reviews (BFIrev). The most promising biomarkers are further validated for their usefulness for dietary assessment by published validation criteria.

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis

Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2000000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.

Systemic Homeostasis in Metabolome, Ionome, and Microbiome of Wild Yellowfin Goby in Estuarine Ecosystem

Data-driven approaches were applied to investigate the temporal and spatial changes of 1,022 individuals of wild yellowfin goby and its potential interaction with the estuarine environment in Japan. Nuclear magnetic resonance (NMR)-based metabolomics revealed that growth stage is a primary factor affecting muscle metabolism. Then, the metabolic, elemental and microbial profiles of the pooled samples generated according to either the same habitat or sampling season as well as the river water and sediment samples from their habitats were measured using NMR spectra, inductively coupled plasma optical emission spectrometry and next-generation 16 S rRNA gene sequencing. Hidden interactions in the integrated datasets such as the potential role of intestinal bacteria in the control of spawning migration, essential amino acids and fatty acids synthesis in wild yellowfin goby were further extracted using correlation clustering and market basket analysis-generated networks. Importantly, our systematic analysis of both the seasonal and latitudinal variations in metabolome, ionome and microbiome of wild yellowfin goby pointed out that the environmental factors such as the temperature play important roles in regulating the body homeostasis of wild fish.

Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet

Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general “linear” multivariate analysis alone limits interpretations due to “non-linear” variations in metabolic data from living organisms. Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data. To overcome the limitation of important variable (metabolite) determinations, we incorporated a random forest conditional variable importance measure into our KPCA-based analytical approach to demonstrate the relative importance of metabolites. Using a market basket analysis, hippurate, the most important variable detected in the importance measure, was associated with high levels of some vitamins and minerals present in foods eaten the previous day, suggesting a relationship between increased hippurate and intake of a wide variety of vegetables and fruits. Therefore, the KPCA-incorporated analytical approach described herein enabled us to capture input–output responses, and should be useful not only for metabolic profiling but also for profiling in other areas of biological and environmental systems.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Exploring the Impact of Food on the Gut Ecosystem Based on the Combination of Machine Learning and Network Visualization

Prebiotics and probiotics strongly impact the gut ecosystem by changing the composition and/or metabolism of the microbiota to improve the health of the host. However, the composition of the microbiota constantly changes due to the intake of daily diet. This shift in the microbiota composition has a considerable impact; however, non-pre/probiotic foods that have a low impact are ignored because of the lack of a highly sensitive evaluation method. We performed comprehensive acquisition of data using existing measurements (nuclear magnetic resonance, next-generation DNA sequencing, and inductively coupled plasma-optical emission spectroscopy) and analyses based on a combination of machine learning and network visualization, which extracted important factors by the Random Forest approach, and applied these factors to a network module. We used two pteridophytes, Pteridium aquilinum and Matteuccia struthiopteris, for the representative daily diet. This novel analytical method could detect the impact of a small but significant shift associated with Matteuccia struthiopteris but not Pteridium aquilinum intake, using the functional network module. In this study, we proposed a novel method that is useful to explore a new valuable food to improve the health of the host as pre/probiotics.

Trans-omics approaches used to characterise fish nutritional biorhythms in leopard coral grouper (Plectropomus leopardus)

Aquaculture is now a major supplier of fish, and has the potential to be a major source of protein in the future. Leopard coral groupers are traded in Asian markets as superior fish, and production via aquaculture has commenced. As feeding efficiency is of great concern in aquaculture, we sought to examine the metabolism of leopard coral groupers using trans-omics approaches. Metabolic mechanisms were comprehensively analysed using transcriptomic and metabolomic techniques. This study focused on the dynamics of muscular metabolites and gene expression. The omics data were discussed in light of circadian rhythms and fasting/feeding. The obtained data suggest that branched-chain amino acids played a role in energy generation in the fish muscle tissues during fasting. Moreover, glycolysis, TCA cycles, and purine metabolic substances exhibited circadian patterns, and gene expression also varied. This study is the first step to understanding the metabolic mechanisms of the leopard coral grouper.

NMR window of molecular complexity showing homeostasis in superorganisms

NMR offers tremendous advantages in the analyses of molecular complexity. The “big-data” are produced during the acquisition of fingerprints that must be stored and shared for posterior analysis and verifications.

FoodPro: A Web-Based Tool for Evaluating Covariance and Correlation NMR Spectra Associated with Food Processes

Foods from agriculture and fishery products are processed using various technologies. Molecular mixture analysis during food processing has the potential to help us understand the molecular mechanisms involved, thus enabling better cooking of the analyzed foods. To date, there has been no web-based tool focusing on accumulating Nuclear Magnetic Resonance (NMR) spectra from various types of food processing. Therefore, we have developed a novel web-based tool, FoodPro, that includes a food NMR spectrum database and computes covariance and correlation spectra to tasting and hardness. As a result, FoodPro has accumulated 236 aqueous (extracted in D₂O) and 131 hydrophobic (extracted in CDCl₃) experimental bench-top 60-MHz NMR spectra, 1753 tastings scored by volunteers, and 139 hardness measurements recorded by a penetrometer, all placed into a core database. The database content was roughly classified into fish and vegetable groups from the viewpoint of different spectrum patterns. FoodPro can query a user food NMR spectrum, search similar NMR spectra with a specified similarity threshold, and then compute estimated tasting and hardness, covariance, and correlation spectra to tasting and hardness. Querying fish spectra exemplified specific covariance spectra to tasting and hardness, giving positive covariance for tasting at 1.31 ppm for lactate and 3.47 ppm for glucose and a positive covariance for hardness at 3.26 ppm for trimethylamine N-oxide.