Human Metabolic, Mineral, and Microbiota Fluctuations Across Daily

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Human metabolic, mineral, and microbiota fluctuations across daily nutritional intake visualized by a data-driven approach Takuma Misawa, Yasuhiro Date, and Jun Kikuchi J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr501194k • Publication Date (Web): 28 Jan 2015 Downloaded from http://pubs.acs.org on February 1, 2015

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Journal of Proteome Research

Human

metabolic,

fluctuations

across

mineral, daily

and

microbiota

nutritional

intake

visualized by a data-driven approach Takuma Misawa1, 2, Yasuhiro Date1, 2, and Jun Kikuchi1, 2, 3, 4,*

1

Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho,

Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan 2

RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku,

Yokohama, Kanagawa 230-0045, Japan 3

Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku,

Nagoya, Aichi 464-0810, Japan 4

Biomass Engineering Program, RIKEN Research Cluster for Innovation, 1-7-22 Suehiro-cho,

Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

Email Addresses: Takuma Misawa, [email protected] Yasuhiro Date, [email protected] Jun Kikuchi, [email protected] *Corresponding author Jun Kikuchi, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku,

Yokohama,

Kanagawa

230-0045,

Japan.

Tel:

+81455039439;

Fax:

+81455039489; Email: [email protected] 1

ACS Paragon Plus Environment


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ABSTRACT

Daily intake information is important for an understanding of the metabolic fluctuation of humans exposed to environmental stimuli. However, little investigation has been performed on the variations in dietary intake as an input and the relationship with human fecal, urinary, and salivary metabolic fluctuations as output information triggered by daily dietary intake. In the present study, we describe a data-driven approach for visualizing the daily intake information on a nutritional scale and for evaluating input–output responses under uncontrolled diets in a human study. For the input evaluation of nutritional intake, we collected information about daily dietary intake and converted this information into a numeric data of nutritional elements. Further, for the evaluation of output metabolic, mineral, and microbiota responses, we characterized the metabolic, mineral, and microbiota variations of non-invasive human samples of feces, urine, and saliva. The data-driven approach captured significant differences in the fluctuation of intestinal microbiota and some metabolites caused by a high-protein and a high-fat diet in daily life. This approach should contribute to the metabolic assessment of humans affected by environmental and nutritional factors under unlimited and uncontrolled diets.

KEYWORDS: non-invasive human sampling, metabonomics, dietary intake variations, nutrient digitalization, comprehensive significant test, data-driven approach

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Introduction Human beings, with trillions of microbes in their intestinal tracts as symbionts,1 are considered superorganisms.2 These microbial symbionts provide characteristic features that the hosts do not perform by themselves and may provide essential nutrients and micronutrients or process indigestible components in the diet.3, 4 The metabolic pathways resulting from dietary intakes are interactively modulated by the microbial symbionts with human cells and organs in the intestines.5 Thus, the cooperative interactions and modulations in metabolism caused by the host and symbiotic microbiota are significant mechanisms for the survival of human beings and human health. Although the metabolic activities and capabilities modulated by the cooperative interactions in the intestinal symbiotic ecosystems are significantly influenced by variations and alterations in dietary intakes, the intestinal symbiotic ecosystems appropriately respond to the impact of dietary intake and maintain metabolic homeostasis.6, 7 The metabolic modulations by intestinal symbiotic ecosystems have been actively investigated in terms of microbial composition, variation, and diversity with host–microbial interactions.8, 9 However, little investigation has been performed regarding the variation in dietary intake as an input and the relationship with metabolic fluctuations for all human fecal, urinary, and salivary samples together triggered by dietary intakes as an output information. Considering the intricate characteristics of both dietary intake and metabolic response inherent to human beings, many studies have employed the holistic approach to various analytical data to acquire the maximum possible information. Due to the massive amount of data produced from various analyses, results are difficult to visualize and process. The data-driven approach is a pipeline that was built using integrative statistical analysis that can examine such huge datasets by filtering, organizing, and framing the information based on the strength of the various mutual trends of the fluctuations in organisms and chemicals 3



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simultaneously occurring in the human environment.10, 11 In contrast, the nuclear magnetic resonance (NMR)-based metabonomic approach is widely used for investigating the metabolic characterization in mammals and their intestinal symbiotic systems.12-24 Particularly in human studies, metabolic characterizations from non-invasive samples, such as urine, saliva, and feces are well known to have a large quantity of information for human health and disease.25-28 Here, we describe an advanced analytical approach for evaluating the metabolic, mineral, and microbiota fluctuations of all fecal, urinary, and salivary samples from humans together and synchronously related with variations in dietary intakes under unlimited and uncontrolled diets. In this study, we used non-invasive human sampling in combination with an NMR-based metabonomic approach, inductively coupled plasma-optical emission spectrometry (ICP-OES)-based ionomic approach, and next-generation sequencer (MiSeq)-based microbiome approach. Using these huge datasets, we introduced the data-driven approach for visualization of input–output variations and characterization of metabolic profiles modulated by microbiota in the symbiotic systems of humans.

Materials and Methods Human subjects and experimental design We obtained the new data on metabolite, microbial, and daily intake derived from eight volunteers. This eight healthy volunteers (aged 23–43 years) who were selected by us (Table S1) gave written informed consent to participate in this study before the examination. In addition to this, a total of sixteen volunteers were analyzed in the first experiment, in order to compare the obtained new data and previously published authorized data of eight volunteers.29 Since the importance of the fluctuation triggered by food intakes was observed in the total of sixteen volunteers data, we focused on the metabolic and microbiota 4


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fluctuations triggered by food intakes using the new eight volunteers who were selected by us (i.e., the authorized data were not used for further analyses).

The eight volunteers were

requested to take a photograph of each meal before it was eaten (Movie S1). In addition, information concerning beverages and snacks was collected from the volunteers by capturing photographs, or memo (Table S2). All human experiments were approved by the human ethical committees of RIKEN Yokohama Research Institute and Yokohama City University.

Calculation of daily dietary information The photographs of the diets (n = 443 day points from 1 to 4 months) was converted to numerical data of nutritional elements. We used questionnaires and interviews secondarily when necessary information was lacking; for example, if a daily intake picture was not submitted, or a comparison material necessary for estimation of the food amount was lacking (Table S2). These elements included the contents of carbohydrates, fats, dietary fiber, minerals, proteins, vitamins, and the protein, fat, and carbohydrate (PFC) ratio, which were calculated using the Microsoft Excel Add-In software “Excel Eiyo-kun Ver. 6.0” (Kenpakusha, Tokyo, Japan) (Figure S1) as in a previous study.30, 31 Excel Eiyo-kun has been developed based on the Shikoku University Nutrition Database. The software accepts the input of names of foodstuffs and dishes. The input information is automatically decomposed by the software into its constituent parts into nutritional-scale data. Nutritional analysis is thereby made available to those lacking extensive knowledge of nutrition.

Sample collections The eight volunteers who were selected by us provided salivary, urinary, and fecal samples over the course of the experiment. The timing of all sample collection depended on each volunteer as summarized in Figure S2. We only requested that volunteers take the 5



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samples at their convenience. As a result, salivary samples (n = 279) were obtained using oral swabs (Salivette, Sarstedt, Tokyo, Japan), which were kept in the mouth for 3 min and repeated three times during each collection. The oral swabs of salivary samples were centrifuged at 3100 × g for 2 min, and the collected aliquots were concentrated to approximately triple to quadruple strength using a centrifugal evaporator (EYELA CVE-200D, Tokyo Rikakikai Co., Ltd., Tokyo, Japan) for increasing the number of detected signals and for enhancing the detected signals

considering the required sample volume.

Further, the condensed salivary samples were then immediately stored at −30°C until analysis. Urine samples (n = 325) were collected from midstream urine using paper cups and stored at −30°C until analysis as well as in reference to previous human metabonomic studies.32 In this study, urine sample was rapidly collected and preservatives were not used. Feces (n = 178) samples were obtained using a plastic spoon and a paper sheet (Nagase-ru, Atleta Inc., Osaka, Japan) and immediately stored at −30°C until analysis.

NMR spectroscopy To evaluate the metabolic profiles of the saliva and urine, 540 µL of the condensed saliva or the collected urine samples were mixed with 60 µL of phosphate buffer solution (0.1 M K2HPO4/KH2PO4, pH 7.0) containing 90% deuterium oxide (D2O) and 10 mM sodium 2,2-dimethyl-2-silapentane-5-sulfonate (DSS) as an internal standard for NMR spectroscopy. For extraction, 10 mg of the freeze-dried fecal samples were extracted with 800 µL of phosphate buffer solution containing 90% D2O and 1 mM DSS according to a previous report.33, 34 The NMR spectra were acquired at 298 K with a Bruker AVENCEII-700 spectrometer equipped with a 1H inverse triple-resonance cryoprobe with Z-axis gradient (Bruker Biospin, Rheinstetten, Germany). For 1H NMR measurements, one-dimensional (1D) NOE correlated spectroscopy (NOESY) pulse sequence with pre-saturation during relaxation 6


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delay (3 s) and mixing time (10 ms) were used for suppressing water resonance. Fecal and salivary samples were thought to contain higher levels of high molecular weight molecular species than urinary samples. The difference in mixing time was accordingly investigated to optimize the protocol for optimal measurement of fecal and salivary samples (Figure S3). There was a trend of decreased intensity with increased mixing time in the high molecular weight region in fecal and salivary samples. Thus, the mixing time was set to 10 ms for all samples including urinary samples. In the 1H NMR spectra, 65,536 data points with a spectral width of 14,098 Hz were collected with 32 transients and 4 dummy scans. In addition, 132 fecal samples were measured by WATERGATE pulse program for comparison with previously published data.29, 36 Prior to Fourier transformation, the free induction decays were multiplied by an exponential window function corresponding to a 0.3 Hz line broadening factor. The NMR spectra were processed using TopSpin 3.1 (Bruker). The two-dimensional (2D) 1H–13C heteronuclear single quantum coherence (HSQC) method for NMR measurements has been previously described.37-39 In brief, 128 complex f1 (13C) and 1024 complex f2 (1H) points were recorded from 256 scans per f1 increment. The obtained spectral widths were 40 and 14 ppm for f1 and f2, respectively. Before Fourier transformation, the free induction decays were multiplied by an exponential window function corresponding to 0.3 Hz (f1) and 1.0 Hz (f2) line broadening factors. Furthermore, peak annotations were made using the SpinAssign program on the PRIMe website (http://prime.psc.riken.jp/)40-42 and the Human Metabolome Database (http://www.hmdb.ca/)43 as well as in reference to previous human metabonomic studies.35, 44-47

ICP-OES analysis To evaluate mineral profiles as output information, 92 urine and 106 feces samples 7



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were measured by ICP-OES (SPS5510, SII Nano-Technology, Chiba, Japan) as previously described.11 For ICP-OES analysis, 10 mg of feces were extracted by nitric acid as previously described.33 The extracted feces samples and 1 mL of the urine samples were diluted to 1:10 and 1:100, respectively, for ICP-OES measurements.

Microbiota analysis To evaluate the microbiota profiles, 10 mg of the fecal samples (n = 44 samples) were extracted according to a previous study.33 PCR for sequencing using MiSeq sequencer (Illumina, San Diego, CA, USA) was performed according to a previous report.48 The sequencing was performed on a MiSeq sequencer according to the manufacturer’s instructions. Moreover, the obtained data were analyzed using QIIME software (http://qiime.org/)49 and were expressed as operational taxonomic units; the results showing over 97% similarity were considered the same group.

Statistical analysis Nutritional intake data was classified by hierarchical cluster analysis (HCA) with Euclidean distance and complete linkage methods using R 3.1.0 software (http://www.R-project.org/). All 1D 1H NMR data were converted to numeric data by TopSpin and reduced by subdividing the spectra into sequential 0.04 ppm designated regions between 1H chemical shifts of –2–12 ppm using an originally constructed function on R software. The nutritional intake information classified by HCA was applied to the analytical (NMR, ICP-OES, and MiSeq sequencing) data, i.e., the analytical data was associated with the nutritional intake data with class information. Comprehensive significance tests based on the nutritional class information of the analytical data were performed using an originally constructed function of the R software. In addition, the constructed function was calculated 8


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based on an improved Wilcoxon rank sum test included in the R package “exactRankTests,” (http://cran.r-project.org/web/packages/exactRankTests/index.html) and was used to comprehensively analyze the significant differences for all the variables in the analytical data. Principal component analysis (PCA) was run using R software and performed according to a previous report.50

Results and Discussion Strategy of the data-driven approach Humans are exposed to several environmental variations caused by extraneous factors, such as daily nutritional intake. For example, we previously reported that fructooligosaccharides (FOS), known as a prebiotic food, affect metabolic fluctuations and microbiota variations in healthy humans. Therefore, we compared the authorized data reported by Kato et al.29 with the newly measured and analyzed fecal data. The results showed significant differences based on the principal component 1 of PCA between the FOS intakes or no FOS intake (Figure S4). From this result, we considered that metabolic fluctuations by daily nutritional intake are one of the important variations due to extraneous factors. To associate nutritional intake data with output fluctuation data, we collected information regarding daily dietary intakes from volunteers for approximately 1–4 months by photographs, questionnaires, and interviews and converted this information into a numeric data of nutritional elements, such as carbohydrates, fats, dietary fiber, minerals, proteins, and vitamins. The variation in input source of the nutritional elements were then analyzed by data-driven approach using HCA to mine and categorize characteristic information from all of the datasets (Figure 1).51 Based on the categorized information from the input data, categorized classes were applied to the three kinds of metabolic variations, three kinds of 9



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mineral variations, and microbiota fluctuations in feces. The output information was obtained by acquiring metabolic, mineral, and microbiota profiles from the non-invasive human samples. In this study, the output data was measured by NMR, ICP-OES, and MiSeq instruments. However, the samples obtained from volunteers F and H were measured only by NMR, in which sample preparation is comparatively easy, because the samples were provided after completion of all the measurements of the samples obtained from the other volunteers. The classified data were then comprehensively analyzed using a significance test based on a custom-written function named “Wilcox.exact” in the exactRankTests package of R, because the default Wilcoxon rank sum test (U-test) function in R is not suitable when the same value is included in the data set. From this analytical flow, the approach enabled us to obtain significant differences for the output information on the metabolic, mineral, and microbiota profiles based on the classified information of the input data.

Characterization of daily dietary intakes The experimental procedure for the calculations using the nutritional data from photographs, questionnaires, and interviews to numeric data was shown in Figure S1. In this figure, experimentally created models of Japanese-style, Western-style, and fast-food meals were converted to a numerical data of nutritional elements using the “Excel Eiyo-kun Ver. 6.0” (Figures S1a and S1b). In addition, from the converted nutritional data, PFC ratios were calculated and analyzed for models of Japanese-style, Western-style, and fast-food meals and for each volunteer (Figures 2a and S1c). From the PFC ratio analysis, the fat ratio in the volunteers was likely to be slightly higher but relatively similar compared with the recommended value from the Ministry of Agriculture, Forestry, and Fisheries of Japan (http://www.maff.go.jp/j/wpaper/w_maff/h21_h/trend/part1/chap2/c2_04.html). Moreover, compared with the model food meals, the balances of PFC ratios in these Japanese volunteers 10


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appeared to be relatively similar to the PFC balance of the fast-food meal. These results suggested that the Japanese volunteers seemed to have a trend of a dietary habit to eat fat-rich diets. Next, the numeric data for the nutritional information from the volunteers was categorized by the data-driven approach using HCA, resulting in two distinct clusters (Figure 3a). It was noted that the outlier (one day point), which was not categorized to clusters 1 or 2 was not considered for further analysis. In addition, the clusters 1 and 2 were further analyzed by PCA to characterize the key factors contributing to these clusters (Figures 3b and 3c). From the loading plot analysis, many amino acids and proteins were abundantly included in cluster 1. Moreover, cluster 1 showed relatively high values of proteins and fats, according to the PFC ratios compared to the cluster 2 (Figure 2b). These results indicate that cluster 1 is a group characterized by a nutritional intake of a high-fat and high-protein diet compared to cluster 2.

Characterization of human saliva samples using NMR-based metabolic profiling To characterize the non-invasive human samples (i.e., saliva, urine, and feces) affected by daily dietary intakes, an analytical method for human saliva samples was modified and optimized for the NMR measurements. We used oral swabs for salivary sampling to enhance the signals derived from low-molecular weight metabolites. In addition, to increase the detection of these signals, salivary samples were condensed using a centrifugal evaporator (Figure S5). Moreover, we evaluated the influence of food residues in the mouth. According to our data obtained by NMR, the food residues affected the metabolic profile up to 30 min after eating. In addition, the saliva metabolic profile was almost unaffected if the mouth had been rinsed with water (Figure S6). In this study, the saliva samples were collected after mouth washing. For annotations of the detected metabolites in the 1H NMR 11



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spectra, we performed 1H–13C HSQC NMR spectroscopy and statistical total correlation spectroscopy (STOCSY) analysis for salivary samples (Figure S7), whereas the metabolites detected in the 1H NMR spectra of the urinary and fecal samples were annotated by referring to previous reports.35, 44-47 In Figure S7, part of the quality check methods for the annotation of 1D NMR using the 2D NMR and STOCSY that had been performed are described. From these analyses, the detected signals from the 1H NMR spectra for urine, saliva, and feces were annotated and summarized in Table 1.

Investigation of individual, gender, and age differences There was no trend by factors, such as individual, gender, and age difference in nutritional intake information (Figure S8). However, there is the slight or powerful trend in organism samples. In contrast, there is a clear trend by the factor of individual difference in these output data, which is the metabolite and microbiome measured by NMR, ICP-OES, and MiSeq depending on the samples. Moreover, there is slight trend due to age difference. However, effects because of gender and age difference were unable to prove across the metabolic fluctuations due to the small sample size.

Metabolic, mineral, and microbiota fluctuations in saliva, urine, and feces associated with nutritional intakes Metabolic, mineral, and microbiota fluctuations in humans are triggered by dietary intakes. To evaluate the output metabolic, mineral, and microbiota phenotypes caused by dietary intakes, 1H NMR-based metabonomic and ICP-OES-based ionomic analyses were performed for saliva, urine, and feces with microbiota analysis of feces in all human volunteers. The representative 1H NMR spectra of saliva, urine, and feces are shown in Figure 4. For a further evaluation of the metabolic, mineral, and microbiota fluctuations 12


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associated with nutritional intake, a comprehensive analysis of a significant test based on the nutritional clusters 1 and 2 was performed on all datasets of the output information (including metabolic, mineral, and microbiota profiles) using an originally constructed function on R software (Figures 5 and S9–S14). The output data were divided into two classes based on the nutritional HCA. In addition, output data measured by NMR, ICP-OES, or MiSeq was applied one or two days before information because it has been reported that fluctuations in intestinal flora are affected by information of diet intake two days previously.1 In cluster 1 by HCA based on nutritional intakes data which is the group eaten high-protein diet in two days before, Oscillospira sp., Bacteroides uniformis, B. fragilis, B. ovatus, Streptococcus luteciae, Blautia sp., Ruminococcus gnavus, Lachnoospiraceae sp., Desulfovibrio sp., Megamonas sp., Clostridium sp., Sutterella sp., and Collinsella aerofaciens significantly increased in the high-protein diet compared to the low-protein diet in the microbiome data of feces samples measured using MiSeq. In contrast, when we examine the metabolites measured by NMR, the organic acids, such as acetate, fumarate, tyramine, butyrate, and propionate made by intestinal microbiota significantly decreased in the high-protein diet compared to the low-protein diet. It has been reported that proteins and fats have a high potential to cause microbial fluctuations.52 The volcano plot based on intestinal bacteria shows that anaerobes increased. However, the volcano plot based on metabolites shows that organic acids, which are made by intestinal bacteria decreased (Figure 5). It is conceivable that organic acid production as a whole decreased because the rate of bacteria, which mainly decompose sugars than amino acids decreased. Further, the production of organic acids using sugars instead of amino acids is more efficient. In addition, a decrease in the high-protein diet compared to the low-protein diet in Bacteroides sp. can be seen in Figure 5a. In contrast, Clostridium sp. decreased in the high-protein diet, as were some SCFAs such as acetate and butyrate (Figure 5b). The previous 13



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study has reported that the production of butyrate and acetate by Bacteroides sp. is lower than that by Clostridium sp.53 Therefore, our method enabled to capture the relationships between not only variations of nutritional intakes (input information) and microbiota/metabolites (output information) but also microbiota fluctuations and metabolic variations. Our data-driven approach is very useful to detect the microbiota and metabolites which are susceptible to daily intakes. Therefore, we considered that the strategy has good applications for evaluation of the microbiota fluctuations and metabolic variations caused by daily dietary intakes. By using our approach, it may be possible to discover lifestyle diseases triggered by collapse of the balance in metabolic pathway modulated by microbiota and their host and to construct prediction models for preventions of the diseases. In this study, we used all of the nutritional variables derived from the data calculated by software for estimating nutrition to cluster the input information. However, this method should be also able to focus on different targets of diet by modifying and optimizing the input information. The data-driven approach enabled us to evaluate the relationships between fecal, urinary, and salivary metabolites and the nutritional elements in dietary intakes and to track the co-variations of nutritional elements with metabolic fluctuations triggered by daily dietary intake. This study also focused on the digitization of nutritional elements in daily dietary intakes and the characterization of metabolic fluctuations in human non-invasive samples synchronously related with variations in dietary intakes. To the best of our knowledge, this is the first study to evaluate the aforementioned associations.

Acknowledgments The authors wish to thank Yuuri Tsuboi, Amiu Shino, Makiko Akama, Kenji Sakata, 14


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Koyuru Nakayama (RIKEN), Kengo Ito, Tatsuki Ogura, and Yuka Shiokawa (Yokohama City University) for stimulating discussions, technical assistance, and useful advice on NMR measurements, processing, and statistical analysis. The authors would like to thank Enago (www.enago.jp) for the English language review. This research was supported in part by a Grant-in-Aid for Scientific Research (Grant no. 25513012) (to J.K.) from Japan Society for the Promotion of Science (JSPS), and also partialy supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), “Technologies for creating next-generation agriculture, forestry and fisheries” funded from Bio-oriented Technology Research Advancement Institution, NARO)

Supporting Information Figure S1, Experimental procedure for calculation of numerical data of nutritional elements. Figure S2. The histograms based on sampling time which is depended on volunteer’s convenience in fecal (a), urinary (b) and salivary (c) samples. Figure S3, 1H NMR spectra normalized by DSS intensity of fecal and salivary samples based on different mixing times. Figure S4, Box plot based on PCA score for PC1 of 1H NMR spectra of fecal samples. Figure S5, 1H NMR spectra of salivary samples based on different condensation conditions. Figure S6, 1H NMR spectra of salivary samples based on different times following eating chocolate. Figure S7, HSQC NMR spectra and STOCSY analysis for annotations of salivary metabolites detected in 1H NMR metabonomic profiling. Figure S8, PCA score plot based on a) nutritional intake information, metabolome data in b)fecal, c)urinary, and d) salivary samples, ionome data in e) fecal and f) urinary samples and

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g) microbiome data derived from fecal samples. Each column shows the same PCA score plots, with only the color coding changed. Figure S9, volcano plot of 1H NMR for feces applied 0–2 days before nutritional intake information. Figure S10, volcano plot of 1H NMR for urine applied 0–2 days before nutritional intake information. Figure S11, volcano plot of 1H NMR for saliva applied 0–2 days before nutritional intake information. Figure S12, volcano plot of ICP-OES for feces applied 0–2 days before nutritional intakes information. Figure S13, volcano plot of ICP-OES for urine applied 0–2 days before nutritional intake information. Figure S14, volcano plot of microbiome data applied 0–2 days before nutritional intake information. Table S1, the information of the volunteers who recorded daily intakes. Table S2, A’s intake information of beverage and snacks excluding water in a month (November, 2013). Movie S1, part of the nutritional intake photographs obtained from volunteer A. This material is available free of charge via the Internet at http://pubs.acs.org.

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Figure legends

Figure 1. Flow chart for the method used in this study.

Figure 2. Protein, fat, and carbohydrate ratios of daily dietary intakes in (a) individual volunteers and (b) clusters based on hierarchical cluster analysis of nutritional intake information. REC: recommended value in Japan; model 1–3: typical Japanese, western, and fast-food style food diets are modeled as described in Fig S2; A–H: individuals; green region: protein ratio; red region: fat ratio; blue region: carbohydrate ratio.

Figure 3. (a) Clustering of nutritional trends for all individuals per day. The outlier was removed. (b) Principal component analysis (PCA) score plot based on nutritional intake information. (c) Only the top 30 materials based on absolute value of PC1 loadings coefficients are presented in the PCA loading plot of PC1. As a result, these materials are positive value only. From top to bottom in “*” descriptor, aspartic acid; tryptophan; threonine; arginine; the sum of all amino acids; the sum of all aromatic amino acids; phenylalanine; valine; serine; tyrosine; sulfur-containing amino acid; alanine; BCAAs; isoleucine; leucine; cystine; total proteins; methionine; lysine; glycine; energy from proteins; glutamic acid; histidine; proline.

Figure 4. Representative 1H NMR spectra for (a) feces, (b) urine, and (c) saliva. The numbers on the NMR peaks indicate the annotated metabolites listed in Table 1.

Figure 5. (a) Volcano plot of microbiome data applied two days before nutritional intake information. (b) Volcano plot of 1H NMR for feces applied two days before nutritional intake 21



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Page 22 of 30

information. Red circle means p value < 0.01. Blue triangle means 0.01 ≤ p- value < 0.05. The numbers indicate the following bacterial strains and metabolites: 1: Oscillospira sp.; 2: Bacteroides uniformis; 3: Bacteroides fragilis; 4: Streptococcus luteciae; 5: Blautia sp.; 6: Bacteroides ovatus; 7: Desulfovibrio sp.; 8: Ruminococcus gnavus; 9: Lachnospiraceae sp.; 10: Megamonas sp.; 11: Clostridium sp.; 12: Sutterella sp.; 13: Collinsella aerofaciens; 14: acetate; 15: fumarate; 16: Tyramine; 17: uracil; 18: propionate; 19: butyrate; 20: arginine; and 21: unidentified metabolites.

22


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 1. List of annotated metabolites detected in 1H NMR spectra for urine, saliva, and feces.

Compound

Chemical Shift (Multiplicity)

Included Sample

1 2

Acetate Acetone

1.91 ppm (s) 2.22 ppm (s)

feces, urine, saliva feces

3 4

Alanine Arginine

1.46 ppm (d) 1.68 ppm (m)

3.76 ppm (q) 1.90 ppm (m)

3.23 ppm (t)

5 6

Butyrate Cholic acid

0.88 ppm (t) 0.725 ppm (s)

1.55 ppm (m)

2.159 ppm (t)


7 8

Choline Citrate

3.9 ppm (s) 2.53 ppm (d)

3.51 ppm (dd) 2.65 ppm (d)

4.06 ppm (ddd)

feces, saliva urine, saliva

9 10

Creatinine Cystine

3.03 ppm (s) 3.18 ppm (dd)

4.05 ppm (s) 3.38 ppm (dd)

11 12

Ethanol Formate

1.17 ppm (t) 8.44 ppm (s)

3.65 ppm (q)

13 14

Fumarate Galactose

6.51 ppm (s) 3.48 ppm (dd)

3.64 ppm (dd)

3.72 ppm (m)

3.82 ppm (m)

feces, saliva urine, saliva

15

Glucose

3.92 ppm (d) 3.23 ppm (dd)

3.98 ppm (d) 3.40 ppm (m)

4.07 ppm (t) 3.46 ppm (m)

5.26 ppm (d) 3.52 ppm (dd)

feces

3.73 ppm (m) 5.22 ppm (d)

3.82 ppm (m)

3.89 ppm (dd)

4.63 ppm (d)

2.45 ppm (m)

3.77 ppm (t)

7.62 ppm (tt)

3.76 ppm (t)

feces, urine, saliva feces, urine

urine, saliva urine

4.10 ppm (dd)

urine, saliva feces, urine, saliva

16 17

Glutamine Glycine

2.16 ppm (m) 3.54 ppm (s)


18 19

Guanidoacetate Hippurate

3.78 ppm (s) 3.96 ppm (d)

7.54 ppm (none)

20 21

Hypoxanthine Isobutyrate

8.17 ppm (s) 1.21 ppm (d)

8.20 ppm (s) 2.59 ppm (m)

22

Isoleucine

0.92 ppm (t) 1.97 ppm (m)

1.00 ppm (d)

1.25 ppm (m)

23 24

Lactate Leucine

1.33 ppm (d) 0.94 ppm (t)

4.10 ppm (q) 1.70 ppm (m)

3.72 ppm (m)

25

Lysine

1.46 ppm (m) 3.074 ppm (t)

1.71 ppm (m)

1.89 ppm (m)

26 27

N-acetyl groups P-cresol

2.05 ppm (m) 2.25 ppm (s)

6.82 ppm (m)

7.13 ppm (dd)

28

Phenylalanine

3.19 ppm (m) 7.42 ppm (m)

3.98 ppm (dd)

7.32 ppm (d)

29 30

Propionate Pyruvate

1.04 ppm (t) 2.46 ppm (s)

2.16 ppm (q)

feces, urine, saliva feces, urine

31 32

Serine Succinate

3.83 ppm (dd) 2.39 ppm (s)

3.96 ppm (m)

feces, urine feces, saliva

33 34

TMA TMAO

2.89 ppm (s) 3.25 ppm (s)

35 36

Tyramine Tyrosine

2.92 ppm (t) 3.02 ppm (dd)

3.23 ppm (t) 3.17 ppm (dd)

37

Uracil

7.17 ppm (m) 5.79 ppm (d)

7.52 ppm (d)

7.82 ppm (dd)

urine urine urine urine, saliva

1.46 ppm (m)

feces, urine feces, urine, saliva feces, urine

3.02 ppm (t)

feces, urine, saliva urine, saliva feces, urine

7.36 ppm (m)

feces, urine, saliva

feces, urine feces, urine, saliva 6.90 ppm (d) 3.92 ppm (dd)

7.21 ppm (d) 6.88 ppm (m)

feces, urine saliva feces, urine

23



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

38

Urea

5.78 ppm (br, s)

39

Valine

0.997 ppm (d)

Page 24 of 30

urine, saliva 1.049 ppm (d)

2.26 ppm (m)

3.61 ppm (d)

feces, urine, saliva

24


Page 25 of 30

Cluster Dendrogram

15 24 204 311 32 18 28 16 236 29 10 253 26 277 8 12 135 11 30 142 9 19 22 17 21

0

1500000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


cluster 1

cluster 2

data-driven approach

ppm

55

60

anmr hclust (*, "complete")

65

70

75

80

85

90

7.0

6.5

6.0

5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

ppm

graphical abstract



input information

output information

daily intakes information (photographs)

human samples (feces, urine, saliva)

numerical estimation

metabolome ionome (NMR) (ICP-OES) microbiome (Miseq)

HCA creating a cluster based on nutritional trends

cluster B

apply data-driven approach classifying analytical data based on nutritional HCA

0

*

numerical conversion

cluster A

anmr hclust (*, "complete")

Cluster Dendrogram

15 24 20 314 321 18 28 16 23 296 10 25 263 27 7 128 13 115 30 14 2 199 22 17 21

1500000

Height

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 30

comprehensive significant difference test using R-language in house developed routine

Figure 1.


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a)

REC model1 model2 model3 A B C D E F G H

15

25

60

23.7

16.5

15.7

45.4

14.0

52.1

27.2

16.1

57.5 36.1

16.0

47.8

28.4

16.8

55.6

28.6

54.6

17.7

67.8

15.7

30.4

15.1

32.7

18.7

0

38.9

34.0

15.3

14.4

59.8

53.9 52.3 36.3

20

40

45.0

80

60

100

b) REC

15

25

20.0

cluster1

cluster2

31.5

14.5

0

60

48.5

28.9

20

56.6

40

60

PFC ratio (%) Figure 2.


80

100

PC2 (11.8%)

384 108 109 123 93 129 110 88 95 125 56 55 70 248 18 295 212 235 339 425 74 287 234 435 60 28 99 44 84 144 302 408 6 256 254 197 267 260 276 294 217 76 178 107 279 216 377 307 394 428 219 298 271 291 417 19 69 131 397 434 220 262 292 306 410 440 106 393 293 419 208 191 54 185 160 209 190 205 274 420 258 249 305 10 333 183 91 239 92 240 118 364 363 379 103 132 283 443 286 204 252 117 147 266 188 418 243 265 135 261 308 161 402 413 318 312 320 102 311 241 13 348 376 17 67 128 22 104 169 89 166 244 81 233 79 300 78 71 273 409 365 111 396 127 94 141 268 378 358 421 317 3 368 181 251 157 388 281 140 352 136 142 325 330 237 392 297 362 228 361 387 404 139 424 351 407 414 255 65 431 343 441 156 328 313 98 225 224 357 15 226 120 301 116 177 304 429 221 373 23 59 416 11 403 422 427 49 82 270 153 367 375 398 238 195 202 327 345 83 223 436 175 423 119 437 439 272 122 112 134 430 163 433 16 155 171 199 341 340 2 288 290 432 36 50 382 401 415 438 278 126 296 168 374 372 9 316 24 53 215 236 277 201 80 369 399 406 319 85 269 222 442 196 227 138 289 246 346 229 200 184 159 331 400 87 370 170 51 354 230 282 75 303 105 162 41 275 321 405 113 12 186 206 61 52 62 264 32 34 21 38 37 152 45 247 121 426 40 285 211 371 143 210 114 172 335 337 198 232 29 344 148 182 7 77 43 342 137 146 356 329 315 359 366 323 385 20 33 284 26 115 57 380 133 189 192 389 213 332 154 326 179 194 167 176 203 72 390 253 324 58 386 174 338 391 25 395 218 353 214 360 381 165 130 150 63 193 145 349 97 39 42 47 100 1 245 66 64 68 90 383 173 187 309 101 124 14 158 30 314 96 48 412 149 257 5 164 31 299 151 242 231 310 336 322 411 8 207 4 73 259 250 355 180 27 347 46 86 35 280 263 334 350

20

40

60

80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

0


cluster1

b) 15

10

-10

-15

-20

-10

PC1 (18.2%)

0

10

20

0.5

Figure 3.


Page 28 of 30

Cluster Dendrogram

a) n=443 method=“complete”

cluster2

c)

nmr_d hclust (*, "complete")

5

0

＊

-5

P

niacin simple proteins ammonia minerals

0.7

0.9

7.5

7.0

6.5

6.0

5.5

//

3 23 21 39 5 23

5

29

1

32 29 26

8

34 36

11 14 17

13

23 31 3

38

35

35 28

8.0

5 11

x1

29 1

x8

26 25

x1

23 18 9 16 17 34 10 33 9 8

x4

25 33 39 31 32 27 2 30 5 24 1 5 3 29 22 23 24 6 5

22 17 4 7

3

15 x1

19

19

12

c)

x6 38

20

b)

37

13

12

a)

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


28 27 35 27 35

Page 29 of 30

4.0 3.5 3.0 2.5 2.0 1.5 1.0

¹H NMR chemical shift (ppm) Figure 4.



low protein diet

11

7

2

4

12

-2.5

5

2.5

0

log2 fold change

low protein diet

22

5

10 9 8

13

b) 3

6

3

2

-5

Page 30 of 30

high protein diet

1

2 1

0

-log10 p-value

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

-log10 p-value

a) 4

high protein diet

15 16

21

17 14

21

21 15

18 1

0 -0.2

19 20

-0.1

0

log2 fold change

Figure 5.


0.1

0.2

Human Metabolic, Mineral, and Microbiota Fluctuations Across Daily

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