Metabolic dynamics analysis by massive data integration: application to tsunami-affected field soils in Japan

A new metabolic dynamics analysis approach has been developed in which massive data sets from time-series of ¹H and ¹³C NMR spectra are integrated in combination with microbial variability to characterize the biomass degradation process using field soil microbial communities. On the basis of correlation analyses that revealed relationships between various metabolites and bacteria, we efficiently monitored the metabolic dynamics of saccharides, amino acids, and organic acids, by assessing time-course changes in the microbial and metabolic profiles during biomass degradation. Specific bacteria were found to support specific steps of metabolic pathways in the degradation process of biomass to short chain fatty acids. We evaluated samples from agricultural and abandoned fields contaminated by the tsunami that followed the Great East earthquake in Japan. Metabolic dynamics and activities in the biomass degradation process differed considerably between soil from agricultural and abandoned fields. In particular, production levels of short chain fatty acids, such as acetate and propionate, which were considered to be produced by soil bacteria such as <i>Sedimentibacter</i> sp. and <i>Coprococcus</i> sp., were higher in the soil from agricultural fields than from abandoned fields. Our approach could characterize soil activity based on the metabolic dynamics of microbial communities in the biomass degradation process and should therefore be useful in future investigations of the environmental effects of natural disasters on soils

SpinCouple: Development of a Web Tool for Analyzing Metabolite Mixtures via Two-Dimensional <i>J</i>‑Resolved NMR Database

A new Web-based tool, SpinCouple, which is based on the accumulation of a two-dimensional (2D) ¹H–¹H <i>J</i>-resolved NMR database from 598 metabolite standards, has been developed. The spectra include both <i>J</i>-coupling and ¹H chemical shift information; those are applicable to a wide array of spectral annotation, especially for metabolic mixture samples that are difficult to label through the attachment of ¹³C isotopes. In addition, the user-friendly application includes an absolute-quantitative analysis tool. Good agreement was obtained between known concentrations of 20-metabolite mixtures versus the calibration curve-based quantification results obtained from 2D-<i>J</i>res spectra. We have examined the web tool availability using nine series of biological extracts, obtained from animal gut and waste treatment microbiota, fish, and plant tissues. This web-based tool is publicly available via http://emar.riken.jp/spincpl

Biogeochemical Typing of Paddy Field by a Data-Driven Approach Revealing Sub-Systems within a Complex Environment - A Pipeline to Filtrate, Organize and Frame Massive Dataset from Multi-Omics Analyses

<div><p>We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (¹H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems.</p></div

Community distributions of the BGC types over the sampling points.

<p>Distributions of relative abundances of communities identified as Archaea, 16S rRNA and 18S rRNA for the BGC types along the sampling points. X-axis: sampling points, Y-axis: relative abundance. Green shadows indicate sampling points on lentic waters and blue shadows indicate the sampling points over lotic waters. Below: a schematic drawing for the sampling points.</p

Schematic representation for the sampling location.

<p>Shadow map showing Kanto region (Japan). Grey area is Saitama prefecture. The rectangle indicates where the samples were taken. Magnified area is schematic map for the sampling site. At right side, schematic figure: Ara River was sampled in two points (ara1 and ara2) as well as paddy fields located within the area between these two river sampling points. Three independent paddy fields (paddy field 1, 2, 3) were selected and three samples were taken from each paddy field (p1f1-3, p2f1-3 and p3f1-3). These paddy fields were connected with Ara River through independent collector streams which were also sampled (p1s, p2s, p3s), respectively for each paddy field. Blue arrows indicate flow direction of Ara river and light brown arrows indicate each sampling points. Gaps in Ara River indicate bridges.</p

Delimiting BGC types.

<p>Plot of PCA scores for the extracted OTU matrix correlated with chemical profile. Four BGC types were delimited by k-means clustering. X-axis: PC1. Y-axis: PC2. BGC I: area enclosed in red with cross symbols, BGC II: area enclosed in blue with x symbols, BGC III: area enclosed in green with circular signals, BGC IV: area enclosed in pink with triangular signals. Arrows indicate the axes separating the BGC types; the quasi-horizontal arrow separates BGC III from BGC IV along the PC1 axis; the quasi-vertical arrow separates BGC I from BGC II along the PC2.</p

Schematic representation for the Biogeochemical Typing (BGC typing).

<p>Yellow box: steps for collection and pre-processing the samples. Orange box: steps for data acquisition and formatting for BGC typing. Red box: steps for BGC typing as the integrated statistical analyses.</p

Chemical profiles.

<p>The chemical profile for the BGC types were divided by ICP-OES, FT-IR and ¹H-NMR variables (omitted groups of variables are those with no high positive statistical dependence for the BGC type). Variables for ICP-OES are elements. Variables for FT-IR are the integrated area corresponding to the chemical bond in interval of wavelength (cm⁻¹) showed in parentheses. Variables for ¹H-NMR are the integrated area for the buckets (chemical shifts) in ppm; values in parentheses are chemical shifts assigned to the same compound or organic function.</p><p>Chemical profiles.</p

Chemical profiles for the sampling points.

<p>A) ICP-OES heatmap. X-axis: elements. Y-axis: sampling points. Red colour intensity corresponds to elemental concentration normalized by element. B) FT-IR spectra. X-axis: wavelength number. Y-axis: absorbance intensity. C) ¹H-NMR spectra. X-axis: chemical shift. Y-axis: intensity.</p

Effects of pH on metal adsorption.

<p><i>F</i>. <i>hygrometrica</i> protonemal cells were incubated with the metal solutions at the indicated pH values, and the unbound metals in the filtrates were quantified. Adsorption rate (%) = (initial concentration − final concentration) / initial concentration × 100.</p