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Reconstruction of a soil microbial network induced by stress temperature

Cite this dataset

Yang, Dailin et al. (2022). Reconstruction of a soil microbial network induced by stress temperature [Dataset]. Dryad.


By applying a nonlinear time-series analysis to the metagenomic data of the soil microbiota cultured under suitable (30℃) or stressful (37℃) conditions, we show how the microbial interaction network responds to temperature stress. While the genera that persisted only under the suitable condition gave fewer positive effects, the genera that appeared only under the stressful condition received more positive effects in agreement with SGH. However, temperature changes also induce reconstruction of a community network, leading to an increased proportion of negative interactions at the whole community level. The anti-SGH pattern can be explained by the stronger competition caused by increased metabolic rate and population densities. 


The soil was sampled at depths of 5-10 cm from the surface in May 19, 2011, and large particles were removed with a 2-mm mesh sieve. First, the soil suspended in phosphate-buffered saline (PBS) was homogenized using a blender. Then, large soil particles were allowed to settle by weak centrifugation (420 × g, 10 min, 10°C), and cells in the supernatant were transferred to a flask. Afterward, the precipitated soil particles were subjected to two more sets of cell isolation, after which a pooled cell suspension in the flask obtained from 1 kg of the soil was filtered (pore size 7μm) to remove small soil particles, and centrifuged (8000 × g, 20 min, 10°C) to obtain cell pellets. 

The microbial community was inoculated into liquid medium in a Sakaguchi flask and incubated at 30 °C and 37 °C with shaking in triplicate, respectively. The composition of the liquid medium was as follows: 1/10-strength W medium (KH2PO4, 170 mg; Na2HPO4, 980 mg; (NH4)2SO4, 100 mg; MgSO4, 48.7 mg; FeSO4, 0.52 mg; MgO, 10.75 mg; CaCO3, 2.0 mg; ZnSO4, 0.81 mg; CuSO4, 0.16 mg; CoSO4, 0.15 mg; and H3BO3, 0.06 mg per liter), containing 10% the soil extract as a carbon source.

We extracted soil microbiota community DNA using DNeasy PowerSoil Kit (QIAGEN, Hilden, Germany). 

We amplified the V3-V4 region using 29 cycles of denaturing at 98 °C for 15 s, annealing at 55 °C for 30 s, and extension at 68 °C for 30 s. Subsequently, PCR products were purified using AmPure XP. Then, amplicon libraries were indexed with a Nextera XT index kit (Illumina), followed by sequencing with an Illumina Miseq platform, using a Miseq reagent kit v3 (300 cycles, paired-end).

We determined the total abundance of microbial community using qPCR with CFX Connect real-time system (Bio-Rad). DNA extraction efficiency was determined by adding a known concentration of DNA encoding the egfp gene as an internal standard.

We used a nonlinear causality test, UIC, to identify the interaction between various soil microbiota genera. UIC is based on the state-space reconstruction founded on the Takens Embedding Theorem, and it was developed based on CCM (Sugihara et al., 2012; Osada & Ushio 2021). To avoid indirect interactions due to long time lag as much as possible, we fixed the time lag to −1 (i.e., 0.5 days). Then, results were verified as significant using bootstrap and surrogate data (Thiel et al. 2006). Furthermore, a regularized S-map (Cenci et al. 2019) was used to determine the strength and sign of individual interactions. For this investigation, we used the mean of the S-map coefficients at each time point to represent the strength of the interactions between the two genera.

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Institute for Fermentation, Osaka

Tohoku University