Artificial Soils Reveal Individual Factor Controls on Microbial Processes

Abstract

Soil matrix properties influence microbial behaviors that underlie nutrient cycling, greenhouse gas production, and soil formation. However, the dynamic and heterogeneous nature of soils makes it challenging to untangle the effects of different matrix properties on microbial behaviors. To address this challenge, we developed a tunable artificial soil recipe and used these materials to study the abiotic mechanisms driving soil microbial growth and communication. When we used standardized matrices with varying textures to culture gas-reporting biosensors, we found that a Gram-negative bacterium (Escherichia coli) grew best in synthetic silt soils, remaining active over a wide range of soil matric potentials, while a Gram-positive bacterium (Bacillus subtilis) preferred sandy soils, sporulating at low water potentials. Soil texture, mineralogy, and alkalinity all attenuated the bioavailability of an acyl-homoserine lactone (AHL) signaling molecule that controls community-level microbial behaviors. Texture controlled the timing of AHL sensing, while AHL bioavailability was decreased ~10⁵-fold by mineralogy and ~10³-fold by alkalinity. Finally, we built artificial soils with a range of complexities that converge on the properties of one Mollisol. As artificial soil complexity increased to more closely resemble the Mollisol, microbial behaviors approached those occurring in the natural soil, with the notable exception of organic matter. IMPORTANCE Understanding environmental controls on soil microbes is difficult because many abiotic parameters vary simultaneously and uncontrollably when different natural soils are compared, preventing mechanistic determination of any individual soil parameter's effect on microbial behaviors. We describe how soil texture, mineralogy, pH, and organic matter content can be varied individually within artificial soils to study their effects on soil microbes. Using microbial biosensors that report by producing a rare indicator gas, we identify soil properties that control microbial growth and attenuate the bioavailability of a diffusible chemical used to control community-level behaviors. We find that artificial soils differentially affect signal bioavailability and the growth of Gram-negative (Escherichia coli) and Gram-positive (Bacillus subtilis) microbes. These artificial soils are useful for studying the mechanisms that underlie soil controls on microbial fitness, signaling, and gene transfer.

Keywords: acylhomoserine lactone; artificial soils; biosensor; cell signaling; indicator gas; soil; synthetic biology; water retention curve.

FIG 1

Design and characterization of artificial soils. (A) Artificial soils produced by: (1) mixing quartz of different sizes together to provide texture; (2) adding clay minerals to vary mineralogy; (3) adjusting pH using CaCO₃; (4) aggregating using wet-dry cycles; and (5) hydrating to the desired water content (θ) and potential (ψ_m). (B) Three artificial soils (Q2a, M2a, and Q2x0.5) and a natural soil. (C) Water retention curves of soils that vary only in texture or (D) mineralogy. Plant available water follows the trend Q2a > Q3a > Q1. (E) Surface area of soils that vary in mineralogy. Error bars represent one standard deviation from three experiments.

FIG 2

Effect of particle size distribution on microbial growth. Genetic circuit used to program constitutive indicator gas production in (A) Ec-MHT and (B) Bs-MHT. In both strains, the MHT gene is chromosomally integrated and expressed using a constitutive promoter so that it is always on. CH₃Br production over time in (C) sand, Q1; (D) liquid; (E) silt loam, Q2a; and (F) clay, Q3a. For each measurement, 10⁶ CFU of Ec-MHT (circles) or Bs-MHT (squares) in 200 μL of MIDV1 medium were added to 2 mL glass vials containing 800 mg of soil. Vials were capped and incubated at 30°C. CH₃Br was measured using a GC-MS every 4 h for 40 h. Gas production was normalized to the maximum signal obtained. Error bars represent one standard deviation from three experiments.

FIG 3

Effect of OM on microbial growth in soils. Ec-MHT (10⁶ CFU in 200 μL of MIDV1) were added to 2 mL glass vials containing 800 mg of soils with differentOM source and amount. Vials were capped and incubated at 30°C. CH₃Br was measured using a GC-MS after 0 and 1 h then every 3 h thereafter. CH₃Br production over time in soils with Xanthan (square) or chitin (triangle) at 0.5% (white) or 1% (gray) (wt/wt). An artificial soil without addition of OM, silt loam Q2a soil (white circle), and a liquid control (gray circle) are shown. Experiments were performed in triplicate. Error bars indicate one standard deviation.

FIG 4

AHL bioavailability changes with soil particle size. (A) Ratiometric gas reporting approach to monitor cell growth (CO₂) and AHL sensing (CH₃Br). In this circuit, LasR activates MHT production and CH₃Br synthesis upon binding AHL. (B) To monitor AHL bioavailability, Ec-MHT (10⁸ cells) in MIDV1 medium (100 μL) were added to the bottom of 2 mL glass vials containing each soil (800 mg). AHL (1 μM) in MIDV1 medium (100 μL) was added to the top of the soil, and vials were capped and incubated at 30°C. CH₃Br and CO₂ were measured using GC-MS at time zero and 1 h after capping, and then every 3 h. (C) CO₂ production in the absence and (D) presence of AHL reveals that cells grow under both conditions. (E) CH₃Br production in the absence and (F) presence of AHL reveals that indicator gas production is AHL-dependent. (G) The ratio of CH₃Br/CO₂ allows for a comparison of the AHL sensed per cell in the presence and (H) absence of AHL. This data shows that soil texture affects the dynamics of AHL sensing. The dashed lines represent a fit to an exponential growth-decay model. Dots indicate average and error bars indicate one standard deviation calculated with n = 3.

FIG 5

Mineralogy and pH affect AHL bioavailability. (A) AHL bioavailability in soils with different mineralogy but the same texture. Ec-AHL-MHT (10⁸ CFU) were mixed with different concentrations of AHL and immediately added to vials containing artificial soils with different clay types in MIDV1. The medium contained 0.25M MOPS to obtain a ψ_m = −80 kPa. Vials were capped, and CH₃Br and CO₂ were measured using a GC-MS after 6 h. The dashed lines represent a Hill function fit to the data. With this fit, different k values were obtained for liquid (7.8 × 10⁻¹¹), Q2a (3.1 × 10⁻¹¹), K2a (2.8 × 10⁻¹¹), I2a (2.9 × 10⁻⁸), and M2a (≥4.2 × 10⁻⁶). (B) Different amounts of AHL were added in 100 μL of MIDV1 medium to vials containing artificial soils with different pH. AHL was incubated for 30 min in the soils before adding the AHL biosensor (10⁸ cells) in 100 μL of MIDV1 containing 0.25 M MOPS, pH 7.0 to achieve FC. Vials were capped and gas production was measured after 6 h. The CH₃Br/CO₂ ratio represents the per cell sensing of AHL. The dashed lines represent a Hill function fit to the data. With this fit, distinct k values are obtained with liquid (2.0 × 10⁻¹¹), Q3a (4.7 × 10⁻¹²), and Q3a-pH 8 (3.5 × 10⁻⁹). Error bars represent one standard deviation determined from three experiments.

FIG 6

AHL bioavailability in artificial soils that recreate different properties of a Mollisol. (A) Different concentrations of AHL were added to MIDV1 medium (300 μL) containing Ec-AHL-MHT (10⁸ CFU), and this mixture was mixed with a series of artificial soils (700 mg) that mimic different levels of complexity found in a Mollisol from Austin, TX. CH₃Br gas was normalized by the CO₂ signal measured after a 6-h incubation in closed vials. The dashed line indicates the Hill function fit to the data. PS = particle size, M = mineralogy, pH = addition of CaCO₃, OM = addition of xanthan gum, and NS = natural soil. In the case of PS+M soil, 0.25 M MOPS, pH 7.0 was included in the buffer to isolate the effect of mineralogy on bioavailability. (B) Maximum gas production (CH₃Br/CO₂) obtained from a fit of the data to the Hill equation reveals a decrease in maximum gas production as soil complexity increases (ordinary one-way ANOVA, Tukey’s multiple comparisons; **, P < 0.001, *, P < 0.03). (C) Amount of AHL (pM) necessary for a half maximum gas response. Artificial soil that recreates texture, mineralogy, and pH requires an AHL concentration within the same order of magnitude as the natural soil to induce the biosensor (nonparametric ANOVA, Dunn’s multiple comparisons; *, P < 0.03). Error bar represents one standard deviation calculated from three replicates.

FIG 7

Artificial soil applications. Artificial soils with different properties can be used to study (A) the bioavailability of a wide range of chemicals of interest, (B) the survival and distribution of microorganisms under different hydration conditions, (C) microbial metabolisms under different oxygen gradients and availability of cofactors, (D) soil formation, and (E) plant-fungi-bacteria interactions.