Phosphate-solubilizing bacteria improve the phytoremediation efficiency of <i>Wedelia trilobata</i> for Cu-contaminated soil

<p>In a controlled experiment, we assessed the effect of phosphate-solubilizing bacterium (PSB) on the soil metal (Cu²⁺) phytoremediation by <i>Wedelia trilobata</i> and examined the effect of the interaction of Cu contamination and PSB on the growth of <i>W. trilobata</i>. We also explored the effect of the interaction of Cu contamination and PSB on the soil microflora. The results showed that the removal efficiency of Cu from soil by <i>W. trilobata</i> increased with an increase in the concentration of PSB, and the translocation factors of Cu (<i>i.e.</i>, leaf:root and stem:root) were both significantly upregulated by PSB. The PSB significantly promoted the growth of <i>W. trilobata</i>; however, the effect of the Cu–PSB interaction on the leaf net photosynthetic rate (Pn) of <i>W. trilobata</i> was not significant, whereas copper contamination had a significant negative influence on the soil microflora, PSB had a significant positive influence on the soil microflora. Thus, PSB improved the phytoremediation efficiency of <i>W. trilobata</i> in Cu-contaminated soil because of the positive influence on the soil microflora, improving soil quality, which then increased the growth of <i>W. trilobata</i> in Cu-contaminated soil. The vigorous growth of <i>W. trlobata</i> led to higher of Cu absorption and translocation from soil as the ultimate result.</p

Plant Photosynthesis-Irradiance Curve Responses to Pollution Show Non-Competitive Inhibited Michaelis Kinetics

<div><p>Photosynthesis-irradiance (PI) curves are extensively used in field and laboratory research to evaluate the photon-use efficiency of plants. However, most existing models for PI curves focus on the relationship between the photosynthetic rate (Pn) and photosynthetically active radiation (PAR), and do not take account of the influence of environmental factors on the curve. In the present study, we used a new non-competitive inhibited Michaelis-Menten model (NIMM) to predict the co-variation of Pn, PAR, and the relative pollution index (<i>I</i>). We then evaluated the model with published data and our own experimental data. The results indicate that the Pn of plants decreased with increasing <i>I</i> in the environment and, as predicted, were all fitted well by the NIMM model. Therefore, our model provides a robust basis to evaluate and understand the influence of environmental pollution on plant photosynthesis.</p></div

Table_1_Unraveling the spatial–temporal distribution patterns of soil abundant and rare bacterial communities in China’s subtropical mountain forest.XLS

IntroductionThe pivotal roles of both abundant and rare bacteria in ecosystem function are widely acknowledged. Despite this, the diversity elevational patterns of these two bacterial taxa in different seasons and influencing factors remains underexplored, especially in the case of rare bacteria.MethodsHere, a metabarcoding approach was employed to investigate elevational patterns of these two bacterial communities in different seasons and tested the roles of soil physico-chemical properties in structuring these abundant and rare bacterial community.Results and discussionOur findings revealed that variation in elevation and season exerted notably effects on the rare bacterial diversity. Despite the reactions of abundant and rare communities to the elevational gradient exhibited similarities during both summer and winter, distinct elevational patterns were observed in their respective diversity. Specifically, abundant bacterial diversity exhibited a roughly U-shaped pattern along the elevation gradient, while rare bacterial diversity increased with the elevational gradient. Soil moisture and N:P were the dominant factor leading to the pronounced divergence in elevational distributions in summer. Soil temperature and pH were the key factors in winter. The network analysis revealed the bacteria are better able to adapt to environmental fluctuations during the summer season. Additionally, compared to abundant bacteria, the taxonomy of rare bacteria displayed a higher degree of complexity. Our discovery contributes to advancing our comprehension of intricate dynamic diversity patterns in abundant and rare bacteria in the context of environmental gradients and seasonal fluctuations.</p

The test results for the NIMM.

<p>a, in <i>T</i>. <i>pratense</i>; b, <i>in W</i>. <i>trilobata</i>; *** means significant at <i>P</i> ≤ 0.001.</p

Data_Sheet_1_Unraveling the spatial–temporal distribution patterns of soil abundant and rare bacterial communities in China’s subtropical mountain forest.docx

IntroductionThe pivotal roles of both abundant and rare bacteria in ecosystem function are widely acknowledged. Despite this, the diversity elevational patterns of these two bacterial taxa in different seasons and influencing factors remains underexplored, especially in the case of rare bacteria.MethodsHere, a metabarcoding approach was employed to investigate elevational patterns of these two bacterial communities in different seasons and tested the roles of soil physico-chemical properties in structuring these abundant and rare bacterial community.Results and discussionOur findings revealed that variation in elevation and season exerted notably effects on the rare bacterial diversity. Despite the reactions of abundant and rare communities to the elevational gradient exhibited similarities during both summer and winter, distinct elevational patterns were observed in their respective diversity. Specifically, abundant bacterial diversity exhibited a roughly U-shaped pattern along the elevation gradient, while rare bacterial diversity increased with the elevational gradient. Soil moisture and N:P were the dominant factor leading to the pronounced divergence in elevational distributions in summer. Soil temperature and pH were the key factors in winter. The network analysis revealed the bacteria are better able to adapt to environmental fluctuations during the summer season. Additionally, compared to abundant bacteria, the taxonomy of rare bacteria displayed a higher degree of complexity. Our discovery contributes to advancing our comprehension of intricate dynamic diversity patterns in abundant and rare bacteria in the context of environmental gradients and seasonal fluctuations.</p

Mathematical fitting of the PI curve using different models.

<p>AIC is Akaike's information criterion.</p

Effect of a pollutant on the normalized Pn under 1000 μmolphotonm^-2 s^-1 PAR.

<p>a, the normalized Pn of all five species regressed with respect to <i>I</i> using linear, power, exponential, and hyperbolic functions. b, the normalized Pn of each species regressed with respect to <i>I</i> using the hyperbolic function. AIC is Akaike's information criterion. ** means significant at <i>P</i> ≤ 0.01.</p

Data matrix for model establishing.

<p>Data matrix for model establishing.</p

Model testing results of the un-competitive inhibited and the competitive inhibited model.

<p>UIMM is the un-competitive inhibited Michaelis-Menten; CIMM is the competitive inhibited Michaelis-Menten; PAR_sat is the light saturation point; PAR_com is the light compensation point; P_m is the maximum photosynthetic rate; φ_c is the quantum efficiency at PAR_com; φ₀ is the intrinsic quantum efficiency; <math><mrow><msub><mrow>PAR</mrow><mrow>com</mrow></msub><mo>=</mo><mrow>Rd</mrow><mo>α</mo></mrow></math>, φ₀ = α∙[1+(β+γ)∙PAR_com],<math><mrow><msub><mo>φ</mo>c</msub><mo>=</mo><mo>α</mo><mo>⋅</mo><mrow><mn>1</mn><mo>+</mo><mrow><mo>(</mo><mrow><mo>β</mo><mo>+</mo><mo>γ</mo></mrow><mo>)</mo></mrow><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>com</mrow></msub><mo>−</mo><mo>β</mo><mo>⋅</mo><mo>γ</mo><mo>⋅</mo><msubsup><mrow>PAR</mrow><mrow>com</mrow><mn>2</mn></msubsup></mrow><mrow><mrow><mrow><mo>(</mo><mrow><mn>1</mn><mo>+</mo><mo>γ</mo><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>com</mrow></msub></mrow><mo>)</mo></mrow></mrow><mn>2</mn></mrow></mrow></math>, <math><mrow><msub><mrow>PAR</mrow><mrow>sat</mrow></msub><mo>=</mo><mrow><msqrt><mrow><mrow><mrow><mo>(</mo><mrow><mo>β</mo><mo>+</mo><mo>γ</mo></mrow><mo>)</mo></mrow><mo>⋅</mo><mrow><mo>(</mo><mrow><mn>1</mn><mo>+</mo><mo>γ</mo><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>com</mrow></msub></mrow><mo>)</mo></mrow></mrow><mo>β</mo></mrow></msqrt><mo>−</mo><mn>1</mn></mrow><mo>γ</mo></mrow></math>, <math><mrow><msub>Pm</msub><mo>=</mo><mrow><mo>α</mo><mo>⋅</mo><mrow><mo>(</mo><mrow><mn>1</mn><mo>−</mo><mo>β</mo><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>sat</mrow></msub></mrow><mo>)</mo></mrow><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>sat</mrow></msub></mrow><mrow><mn>1</mn><mo>+</mo><mo>γ</mo><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>sat</mrow></msub></mrow><mo>−</mo>Rd</mrow></math>;</p><p>*** means significant at <i>P</i> ≤ 0.001.</p><p>Model testing results of the un-competitive inhibited and the competitive inhibited model.</p

Model testing results of the NIMM.

<p>PAR_sat is light saturation point; PAR_com is light compensation point; P_m is maximum photosynthetic rate; φ_c is the quantum efficiency at PAR_com; φ₀ is intrinsic quantum efficiency; <math><mrow><msub><mrow>PAR</mrow><mrow>com</mrow></msub><mo>=</mo><mrow>Rd</mrow><mo>α</mo></mrow></math>, φ₀ = α∙[1+(β+γ)∙PAR_com], <math><mrow><msub><mo>φ</mo>c</msub><mo>=</mo><mo>α</mo><mo>⋅</mo><mrow><mn>1</mn><mo>+</mo><mrow><mo>(</mo><mrow><mo>β</mo><mo>+</mo><mo>γ</mo></mrow><mo>)</mo></mrow><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>com</mrow></msub><mo>−</mo><mo>β</mo><mo>⋅</mo><mo>γ</mo><mo>⋅</mo><msubsup><mrow>PAR</mrow><mrow>com</mrow><mn>2</mn></msubsup></mrow><mrow><mrow><mrow><mo>(</mo><mrow><mn>1</mn><mo>+</mo><mo>γ</mo><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>com</mrow></msub></mrow><mo>)</mo></mrow></mrow><mn>2</mn></mrow></mrow></math>, <math><mrow><msub><mrow>PAR</mrow><mrow>sat</mrow></msub><mo>=</mo><mrow><msqrt><mrow><mrow><mrow><mo>(</mo><mrow><mo>β</mo><mo>+</mo><mo>γ</mo></mrow><mo>)</mo></mrow><mo>⋅</mo><mrow><mo>(</mo><mrow><mn>1</mn><mo>+</mo><mo>γ</mo><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>com</mrow></msub></mrow><mo>)</mo></mrow></mrow><mo>β</mo></mrow></msqrt><mo>−</mo><mn>1</mn></mrow><mo>γ</mo></mrow></math>, <math><mrow><msub>Pm</msub><mo>=</mo><mrow><mo>α</mo><mo>⋅</mo><mrow><mo>(</mo><mrow><mn>1</mn><mo>−</mo><mo>β</mo><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>sat</mrow></msub></mrow><mo>)</mo></mrow><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>sat</mrow></msub></mrow><mrow><mn>1</mn><mo>+</mo><mo>γ</mo><mo>⋅</mo><msub><mrow>PAR</mrow><mrow>sat</mrow></msub></mrow><mo>−</mo>Rd</mrow></math>;</p><p>*** means significant at <i>P</i> ≤ 0.001.</p><p>Model testing results of the NIMM.</p