Compatibility of X-ray computed tomography with plant gene expression, rhizosphere bacterial communities and enzyme activities

Plant Age and Soil Texture Rather Than the Presence of Root Hairs Cause Differences in Maize Resource Allocation and Root Gene Expression in the Field

Understanding the biological roles of root hairs is key to projecting their contributions to plant growth and to assess their relevance for plant breeding. The objective of this study was to assess the importance of root hairs for maize nutrition, carbon allocation and root gene expression in a field experiment. Applying wild type and root hairless rth3 maize grown on loam and sand, we examined the period of growth including 4-leaf, 9-leaf and tassel emergence stages, accompanied with a low precipitation rate. rth3 maize had lower shoot growth and lower total amounts of mineral nutrients than wild type, but the concentrations of mineral elements, root gene expression, or carbon allocation were largely unchanged. For these parameters, growth stage accounted for the main differences, followed by substrate. Substrate-related changes were pronounced during tassel emergence, where the concentrations of several elements in leaves as well as cell wall formation-related root gene expression and C allocation decreased. In conclusion, the presence of root hairs stimulated maize shoot growth and total nutrient uptake, but other parameters were more impacted by growth stage and soil texture. Further research should relate root hair functioning to the observed losses in maize productivity and growth efficiency.

New strategies on the application of artificial intelligence in the field of phytoremediation

Artificial Intelligence (AI) is expected to play a crucial role in the field of phytoremediation and its effective management in monitoring the growth of the plant in different contaminated soils and their phenotype characteristic such as the biomass of plants. This review focuses on recent applications of various AI techniques and remote sensing approaches in the field of phytoremediation to monitor plant growth with relevant morphological parameters using novel sensors, cameras, and associated modern technologies. Novel sensing and various measurement techniques are highlighted. Input parameters are used to develop futuristic models utilizing AI and statistical approaches. Additionally, a brief discussion has been presented on the use of AI techniques to detect metal hyperaccumulation in all parts of the plant, carbon capture, and sequestration along with its effect on food production to ensure food safety and security. This article highlights the application, limitation, and future perspectives of phytoremediation in monitoring the mobility, bioavailability, seasonal variation, effect of temperature on plant growth, and plant response to the heavy metals in soil by using the AI technique. Suggestions are made for future research in this area to analyze which would help to enhance plant growth and improve food security in long run.

Root exposure to apple replant disease soil triggers local defense response and rhizoplane microbiome dysbiosis

A soil column split-root experiment was designed to investigate the ability of apple replant disease (ARD)-causing agents to spread in soil. 'M26' apple rootstocks grew into a top layer of Control soil, followed by a barrier-free split-soil layer (Control soil/ARD soil). We observed a severely reduced root growth, concomitant with enhanced gene expression of phytoalexin biosynthetic genes and phytoalexin content in roots from ARD soil, indicating a pronounced local plant defense response. Amplicon sequencing (bacteria, archaea, fungi) revealed local shifts in diversity and composition of microorganisms in the rhizoplane of roots from ARD soil. An enrichment of operational taxonomic units affiliated to potential ARD fungal pathogens (Ilyonectria and Nectria sp.) and bacteria frequently associated with ARD (Streptomyces, Variovorax) was noted. In conclusion, our integrated study supports the idea of ARD being local and not spreading into surrounding soil, as only the roots in ARD soil were affected in terms of growth, phytoalexin biosynthetic gene expression, phytoalexin production and altered microbiome structure. This study further reinforces the microbiological nature of ARD, being likely triggered by a disturbed soil microbiome enriched with low mobility of the ARD-causing agents that induce a strong plant defense and rhizoplane microbiome dysbiosis, concurring with root damage.

Soil Texture, Sampling Depth and Root Hairs Shape the Structure of ACC Deaminase Bacterial Community Composition in Maize Rhizosphere

Preservation of the phytostimulatory functions of plant growth-promoting bacteria relies on the adaptation of their community to the rhizosphere environment. Here, an amplicon sequencing approach was implemented to specifically target microorganisms with 1-aminocyclopropane-1-carboxylate deaminase activity, carrying the acdS gene. We stated the hypothesis that the relative phylogenetic distribution of acdS carrying microorganisms is affected by the presence or absence of root hairs, soil type, and depth. To this end, a standardized soil column experiment was conducted with maize wild type and root hair defective rth3 mutant in the substrates loam and sand, and harvest was implemented from three depths. Most acdS sequences (99%) were affiliated to Actinobacteria and Proteobacteria, and the strongest influence on the relative abundances of sequences were exerted by the substrate. Variovorax, Acidovorax, and Ralstonia sequences dominated in loam, whereas Streptomyces and Agromyces were more abundant in sand. Soil depth caused strong variations in acdS sequence distribution, with differential levels in the relative abundances of acdS sequences affiliated to Tetrasphaera, Amycolatopsis, and Streptomyces in loam, but Burkholderia, Paraburkholderia, and Variovorax in sand. Maize genotype influenced the distribution of acdS sequences mainly in loam and only in the uppermost depth. Variovorax acdS sequences were more abundant in WT, but Streptomyces, Microbacterium, and Modestobacter in rth3 rhizosphere. Substrate and soil depth were strong and plant genotype a further significant single and interacting drivers of acdS carrying microbial community composition in the rhizosphere of maize. This suggests that maize rhizosphere acdS carrying bacterial community establishes according to the environmental constraints, and that root hairs possess a minor but significant impact on acdS carrying bacterial populations.

Microbial Identification, High-Resolution Microscopy and Spectrometry of the Rhizosphere in Its Native Spatial Context

During the past decades, several stand-alone and combinatorial methods have been developed to investigate the chemistry (i.e., mapping of elemental, isotopic, and molecular composition) and the role of microbes in soil and rhizosphere. However, none of these approaches are currently applicable to characterize soil-root-microbe interactions simultaneously in their spatial arrangement. Here we present a novel approach that allows for simultaneous microbial identification and chemical analysis of the rhizosphere at micro- to nano-meter spatial resolution. Our approach includes (i) a resin embedding and sectioning method suitable for simultaneous correlative characterization of Zea mays rhizosphere, (ii) an analytical work flow that allows up to six instruments/techniques to be used correlatively, and (iii) data and image correlation. Hydrophilic, immunohistochemistry compatible, low viscosity LR white resin was used to embed the rhizosphere sample. We employed waterjet cutting and avoided polishing the surface to prevent smearing of the sample surface at nanoscale. The quality of embedding was analyzed by Helium Ion Microscopy (HIM). Bacteria in the embedded soil were identified by Catalyzed Reporter Deposition-Fluorescence in situ Hybridization (CARD-FISH) to avoid interferences from high levels of autofluorescence emitted by soil particles and organic matter. Chemical mapping of the rhizosphere was done by Scanning Electron Microscopy (SEM) with Energy-dispersive X-ray analysis (SEM-EDX), Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS), nano-focused Secondary Ion mass Spectrometry (nanoSIMS), and confocal Raman spectroscopy (μ-Raman). High-resolution correlative characterization by six different techniques followed by image registration shows that this method can meet the demanding requirements of multiple characterization techniques to identify spatial organization of bacteria and chemically map the rhizosphere. Finally, we presented individual and correlative workflows for imaging and image registration to analyze data. We hope this method will be a platform to combine various 2D analytics for an improved understanding of the rhizosphere processes and their ecological significance.