To extract DNA, the samples were resuspended in a lysis buffer and microbial cells were mechanically lysed through bead beating. For all bulk soil and rhizosphere data, bead beating and purification were performed with the MoBio PowerSoil kit (SDS/mechanical lysis) because of its unmatched ability to remove humics and other PCR inhibitors in our soil. EC DNA from Arabidopsis experiments was prepared with the MP Bio Fast DNA Spin Kit for soil (also a SDS/mechanical lysis) because the more intense bead-beating protocol and lysis matrix gave improved lysis of whole roots and higher DNA yield, and soil PCR inhibitors were less of a problem with these samples. Our procedure yielded around 1 μg of DNA per rhizosphere sample, and more total DNA for EC samples (although a significant portion of EC DNA sequenced was of host origin). Although MoBio Powersoil and MP Bio Fast DNA use highly similar bead-beating/mechanical lysis methods, we developed a custom method of sample pre-homogenization that allowed us to prepare some EC samples using the MoBio kit. A comparison of Col-0 fractions soil, rhizosphere and EC across four soil digs of MF, where EC was prepared using MoBio in two digs and MP Bio in the other two digs, shows that although we cannot rule out a slight kit effect, both kits produce highly similar clustering separating EC from rhizosphere and soil fractions (Supplementary Fig. 6 , replicates 3 and 4). DNA quantity was assessed with the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen) and a plate fluorospectrometer.
>
Phenomena
>
Natural Phenomenon or Process
>
Rhizosphere
Rhizosphere
The rhizosphere is the narrow zone of soil that is directly influenced by root secretions and associated microorganisms.
It is a critical interface where complex interactions between plant roots, soil, and microbes occur, driving nutrient cycling, plant health, and ecosystem function.
Rhizosphere processes are essential for sustainable agriculture, environmental remediation, and understanding belowground plant-microbial dynamics.
Researchers studying the rhizosphere can leverage PubCompare.ai's AI-driven platform to optimize their research by locating the best protocols from literature, preprints, and patents, enhancing reproducibility and accuracy through AI-assisted protocol selection.
It is a critical interface where complex interactions between plant roots, soil, and microbes occur, driving nutrient cycling, plant health, and ecosystem function.
Rhizosphere processes are essential for sustainable agriculture, environmental remediation, and understanding belowground plant-microbial dynamics.
Researchers studying the rhizosphere can leverage PubCompare.ai's AI-driven platform to optimize their research by locating the best protocols from literature, preprints, and patents, enhancing reproducibility and accuracy through AI-assisted protocol selection.
Most cited protocols related to «Rhizosphere»
Arabidopsis
Biological Assay
Buffers
Cells
Dietary Fiber
DNA, A-Form
DNA, Double-Stranded
inhibitors
PicoGreen
Plant Roots
Rhizosphere
To compare the speed and trimming results of ITSxpress, we compared the ITS1 and ITS2 sequences from 15 soil samples collected from the rhizosphere of maize in fields with different winter cover crops. ITS1 reads were amplified using the ITS1F/ITS2 primer set (
Gardes & Bruns, 1993 (link);
White
et al., 1990 ). ITS2 reads were amplified using the ITS3/ITS4 primer set (
White
et al., 1990 ). Reads were multiplexed and sequenced on an Illumina Miseq in 2x300bp run mode using version 3.0 chemistry.
Tests of ITSxpress and ITSx performance were run on single compute nodes with 2 x 10 core Intel Xeon Processors (E5-2670 v2 2.50GHz 25MB cache) with hyper-threading enabled, 128GB DDR3 ECC memory and two Intel DC S3500 Series SATA 6.0Gb/s SSDs. For the first test of trimming speed, 5 replicates were run where 15 ITS1 and 15 ITS2 samples were trimmed using ITSxpress and ITSx with 4 logical cores. Trimming was done using ITSxpress with default settings. ITSx was run with multithreading and heuristic filtering turned on and only the fungal database selected. The running times for ITSx and ITSxpress were plotted on a log scale,
Figure 1 . The number of total reads in each sample and reads remaining after clustering at 99.5% identity are shown on a log scale,
Figure 2 .
To compare the performance of ITSxpress and ITSx as computer cores were added, tests were run on the ITS1 and ITS2 sample with the largest numbers of sequences (ITS1: n=100543 16% unique, ITS2: n=145499, 30% unique). The sample was processed 5 times with 1, 4, 8, 16, 30, and 40 virtual compute cores. The mean and standard error were plotted,
Figure 3 . Program settings were the same as in the first test.
The trimming positions from ITSx and ITSxpress were compared for every ITS1 and ITS2 sequence. If a read was not trimmed identically by ITSx and ITSxpress, it was globally aligned and the start and stop positions were compared. Alignment was done using the Biopython Pairwise2 implementation of a global alignment function with the parameters (match score: 2, mismatch penalty: -1, gap opening penalty: -0.5, gap extension penalty: -0.1) (
Cock
et al., 2009 ).
Gardes & Bruns, 1993 (link);
et al., 1990
et al., 1990
Tests of ITSxpress and ITSx performance were run on single compute nodes with 2 x 10 core Intel Xeon Processors (E5-2670 v2 2.50GHz 25MB cache) with hyper-threading enabled, 128GB DDR3 ECC memory and two Intel DC S3500 Series SATA 6.0Gb/s SSDs. For the first test of trimming speed, 5 replicates were run where 15 ITS1 and 15 ITS2 samples were trimmed using ITSxpress and ITSx with 4 logical cores. Trimming was done using ITSxpress with default settings. ITSx was run with multithreading and heuristic filtering turned on and only the fungal database selected. The running times for ITSx and ITSxpress were plotted on a log scale,
To compare the performance of ITSxpress and ITSx as computer cores were added, tests were run on the ITS1 and ITS2 sample with the largest numbers of sequences (ITS1: n=100543 16% unique, ITS2: n=145499, 30% unique). The sample was processed 5 times with 1, 4, 8, 16, 30, and 40 virtual compute cores. The mean and standard error were plotted,
The trimming positions from ITSx and ITSxpress were compared for every ITS1 and ITS2 sequence. If a read was not trimmed identically by ITSx and ITSxpress, it was globally aligned and the start and stop positions were compared. Alignment was done using the Biopython Pairwise2 implementation of a global alignment function with the parameters (match score: 2, mismatch penalty: -1, gap opening penalty: -0.5, gap extension penalty: -0.1) (
et al., 2009
Full text: Click here
Crop, Avian
Memory
N-hydroxysuccinimide S-acetylthioacetate
Oligonucleotide Primers
Rhizosphere
SSD
Zea mays
Microbial community analysis was conducted as in the study by Naylor et al. (13 (link)) on soil, rhizosphere, and root endosphere samples of two sorghum cultivars collected from field-grown plants in Parlier, California (36.6008°N, 119.5109°W). Metatranscriptome profiling, metabolomics, qPCR, HPLC analysis, bacterial isolation and conjugation, inoculation experiments, and confocal microscopy were performed as described in SI Appendix, Material and Methods . A combined assembly of the metatranscriptome can be accessed and downloaded via integrated microbial genomes (IMG)/expert review (ER) (https://img.jgi.doe.gov/ ) using the IMG ID 3300017790.
Bacteria
Genome, Microbial
High-Performance Liquid Chromatographies
isolation
Microbial Community
Microscopy, Confocal
Plant Roots
Plants
Rhizosphere
Sorghum
Vaccination
Apicoectomy
Bacteria
Buffers
Carbon
Detergents
Genotype
Inflorescence
Meristem
Photosynthesis
Plant Roots
Plants
Reproduction
Rhizosphere
Specimen Collection
Sterility, Reproductive
There are technical drivers of research priorities: the tools we have. As soon as certain aspects of plant functioning become measurable, we start using those tools and assign overarching significance to these measurements, perhaps, because we aim at doing important things, simply because we are important, at least to ourselves. Things we cannot measure or observe become a matter of unimportance. Although we will continue to depend on scientific tools and their availability, the challenge is, not to get trapped in studying what we have tools for, but to go beyond, based on the challenges posed by theory, developing novel approaches that will permit us entering the terrain that remained largely unexplored for methodological reasons.
As was explained above, water shortage and low temperature, and to some extent nutrient shortage, are not primarily affecting plant carbon capture (photosynthesis), but rather affect tissue formation directly. Well known in plant physiology, plant ecologists tend to overlook the great sensitivity of meristematic tissues to low turgor pressure, low temperature, and shortage in key nutrients. These tissues stop building new cells at water potentials, low temperatures, and critically low nutrient supply that still permit reasonably high rates of photosynthetic CO2 uptake. Not surprisingly, the initial response of plants to such tissue-level growth constraints leads to an accumulation of non-structural carbon-metabolites (osmotically inactive ones such as starch and lipids), rather than to carbon-starvation (Körner, 2003 (link)). This discrepancy between awareness and reality roots in the convenient tools and techniques we have to measure photosynthesis and the absence of tools to monitor cell division and cell differentiation in situ, and/or to assess discrepancies between demand and supply of photoassimilates.
Another example for our methods driven priorities is the generally great significance attributed to air conditioning or to climate aspects in general when manipulative experiments are designed (e.g., CO2-enrichment works), although soils exert far greater influences on plant responses to what ever treatment we apply. I invite readers to check the length authors spend on describing atmospheric conditions in their experiments versus the soil conditions. The simple reason is that we can engineer atmospheric conditions, but we have no means to engineer plant–soil or plant–soil–microbe interactions, as decisive these might be. To my knowledge, the only experiment where the response of plants to a high CO2-environment were tested with plants growing in two different soil types (see Spinnler et al., 2002 (link)), revealed two different story lines, just depending on which soil was chosen. The challenge is to arrive at a broad appreciation that soil conditions (e.g., disturbed or undisturbed) are pre-determining experimental results. I join Högberg et al. (2005 ) in their viewing soil microbiota associated with roots as an integral part of plant functioning, to the extent, that they may actually be seen as part of the autotrophic system, rather than belonging to the heterotrophic world.
On a similar avenue, root research was and still is a minor fraction compared to leaf research, although there is no theoretical reason for such a posteriority. Both are equally significant, in fact roots may be more influential with respect to limiting resources. The only reason is methodology. While a leaf can be studied in isolation (e.g., some sensors mounted to it), a root does not function properly without its intact rhizosphere, apart from its poor visibility. We can “bring” the atmosphere into the lab (growth chambers), but we cannot bring a coupled rhizosphere to the lab. Any pot experiment is confounded as soon as plants respond differently to two treatments, because, inevitably, the treatment changes the root-space/plant size relationship. So the challenge here is testing hypothesis on plant responses with plants grown with unconstrained, well-developed soil biota in action. Most commonly, this can only be done in the field.
As was explained above, water shortage and low temperature, and to some extent nutrient shortage, are not primarily affecting plant carbon capture (photosynthesis), but rather affect tissue formation directly. Well known in plant physiology, plant ecologists tend to overlook the great sensitivity of meristematic tissues to low turgor pressure, low temperature, and shortage in key nutrients. These tissues stop building new cells at water potentials, low temperatures, and critically low nutrient supply that still permit reasonably high rates of photosynthetic CO2 uptake. Not surprisingly, the initial response of plants to such tissue-level growth constraints leads to an accumulation of non-structural carbon-metabolites (osmotically inactive ones such as starch and lipids), rather than to carbon-starvation (Körner, 2003 (link)). This discrepancy between awareness and reality roots in the convenient tools and techniques we have to measure photosynthesis and the absence of tools to monitor cell division and cell differentiation in situ, and/or to assess discrepancies between demand and supply of photoassimilates.
Another example for our methods driven priorities is the generally great significance attributed to air conditioning or to climate aspects in general when manipulative experiments are designed (e.g., CO2-enrichment works), although soils exert far greater influences on plant responses to what ever treatment we apply. I invite readers to check the length authors spend on describing atmospheric conditions in their experiments versus the soil conditions. The simple reason is that we can engineer atmospheric conditions, but we have no means to engineer plant–soil or plant–soil–microbe interactions, as decisive these might be. To my knowledge, the only experiment where the response of plants to a high CO2-environment were tested with plants growing in two different soil types (see Spinnler et al., 2002 (link)), revealed two different story lines, just depending on which soil was chosen. The challenge is to arrive at a broad appreciation that soil conditions (e.g., disturbed or undisturbed) are pre-determining experimental results. I join Högberg et al. (2005 ) in their viewing soil microbiota associated with roots as an integral part of plant functioning, to the extent, that they may actually be seen as part of the autotrophic system, rather than belonging to the heterotrophic world.
On a similar avenue, root research was and still is a minor fraction compared to leaf research, although there is no theoretical reason for such a posteriority. Both are equally significant, in fact roots may be more influential with respect to limiting resources. The only reason is methodology. While a leaf can be studied in isolation (e.g., some sensors mounted to it), a root does not function properly without its intact rhizosphere, apart from its poor visibility. We can “bring” the atmosphere into the lab (growth chambers), but we cannot bring a coupled rhizosphere to the lab. Any pot experiment is confounded as soon as plants respond differently to two treatments, because, inevitably, the treatment changes the root-space/plant size relationship. So the challenge here is testing hypothesis on plant responses with plants grown with unconstrained, well-developed soil biota in action. Most commonly, this can only be done in the field.
Atmosphere
Awareness
Biological Community
Carbon
Cells
Climate
Cold Temperature
Differentiations, Cell
Division, Cell
Fixation, Carbon
Heterotrophy
Hypersensitivity
isolation
Lipids
Meristem
Microbial Community
Microbial Interactions
Nutrients
Photosynthesis
Plant Leaves
Plant Roots
Plants
Pressure
Rhizosphere
Starch
Tissues
Vision
Most recents protocols related to «Rhizosphere»
Total genomic DNA was extracted from each rhizosphere soil or root samples using a Power Soil® DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA) according to the manufacturer’s instructions. To assess DNA concentration and purity, the DNA extracts were run on 1% agarose gels at 110 V for 30 min and quantified using a NanoDrop 2000 spectrophotometer (Thermo Scientific). The extracted total genomic DNA samples were stored at 20 °C until subjected to high-throughput sequencing.
Approximately 400-bp DNA fragments of the bacterial 16S rRNA gene targeting the hypervariable region V3-V4 were amplified using barcoded universal primer pair 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′) in the bacterial community analysis for the five plant species [22 (link)]. To minimize the effect of chloroplast DNA of host plant on microbiota analyses, another barcoded universal primer pair 799F (5′-AACMGGATTAGATACCCKG-3′) and 1193R (5′-ACGTCATCCCCACCTTC C-3′), spanning ~ 450 bp of the V5-V7 regions of the 16S rRNA gene, was used in the subsequent community analysis, including the analysis of tomato microbiota at different developmental stages, and of tomato amended with different nitrogen sources [35 (link), 43 (link), 72 (link)]. Amplified PCR products in each experiment were separately processed to purify, combined in equimolar ratios, and subjected to high-throughput sequencing on an Illumina Mi-Seq sequencing platform, and paired 250-nucleotide reads were produced at Sangon Biotech (Shanghai, China).
Approximately 400-bp DNA fragments of the bacterial 16S rRNA gene targeting the hypervariable region V3-V4 were amplified using barcoded universal primer pair 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′) in the bacterial community analysis for the five plant species [22 (link)]. To minimize the effect of chloroplast DNA of host plant on microbiota analyses, another barcoded universal primer pair 799F (5′-AACMGGATTAGATACCCKG-3′) and 1193R (5′-ACGTCATCCCCACCTTC C-3′), spanning ~ 450 bp of the V5-V7 regions of the 16S rRNA gene, was used in the subsequent community analysis, including the analysis of tomato microbiota at different developmental stages, and of tomato amended with different nitrogen sources [35 (link), 43 (link), 72 (link)]. Amplified PCR products in each experiment were separately processed to purify, combined in equimolar ratios, and subjected to high-throughput sequencing on an Illumina Mi-Seq sequencing platform, and paired 250-nucleotide reads were produced at Sangon Biotech (Shanghai, China).
Full text: Click here
Bacteria
Chloroplast DNA
Chloroplasts
DNA, Plant
Gels
Genes, Bacterial
Genome
isolation
Lycopersicon esculentum
Microbial Community
Nitrogen
Nucleotides
Oligonucleotide Primers
Plant Roots
Plants
Rhizosphere
Ribosomal RNA Genes
RNA, Ribosomal, 16S
Sepharose
To assess the effects of Meloidogyne spp. on root-associated microbiota, rhizosphere soil and root samples of healthy and parasitized plants were collected in Shunchang County of Fujian province, China (26° 38′–27° 121′ N, 117° 29′–118° 14′ E) in June 2016 (Additional Table S1 ). Samples of three vegetables, tomato (Solanum lycopersicum), lettuce (Lactuca sativa L. var. ramosa Hort.), and celery (Apium graveolens L.), were collected from a vegetable farm, monitored for RKN parasitism for at least 5 years before sample collection [67 ]. The field prevalence of RKN for the three crops were approximately 30–50%. Two perennial plants, Snakegourd fruit (Trichosanthes kirilowii Maxim.) and citrus (Citrus reticulata Blanco), attacked by RKN for at least 2 years (severe parasitism, with > 75% roots with galls, and swollen by > 75%), were separately collected from orchards with RKN. The collected lettuce and celery roots showed a low RKN parasitism symptom (less than one third roots with galls), and tomato root with a moderate RKN parasitism symptom (more than half of roots with galls) (Additional Table S1 ). At least three replicated healthy or nematode-parasitized plants were sampled for each plant species. The collected plants were used to separate rhizosphere soil and root samples for the 16S rRNA gene-based high-throughput sequencing and bacterial community analysis (Additional Table S1 ).
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Agricultural Crops
Apium graveolens
Apium graveolens var. dulce
Bacteria
Citrus
Citrus reticulata
Fruit
Genes
Lactuca sativa
Lycopersicon esculentum
Meloidogyne
Microbial Community
Nematoda
Plant Roots
Plants
Rhizosphere
RNA, Ribosomal, 16S
Specimen Collection
Trichosanthes kirilowii
Vegetables
During sampling, healthy and nematode-parasitized plants were pulled carefully from soil and shaken to remove large soil particles, leaving soil that was strongly attached to the roots. Rhizosphere soil and roots were separately collected from the sampled materials as described by Edwards et al. and Kwak et al. [70 (link), 71 (link)]. To obtain the bacterial community profiles specifically associated with RKN parasitism, galls induced by Meloidogyne spp. were separated from the surface-sterilized parasitized roots with a sterile scalpel [22 (link)]. Briefly, surface-sterilized parasitized roots were separated into two fractions: one contained the galls, whereas the other contained the non-swollen part of the parasitized root system. Together, for each sampled healthy plant, the rhizosphere soil and root fractions were isolated. For each nematode-parasitized plant, its rhizosphere soil and the two root fractions were obtained. In total, 75 samples (30 from rhizosphere soil, 30 from roots, and 15 from gall samples) were collected from healthy and parasitized plants, in the five different plant species (Additional Table S1 ). Similarly, 135 samples (57 rhizosphere soil, 57 root, and 21 gall samples) were collected from 9 or 10 growth stages, respectively, from the healthy and nematode-parasitized tomato plants (Additional Table S3 ).
Full text: Click here
Bacteria
Lycopersicon esculentum
Meloidogyne
Nematoda
Plant Roots
Plants
Rhizosphere
Sterility, Reproductive
To minimize the confounding effects of plant species and their differential susceptibilities to RKN attacks on bacterial community analyses, we systematically investigated bacterial community composition around roots at the different growth and disease developmental stages using tomato as a model. Seeds of tomato cultivar Xinzhongshu No. 4, susceptible to RKN (Meloidogyne incognita) were surface-sterilized in 0.5% sodium hypochlorite solution for 15 min and 70% ethanol for 1 min. The sterilized seeds were rinsed extensively in sterile water five times, and then germinated in sterile plates under dark condition at 28 °C for 3 days [22 (link), 68 (link)]. Germinated seeds were separately planted into two adjacent experimental fields at Qishan campus of Fujian Normal University in Fuzhou, Fujian province, China (26° 01′ N, 119° 12′ E) from June to August 2017. One field had no record of extensive RKN parasitism and was used to grow healthy plants. Another field was a nursery for tomato plants known to contain M. incognita for at least 3 years prior to planting, at nematode parasitism rates above 90%.
To investigate the effects of plant growth and RKN parasitism on tomato root-associated microbiota, we collected the first tomato samples at the second true leaf stage (about ten days after planting). After that, samplings were conducted every 7 days. A total of nine (for nematode-parasitized plants) or ten (for healthy tomato plants) stages were sampled. For each stage, three replicated plants were sampled for both the healthy and nematode parasitism treatments (Additional Table S3 ). The healthy and parasitized conditions of tomato plants were confirmed by examining the presence of RKN in small fragments of sampled roots, stained with acid fuchsin, following an established protocol [69 ]. The collected plant samples were used to further separate rhizosphere soil and root samples for the following 16S rRNA gene sequencing and bacterial community analysis (Additional Table S3 ).
To investigate the effects of plant growth and RKN parasitism on tomato root-associated microbiota, we collected the first tomato samples at the second true leaf stage (about ten days after planting). After that, samplings were conducted every 7 days. A total of nine (for nematode-parasitized plants) or ten (for healthy tomato plants) stages were sampled. For each stage, three replicated plants were sampled for both the healthy and nematode parasitism treatments (Additional Table S
Full text: Click here
Bacteria
Ethanol
Genes
Lycopersicon esculentum
Meloidogyne
Microbial Community
Nematoda
Plant Development
Plant Diseases
Plant Embryos
Plant Leaves
Plant Roots
Plants
Rhizosphere
RNA, Ribosomal, 16S
Sodium Hypochlorite
Specimen Collection
Sterility, Reproductive
Susceptibility, Disease
The non-targeted metabolomics method was used to analyze the metabolites in the rhizosphere soil of tea tree. This was done by taking 50 mg of soil samples and homogenizing them with 500 μL of ice-cold methanol/water (70%, v/v), respectively. The mixtures were homogenized at 30 Hz for 2 min. After homogenization, the mixture was shaken for 5 min and incubated on ice for 15 min, and centrifuged at 12,000 rpm and 4 °C for 10 min and suck 400 μL of supernatant into another centrifuge tube. 500 mL of ethyl acetate/methanol (V, 1:3) was added to the original centrifuge tube, oscillated for 5 min and incubated on ice for 15 min, centrifuged at 12,000 rpm and 4 °C for 10 min, and then took 400 mL of supernatant. The two supernatants were combined and concentrated. Then 100 μL of 70% methanol water was added into the dried product for 3min of ultrasonication. Finally, the product was centrifuged at 12,000 rpm and 4 °C for 3 min, and 60 μL of supernatant was aspirated for LC-MS/MS analysis.
Full text: Click here
Cold Temperature
ethyl acetate
Methanol
Rhizosphere
Tandem Mass Spectrometry
Trees
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More about "Rhizosphere"
The rhizosphere, the narrow zone of soil directly influenced by root secretions and associated microorganisms, is a critical interface where complex plant-soil-microbe interactions occur.
These rhizosphere processes are essential for sustainable agriculture, environmental remediation, and understanding belowground dynamics.
Researchers can leverage AI-driven platforms like PubCompare.ai to optimize their rhizosphere studies by locating the best protocols from literature, preprints, and patents, enhancing reproducibility and accuracy through AI-assisted protocol selection.
The rhizosphere is a dynamic and multifaceted environment, encompassing the soil, plant roots, and the diverse microbial communities that thrive in this unique niche.
This zone is characterized by a high concentration of root exudates, nutrients, and microbial activity, making it a hub of critical ecosystem functions.
Leveraging advanced molecular techniques, such as the FastDNA SPIN Kit for Soil, MiSeq platform, PowerSoil DNA Isolation Kit, DNeasy PowerSoil Kit, and E.Z.N.A.® soil DNA Kit, researchers can delve into the complex microbial communities and genomic profiles within the rhizosphere.
Tools like the NanoDrop 2000 spectrophotometer, Qubit 2.0 Fluorometer, and NanoDrop 1000 spectrophotometer provide precise quantification and quality assessment of nucleic acids extracted from rhizosphere samples.
By harnessing the power of AI-driven platforms like PubCompare.ai, researchers can streamline their rhizosphere investigations, optimizing their experimental designs, enhancing reproducibility, and accelerating their discoveries.
This AI-assisted approach enables researchers to navigate the vast landscape of rhizosphere-related literature, preprints, and patents, ultimately unlocking new insights and advancing our understanding of this critical interface between plants, soil, and microbes.
These rhizosphere processes are essential for sustainable agriculture, environmental remediation, and understanding belowground dynamics.
Researchers can leverage AI-driven platforms like PubCompare.ai to optimize their rhizosphere studies by locating the best protocols from literature, preprints, and patents, enhancing reproducibility and accuracy through AI-assisted protocol selection.
The rhizosphere is a dynamic and multifaceted environment, encompassing the soil, plant roots, and the diverse microbial communities that thrive in this unique niche.
This zone is characterized by a high concentration of root exudates, nutrients, and microbial activity, making it a hub of critical ecosystem functions.
Leveraging advanced molecular techniques, such as the FastDNA SPIN Kit for Soil, MiSeq platform, PowerSoil DNA Isolation Kit, DNeasy PowerSoil Kit, and E.Z.N.A.® soil DNA Kit, researchers can delve into the complex microbial communities and genomic profiles within the rhizosphere.
Tools like the NanoDrop 2000 spectrophotometer, Qubit 2.0 Fluorometer, and NanoDrop 1000 spectrophotometer provide precise quantification and quality assessment of nucleic acids extracted from rhizosphere samples.
By harnessing the power of AI-driven platforms like PubCompare.ai, researchers can streamline their rhizosphere investigations, optimizing their experimental designs, enhancing reproducibility, and accelerating their discoveries.
This AI-assisted approach enables researchers to navigate the vast landscape of rhizosphere-related literature, preprints, and patents, ultimately unlocking new insights and advancing our understanding of this critical interface between plants, soil, and microbes.