Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes

Here, we provide a workflow that includes the best bioinformatics method to conduct the experiments to assess the effect of glyphosate on microbiomes. The main advantage is that it provides a robust testable hypothesis for future field and lab experiments. The methodology described can be applied to a variety of systems, including plants, animals, and microbes.

Each part of the methodology is quite straightforward. Perhaps the most challenging part is to integrate a multidisciplinary team capable of performing different parts of the work. Demonstrating the procedure will be Suni Matthew, a researcher from my laboratory.

To begin, start experiment one by dividing the experimental field into 10 replicates of control and GBP treatment with buffer strips of vegetation between the plots. Treat the control plots with tap water and the GBP plots with commercial GBP to mimic the maximal permitted glyphosate dosage in agricultural practices. Sample the microbiota from experimental plants.

Collect 10 replicates of the plant samples from the field, immediately place them on ice and bring them to the laboratory for further processing. For experiment two, collect beddings, including wood shavings, feces, and some spilled feed from quails fed on GBP contaminated or control feeds in a 12-month aviary experiment. Send the bedding samples to an accredited laboratory for the glyphosate concentration measurements directly after it is spread.

Plant perennial grass and strawberry plants in each plot to study their root and leaf microbiota. Identify and remove the endophytic microbes by washing and sterilizing the plant samples. Once the samples are sterilized, extract genomic DNA using a commercially available plant DNA extraction kit following the manufacturer's protocol.

Using a nested approach, amplify the variable regions V6 to V8 of the 16S rRNA gene and make PCR master mix for the required number of samples as described in the text manuscript. For the first round of PCR, use primers 799 forward and 1492 reverse. Set up the amplification profile in the thermocycler for 35 cycles.

To verify amplification, carry out electrophoresis using five microliters of amplified product and then dilute the rest 25 microliters of the PCR product in autoclaved ultrapure water at the ratio of one to 10. Using the universal primers 1062 forward and 1390 reverse, conduct the second round of amplification with 25 cycles. Dilute the resulting PCR products in autoclaved ultrapure water at an equal ratio.

Carry out the third round of amplification with eight cycles to tag the products with the barcodes and P1 adapter sequence. Verify the concentration in quality of PCR products on a bioanalyzer and pool the volumes constituting 30 nanograms of DNA of each sample in a 1.5 milliliter tube to prepare an equimolar library. Select the amplicons of size 350 to 550 base pairs by size fractionation using an automated DNA size selection system on an agarose gel cassette.

Collect the elute containing the amplicons of the specified size into a vial in the cassette resulting in a purified 16S rRNA gene library. Transfer the collected eluent into a 1.5 milliliter tube and verify the purity and concentration on the bioanalyzer. Dilute the DNA library using autoclaved ultrapure water to a final concentration of 26 picomolar.

To amplify the ITS region, after setting up the reaction with ITS primers, set the amplification profile on the thermocycler. Then analyze five microliters of the amplified product on 1.5%agarose gel and dilute the remaining 25 microliters of product at the ratio of one to 10 using autoclaved ultrapure water. Make PCR master mix for the required number of samples with barcode-tagged forward primers and P1 adapter-tagged reverse primers.

Using the diluted PCR product as the template, conduct the second round of amplification. Similar to the library preparation for the 16S rRNA gene, prepare the library for the purified ITS region. Initiate the bioinformatics analyses of the EPSPS protein sequence.

Assess the potential sensitivity of the query sequence to glyphosate from the server provided outputs where output one shows the fraction of amino acid markers present in the query sequences and the number of motifs. Output two shows alignments of the query and reference sequences based on marker residues. Output three shows full pair-wise alignments of the query and reference sequences.

Output four shows EPSPS reference sequences of Vibrio cholerae, Coxiella burnetii, Brevundimonas vesicularis, and Streptomyces davawensis. At the end of the output page, find links to external tools such as BLASTp and conserved domains to further analyze the query EPSPS sequence. After identifying the EPSPS sequences from the public repositories, access these datasets from the EPSPS class server's main page containing taxonomical information and the EPSPS class of over 50, 000 sequences.

Weigh the above-ground biomass of the plants at the end of the field season to compare the growth of the plants in GBP and control plots. In a spreadsheet, map the bacterial OTUs for microbiome experiments into pre-computed datasets. And based on a probabilistic score, calculate the intrinsic sensitivity to glyphosate.

This method was used to quantify changes in sensitivity in the EPSPS protein at a micro evolutionary level. We identified changes in sensitivity status in 12 out of 32 closely related groups of prokaryotes analyzed. Thus, the glyphosate-based products'continuous use may produce microbial dysbiosis in the plant, animal, and soil microbiomes.

Ensure the quality of pre and post-PCR procedures to obtain optimum amplicon libraries. Also ensure the EPSPS protein sequence is not partial before using the software. At field experiments, make sure to prevent contamination of controls with glyphosate.