Name: Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes (Video) | JoVE Journal
Uploaded: 2022-01-10T14:00:00.000+00:00
Duration: 7 min 42 s
Description: Discover bioinformatics methods to assess glyphosate effects on plant, animal, and soil microbiomes. This workflow enables robust hypothesis testing for ecological and agricultural research applications.

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5 minutes

This work was funded by the Academy of Finland (grant no. 311077 to Marjo Helander).

Conflicts of interest: none.

This protocol provides general guidance on how to quantify the effect of GBP on microbiomes based on the analysis of the EPSPS protein. The protocol has three major critical steps: (i) Quantification of the EPSPS protein from microbiome data. This step is critical because EPSPS is the direct target enzyme of the herbicide. Thus, species that have a copy of the EPSPS gene may be impacted by the use of GBP. Nevertheless, even species that lack a copy of the EPSPS gene may be impacted by the herbicide through alternative non-target mechanisms43,44. (ii) If the analysis of the EPSPS gene&#160;is not included in the design of the study, it is possible to get a good estimate by analyzing the 16S rRNA (bacteria) or ITS (fungi). In this case, it is essential to rely on a comprehensive reference table (e.g., the ATGC database provides sequences of the EPSPS protein from several closely related species). (iii) The EPSPS protein is divided into potentially sensitive or resistant to glyphosate depending on certain amino acid residues of the active site of the EPSPS. However, mutations affecting a single amino acid may alter this classification45 and transitions among classes may occur in a relatively short period of time14.
The potential sensitivity of organisms to glyphosate can be determined by reference genomes, amino acid markers and sequence alignments. (i) Reference genomes: The EPSPS enzyme can be classified as potentially sensitive (class I [alpha or beta]46,47) or resistant (classes II48,49, III50 and IV51) to glyphosate based on the presence of amino acid markers and motifs (in the case of class III). These amino acid markers and motifs are based on the location of amino acid residues in the EPSPS protein of Vibrio cholerae (vcEPSPS, class I), Coxiella burnetii (cbEPSPS, class II), Brevundimonas vesicularis (bvEPSPS, class III), and Streptomyces davawensis (sdEPSPS, class IV). (ii) Amino acid markers: Glyphosate interacts with the EPSPS enzyme and competes with phosphoenolpyruvate (PEP, the second substrate of the EPSPS enzyme)52,53. In certain species, small amino acid changes in the EPSPS sequence provide a higher affinity for the PEP and a resistance to glyphosate12, 14, 52, 54, 55. In other sequences, glyphosate binds the EPSPS sequence in a non-inhibitory conformation 45. Although many resistant 12,14,48,49,52,54,55 and tolerant56,57 EPSP sequences to glyphosate have been described, the current classification system for the EPSPS is divided into four major classes (I-IV )12 (Table 5). (iii) Sequence alignments: In order to classify an EPSPS enzyme, we performed pairwise alignments, with a multiple sequence alignment program-default parameters35-, of the query sequence against each one of the reference sequences (vcEPSPS, cbEPSPS, bvEPSPS and sdEPSPS). These alignments are necessary to identify the positions of the amino acid markers in the query sequence. As a result, an enzyme is classified as described12-class I, II and/or IV based on the presence of amino acid markers and class III based motif markers.
The protocol is based on four known types of EPSPS: one type is sensitive, the other three are resistant). However, approximately 10% of EPSPS sequences in prokaryotes are yet unclassified (16% in archaea and 8% in bacteria)12. Thus, further research should analyze those sequences to determine glyphosate sensitivity. The EPSPSClass server provides an option to test new genetic markers. The identification of known classes of the EPSPS is straightforward, as shown in section 4.4. and Figure 5. Furthermore, in those cases where users want to compare their own query and reference proteins, the server provides an option to manually include a reference sequence and a set of amino acid markers (Figure 11). This option can be utilized to identify novel classes of the EPSPS, as well as to test other herbicides and target sequences.
The analysis of the EPSPS class is determined by sequence analysis and the presence/absence of amino acid markers. This is a preliminary estimate that can be used for hypothesis testing in the field. Amino acid markers have been determined in the literature based on empirical and observational studies46,47,48,49,50,51. However, reference protein sequences to determine EPSPS class have been tested only in a limited number of species and may occasionally fail to explain resistance to glyphosate. The effect of compensatory mutations, and EPSPS-associated domains (mostly in fungi) may also affect the sensitivity to glyphosate58. This paper&#39;s analysis is based on four EPSPS classes. A survey of bacteria in the human gut microbiome showed that around 30% of them were unclassified (i.e., EPSPS proteins from these species do not belong to any of the known classes), and additional studies are needed to identify other EPSPS classes. Also, it should be noticed that the EPSPS protein sequence in bacteria and plants is unidomain, whereas fungal EPSPS proteins contain several domains59. Thus, a protein folding in fungi may lead to a different response of the EPSPS enzyme to glyphosate. Moreover, additional non-target mechanisms of resistance (e.g., efflux pumps and overexpression of the EPSPS gene13) or sensitivity to glyphosate (e.g., the effect of glyphosate on the mitochondrial transport chain12) are not considered.
Although GBPs have been around as a herbicide since 1974 and have been widely utilized since 1991, this is the first bioinformatics method to determine the potential sensitivity of organisms to glyphosate. The method is based on the identification of known amino acid residues in the target sequence. Thus, our method provides a baseline estimate of the potential effect of glyphosate on the species. In the near future, novel bioinformatics methods should include additional classes of the EPSPS protein to determine the potential sensitivity to glyphosate of unclassified sequences12,54,55. In addition, given that the exact behavior of the EPSPS enzyme may vary by single amino acid changes12,14,52,54,55, further in silico experiments should take into account small variations in the folding of the EPSPS protein, as well as the effect of the EPSPS-associated domains on the protein structure in fungi58. Moreover, it has been shown that tolerance to glyphosate may be produced by overexpression of the EPSPS protein56,57; thus bioinformatics analyses based on the amelioration of the codon usage60 may be utilized to identify novel EPSPS sequences that maximize or minimize gene expression.
Farmers, politicians, and decision-makers urgently need a thorough understanding of the risks associated with the heavy use of pesticides. Thus, both bioinformatic tools revealing the potential sensitivity of organisms to pesticides and well-replicated, randomized, and field-realistic experimental studies conducted in different environments are necessary. The presented bioinformatic method designed to examine organisms&#39; sensitivity to glyphosate can be modulated for other pesticides. Similarly, the methods of experimental ecology can be applied to study any related ecological questions. Together, the methods can be used to demonstrate casualties between field observations, genomic data, and pesticide use. All presented methods are invaluable in risk assessment. Bioinformatic methods can be used, for example, in monitoring microbial adaptations to agrochemicals and to provide a quantitative method to test the potential other associated risks, such as an increase in resistance of pathogens to agrochemicals, negative effects on microbes used as biological control agents in integrated pest management (IPM), and antibiotic resistance in bacteria.

The heavy use of pesticides in modern agriculture is clearly a major contributor to the decline of biodiversity1. This paper focuses on glyphosate because glyphosate-based products (GBPs) have become the most widely used pesticides globally due to their efficiency and affordable price2,3. In addition to killing weeds in agricultural fields, GBPs are commonly used in silviculture, urban environments, and home gardens; additionally, they have been proclaimed as nontoxic to non-target organisms if used in accordance with the manufacturer&#39;s instructions. However, an increasing number of recent studies have revealed that residues of glyphosate and its degradation products may be retained and transported in soils, thereby having cascading effects on non-target organisms4,5,6,7,8 . The effects of glyphosate are not limited only to plants-the shikimate pathway is present in many fungi and prokaryotes as well. Glyphosate targets the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in the shikimate pathway, also known as aroA9. This enzyme is at the center of the shikimate pathway in the synthesis of the three essential aromatic amino acids (phenylalanine, tyrosine, and tryptophan), and it is present in most prokaryotes, plants, and fungi10,11. Some microbial species have developed partial or absolute resistance to glyphosate by means of several mechanisms, including mutations in the EPSPS sequences. Thus, it has been suggested that the use of GBPs may have a direct effect on plant and animal microbiomes, including the human gut microbiome12,13,14. Nevertheless, the use of GBP may have an adverse impact on virtually any ecosystem function and service relying on microbes and microbe-facilitated processes. The consequent threats may concern biochemical soil processes, pollination biology, and animal and human wellbeing. This calls for a more comprehensive understanding of how glyphosate affects shikimate pathways and methods to assess the sensitivity of microbes to glyphosate.
In this protocol, we present a pipeline to test the effect of glyphosate and GBP on the microbiome, from field experiments to bioinformatics analyses. We describe in detail a recently published bioinformatics method that can be used to determine the potential sensitivity of organisms to glyphosate12. To the researchers&#39; knowledge, this is the first and so far, the only bioinformatics tool to assess the intrinsic sensitivity of the enzyme EPSPS to the active component of GBPs. This bioinformatics method is based on the detection of known amino acid markers in the glyphosate target enzyme (EPSPS)12. The pipeline is divided into five main working phases (Figure 1): 1) a brief introduction to two field experiments to test the effect of GBPs, 2) a brief summary of microbiome analyses (16S rRNA, ITS, and EPSPS gene), 3) gathering EPSPS sequences from public repositories, 4) determining the potential sensitivity of organisms to glyphosate, and 5) assessing the EPSPS class from universal microbial markers (16S rRNA and ITS).

<table><tbody><tr><td>2100 Bioanalyzer Instrument</td><td>Agilent</td><td>G2939B</td><td>To check the concentration and quality of PCR products</td></tr><tr><td>dNTP mix (10 mM each)</td><td>ThermoFisher Scientific</td><td>R0192</td><td>For PCR reactions</td></tr><tr><td>GoTaq G2 DNA Polymerase kit</td><td>Promega</td><td>M7848</td><td>PCR buffer and DNA Polymerase for PCR amplification</td></tr><tr><td>Invisorb Spin Plant Mini Kit</td><td>INVITEK Molecular</td><td>1037100300</td><td>Genomic DNA extraction from plant tissues</td></tr><tr><td>Ion Chip Minifuge</td><td>ThermoFisher Scientific</td><td>4479672</td><td>For targeted sequencing of microbial PCR products</td></tr><tr><td>Ion PGM System</td><td>ThermoFisher Scientific</td><td>4462921</td><td>For targeted sequencing of microbial PCR products</td></tr><tr><td>Ion PGM Torrent Server</td><td>ThermoFisher Scientific</td><td>4483643</td><td>For targeted sequencing of microbial PCR products</td></tr><tr><td>Pippinprep</td><td>SageScience</td><td>PIP0001</td><td>For size fractionation of PCR amplicons</td></tr><tr><td>Pressure tank</td><td>Berthoud</td><td>102140</td><td>For sprayin glyphosate based products in field</td></tr><tr><td>Primers</td><td>Sigma Aldrich</td><td>Custom-made</td><td>For PCR amplification</td></tr><tr><td>Rotary tiller</td><td>Grillo</td><td>984511</td><td>For tilling the soil in experimental plots</td></tr><tr><td>S1000 ThermalCycler</td><td>BIO-RAD</td><td>1852196</td><td>For PCR amplification</td></tr></tbody></table>

quantification of the potential impact of glyphosate-based products on microbiomes

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

Glyphosate-based products (GBP) are the most common broad-spectrum herbicides worldwide. The target of glyphosate is the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in the shikimate pathway, which is virtually universal in plants. The inhibition of the enzyme stops the production of three essential amino acids: phenylalanine, tyrosine, and tryptophan. EPSPS is also present in fungi and prokaryotes, such as archaea and bacteria; thus, the use of GBP may have an impact on the microbiome composition of soils, plants, herbivores, and secondary consumers. This article aims to present general guidelines to assess the effect of GBP on microbiomes from field experiments to bioinformatics analyses and provide a few testable hypotheses. Two field experiments are presented to test the GBP on non-target organisms. First, plant-associated microbes from 10 replicated control and GBP treatment plots simulating no-till cropping are sampled and analyzed. In the second experiment, samples from experimental plots fertilized by either poultry manure containing glyphosate residues or non-treated control manure were obtained. Bioinformatics analysis of EPSPS protein sequences is utilized to determine the potential sensitivity of microbes to glyphosate. The first step in estimating the effect of GBP on microbiomes is to determine their potential sensitivity to the target enzyme (EPSPS). Microbial sequences can be obtained either from public repositories or by means of PCR amplification. However, in the majority of field studies, microbiome composition has been determined based on universal DNA markers such as the 16S rRNA and the internal transcribed spacer (ITS). In these cases, sensitivity to glyphosate can only be estimated through a probabilistic analysis of EPSPS sequences using closely related species. The quantification of the potential sensitivity of organisms to glyphosate, based on the EPSPS enzyme, provides a robust approach for further experiments to study target and non-target resistant mechanisms.

The aim of this protocol is to provide a general pipeline, from field experiments to bioinformatics analyses, that quantifies the potential sensitivity of organisms to the herbicide glyphosate. In Experiment 2 the average glyphosate concentration in the quail feed was 164 mg/kg and the average glyphosate concentration of the excreta samples (urine and fecal matter combined) was 199 mg/kg. Beddings collected from quails fed with GBP-contaminated feed had, on average, 158 mg/kg and control beddings measuring 0.17 mg/kg of glyphosate (Table 3). In the field experiments, plant species responded differently to glyphosate residues in the soils (section 1). Biomass of oat and turnip rape was greater in control soils compared to GBP-treated soils. However, faba beans and potato appeared to benefit from GBP treatment at the end of the growing season15. Glyphosate in poultry manure decreased plant growth in grass (Festuca pratensis) and strawberry (Fragaria x vescana) (section 1). The microbiota analyses from the field experiments have not yet been fully analyzed and are not presented here (section 2). The results of this protocol, when read either directly (as shown in sections 3 and 4) or indirectly (section 5), provide a measure of the proportion of potentially sensitive and resistant organisms to glyphosate in a dataset (Figure 9). The use of this method was tested with a collection of EPSPS protein sequences from microbial species of the core human gut microbiome that were obtained from public repositories12. In the study, 890 strains from the 101 most abundant bacterial species were analyzed with the EPSPSClass method to quantify the proportion of sensitive and resistant bacteria. The results showed that 54% of the species in the core human gut microbiome are potentially sensitive to glyphosate12. This trend is also observed in most of the prokaryotic world; additionally, in eukaryotes (mainly plants and fungi), the proportion of potentially sensitive species is even higher12. Moreover, we have utilized this method to quantify changes in sensitivity in the EPSPS protein at a microevolutionary level (Figure 10)14. We identified changes in sensitivity status in 12 out of 32 closely related groups of prokaryotes analyzed (Table 4)14. Thus, the continuous use of the GBPs may produce microbial dysbiosis (i.e., an imbalance of sensitive and resistant bacterial species) in plant, animal, and soil microbiomes. Moreover, it has been hypothesized that an increase in glyphosate-resistant bacteria may promote multidrug-resistant microbiomes14,41,42. Thus, this protocol sheds light on the interpretation of all these scenarios, as the EPSPS classification method provides a direct estimate of the intrinsic sensitivity of microbiomes to glyphosate. Due to the intrinsic sensitivity of the EPSPS protein to glyphosate is phylogenetically conserved14, it is possible to extrapolate the results from existing datasets into unknown microbiomes (Figure 8).
<img alt="Glyphosate exposure sensitivity diagram; field experiments, EPSPS data flow, sensitivity analysis." class="xfigimg" src="/files/ftp_upload/63109/63109fig01.jpg" data-altupdatedat="1788282455000" data-alt="Figure 1"> 
Figure 1: General pipeline This is a general pipeline to analyze sensitivity to GBP from field experiments to bioinformatics analysis. <a href="https://www.jove.com/files/ftp_upload/63109/63109fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Crop rotation plan diagram, including control (C1-C10) and GBP (G1-G10), with turnip, oat, faba bean, potato." class="xfigimg" src="/files/ftp_upload/63109/63109fig02.jpg" data-altupdatedat="1788282455000" data-alt="Figure 2"> 
Figure 2: Field experiment 1 to test the effects of GBP residues on crop plant-associated microbes. The experimental field consists of alternating 10 control plots and 10 GBP treatment plots (23 m x 1.5 m) with 1.5 m buffer strips between plots. Two times a year since 2014, the GBP plots were treated with commercial GBP (glyphosate concentration 450 g L-1, application rate 6.4 L ha-1 in 5 L of tap water per plot) and the control plots with the same amount of tap water without glyphosate. The treatments were applied with a hand-operated pressure tank using a plastic hood in the sprinkler tip to protect GBPs from spreading outside the treatment plots. After a two-week safety period following the GBP application, oats (Avena sativa), faba beans (Vicia faba), and turnip rapes (Brassica rapa subsp. oleifera) were sown, and potatoes (Solanum tuberosum) were planted in the plots. Microbiota samples from the studied crop plants, leaves and roots, were collected several times since the start of the experiment in 2014. <a href="https://www.jove.com/files/ftp_upload/63109/63109fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Aviary experiment chart: quail manure impact on plant plots; data sampling for microbiota analysis." class="xfigimg" src="/files/ftp_upload/63109/63109fig03.jpg" data-altupdatedat="1788282455000" data-alt="Figure 3"> 
Figure 3: Field experiment 2 tested the consequences of GBP residues in manure fertilizer for two perennial crops and their associated microbiota. Beddings collected from a 12-month aviary experiment with Japanese quails fed with control or GBP-contaminated feed were used as manure fertilizer in a field experiment. The experimental field consisted of 18 control and 18 GBP plots (1 m x 1 m) arranged in a 6 x 6 chessboard grid. The beddings were spread on the experimental field twice, in August 2018 and May 2019 (25 L / plot). Control plots were fertilized with beddings collected from quails fed with control feed and GBP plots with beddings from quails fed with GBP-contaminated feed. Glyphosate residues in control beddings were 0.17 mg/kg of glyphosate and in GBP-bedding, the amount was 158 mg/kg of glyphosate. Two endophyte-symbiotic (E+), two endophyte-free (E-) Festuca pratensis, and two Fragaria x vescana were planted per plot in September 2018, approximately one month after the spread of the first beddings. Measurements of plant performance and fitness as well as sampling for root-and leaf-associated microbiota were conducted during two consecutive growing seasons (2019 &amp; 2020). <a href="https://www.jove.com/files/ftp_upload/63109/63109fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="PCR process diagram for gene sequencing; primers on microbes; sequencing library creation." class="xfigimg" src="/files/ftp_upload/63109/63109fig04.jpg" data-altupdatedat="1788282455000" data-alt="Figure 4"> 
Figure 4: Analysis of the microbial taxa using 16S rRNA gene/ITS region and sensitivity of microbiomes to glyphosate using the EPSPS gene. (A) Analysis of 16S rRNA or ITS sequences to identify microbial taxa. (B) Analysis of EPSPS sequences to identify sensitivity of microbes to glyphosate (GS-glyphosate sensitive/GR-glyphosate resistant) <a href="https://www.jove.com/files/ftp_upload/63109/63109fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="DNA sequence alignment diagram showing EPSPS classes and identity markers percentages." class="xfigimg" src="/files/ftp_upload/63109/63109fig05.jpg" data-altupdatedat="1788282455000" data-alt="Figure 5"> 
Figure 5: Algorithm to identify the class of EPSPS protein sequences. The input is an EPSPS protein sequence in FASTA format. The algorithm performs comparisons with known amino acid markers in reference protein sequences that determine the potential sensitivity to glyphosate. The algorithm was implemented at the freely accessible web server EPSPSClass29. <a href="https://www.jove.com/files/ftp_upload/63109/63109fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="EPSP synthase sequence analysis setup, sequence input, alignment results, class identity chart, BLAST run." class="xfigimg" src="/files/ftp_upload/63109/63109fig06.jpg" data-altupdatedat="1788282455000" data-alt="Figure 6"> 
Figure 6: Basic inputs and outputs of the EPSPSClass web server. (A) Input: an EPSPS protein sequence in FASTA format. (B) Output 1 - identity: fraction of amino acid markers present in the query sequences (Classes I-IV)and motifs (Class III). (C) Output 2 - identity: alignments of the query and reference sequences. (D) Output 3 - pairwise alignments of the query and reference sequences. (E) Reference EPSPS sequences: Vibrio cholerae (vcEPSPS, class I), Coxiella burnetii (cbEPSPS, class II), Brevundimonas vesicularis (bvEPSPS, class III), Streptomyces davawensis (sdEPSPS, class IV). (F) Links to perform addition blastp searches and identification of conserved domains <a href="https://www.jove.com/files/ftp_upload/63109/63109fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="EPSPS synthase classification, diagram; precomputed datasets, taxonomic data, species prediction." class="xfigimg" src="/files/ftp_upload/63109/63109fig07.jpg" data-altupdatedat="1788282455000" data-alt="Figure 7"> 
Figure 7: Access to pre-computed datasets of EPSPS sequences. Follow the indications in the figure to access the pre-computed dataset of EPSPS sequences. <a href="https://www.jove.com/files/ftp_upload/63109/63109fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Microbiome project chart showing microbial sensitivity classification; datasets; S. aureus to S. islandicus." class="xfigimg" src="/files/ftp_upload/63109/63109fig08.jpg" data-altupdatedat="1788282455000" data-alt="Figure 8"> 
Figure 8: Example of how to estimate the potential sensitivity in microbiome projects without EPSPS sequences. The example uses values from the database of Alignable Tight Genomic Clusters30, which contains sequences from prokaryotic species. Hypothetical species from a microbiome project are Staphylococcus aureus, Corynebacterium diphtheriae, Campylobacter jejuni, Chlamydia psittaci and Sulfolobus islandicus. The sensitivity score to glyphosate is calculated as Number_Sensitive_Sequences/Total_Number_Of_Sequences. <a href="https://www.jove.com/files/ftp_upload/63109/63109fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Microbiome response to glyphosate; Sensitive, resistant, unclassified; Diagram, time progression." class="xfigimg" src="/files/ftp_upload/63109/63109fig09.jpg" data-altupdatedat="1788282455000" data-alt="Figure 9"> 
Figure 9: Scheme of the interpretation of the results from this protocol and hypothetical evolutionary scenarios. (A) In a microbiome, the proportion of potential sensitivity (in green) and resistance (in red) bacteria is approximately 50:50. Black dots denote microbial species unclassified; thus, their sensitivity to glyphosate is unknown. In some microbiomes, the proportion of sensitive bacteria is slightly higher, as in the human gut microbiome12. (B) Over time, the use of glyphosate may lead to microbial dysbiosis (i.e., an imbalance in the proportion of sensitive and resistant bacteria) leading to different hypothetical scenarios. (C) Hypothetical case 1 (no selection): The use of glyphosate does not influence the microbiome; thus, the proportion of sensitive and resistant bacteria remains constant. (D) Hypothetical case 2: The use of glyphosate removes bacteria sensitive to glyphosate from the population. We speculate that this scenario may be dose-dependent. (E) Hypothetical case 3: Selection pressure from the use of glyphosate enhances mutations in the EPSPS gene that change the sensitivity status of bacteria. Thus, the entire microbial population becomes resistant to glyphosate. Moreover, in this scenario, there might be an increase in multidrug-resistant bacteria. (F) Hypothetical case 4: the use of glyphosate alters the composition of certain bacterial species, producing an imbalance towards resistant bacteria, whereas some bacterial species remain unaltered, possibly due to additional resistant mechanisms such as efflux pumps or by overexpression of the EPSPS gene13. This scenario may also lead to an increase in glyphosate-resistant bacteria, as well as an increase in bacterial resistance to additional antibiotics. <a href="https://www.jove.com/files/ftp_upload/63109/63109fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Glyphosate resistance phylogenetic tree diagram; taxonomy, lifestyle, GC content analysis." class="xfigimg" src="/files/ftp_upload/63109/63109fig10v2.jpg" data-altupdatedat="1788282455000" data-alt="Figure 10"> 
Figure 10: Distribution of the predicted sensitivity to glyphosate across the species tree. Pie charts indicate the proportion of species that are putatively sensitive (green) or resistant (red) to glyphosate, and unclassified (black). This figure has been adapted with permission from Rainio et al.14. <a href="https://www.jove.com/files/ftp_upload/63109/63109fig10largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Sequence insertion interface for protein analysis; includes FASTA and amino acid marker input fields." class="xfigimg" src="/files/ftp_upload/63109/63109fig10.jpg" data-altupdatedat="1788282455000" data-alt="Figure 11"> 
Figure 11: Inputs and outputs of the EPSPSClass webserver to test user's own reference sequence. (A) Input 1: query sequence. (B) Input 2: reference sequence. (C) Input 3: amino acid markers in the reference sequences. (D) Output: identity: fraction of amino acid markers in the query sequences (class I-IV and user's own reference sequences). <a href="https://www.jove.com/files/ftp_upload/63109/63109fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: List of primers for PCR amplification of 16S rRNA gene and ITS region in microbiome analysis <a href="https://www.jove.com/files/ftp_upload/63109/Table%201-63109R2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: Codes of the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in different databases <a href="https://www.jove.com/files/ftp_upload/63109/Table%202_63109R2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 3: Average glyphosate concentration <a href="https://www.jove.com/files/ftp_upload/63109/Table%203-63109R2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 4: Summary table of the percentage of species sensitive/resistant to glyphosate. This table has been adapted with permission from Rainio et al.14. <a href="https://www.jove.com/files/ftp_upload/63109/Table%204-63109R2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 5: Positions of the amino acid markers in the reference sequences <a href="https://www.jove.com/files/ftp_upload/63109/Table%205-63109R2.xlsx" target="_blank">Please click here to download this Table.</a>

Glyphosate-based products (GBP) are the most common broad-spectrum herbicides worldwide. In this article, we introduce general guidelines to quantify the effect of GBP on microbiomes, from field experiments to bioinformatics analyses.

Glyphosate-based products (GBP) are the most common broad-spectrum herbicides worldwide. The target of glyphosate is the enzyme ...

quantification-potential-impact-glyphosate-based-products-on

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High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

Discover bioinformatics methods to assess glyphosate effects on plant, animal, and soil microbiomes. This workflow enables robust hypothesis testing for ecological and agricultural research applications.

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

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