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

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="Figure 1" class="xfigimg" src="/files/ftp_upload/63109/63109fig01.jpg" /> 
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.remotexs.ntu.edu.sg/files/ftp_upload/63109/63109fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63109/63109fig02.jpg" /> 
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.remotexs.ntu.edu.sg/files/ftp_upload/63109/63109fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63109/63109fig03.jpg" /> 
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&#160;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 &#38; 2020). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63109/63109fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63109/63109fig04.jpg" /> 
Figure 4: Analysis of the microbial taxa using 16S rRNA gene/ITS&#160;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.remotexs.ntu.edu.sg/files/ftp_upload/63109/63109fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63109/63109fig05.jpg" /> 
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.remotexs.ntu.edu.sg/files/ftp_upload/63109/63109fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/63109/63109fig06.jpg" /> 
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.remotexs.ntu.edu.sg/files/ftp_upload/63109/63109fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/63109/63109fig07.jpg" /> 
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.remotexs.ntu.edu.sg/files/ftp_upload/63109/63109fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/63109/63109fig08.jpg" /> 
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.remotexs.ntu.edu.sg/files/ftp_upload/63109/63109fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/63109/63109fig09.jpg" /> 
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.remotexs.ntu.edu.sg/files/ftp_upload/63109/63109fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/63109/63109fig10v2.jpg" /> 
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.remotexs.ntu.edu.sg/files/ftp_upload/63109/63109fig10largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/63109/63109fig10.jpg" /> 
Figure 11: Inputs and outputs of the EPSPSClass webserver to test user&#39;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&#39;s own reference sequences). <a href="https://www-jove-com.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/files/ftp_upload/63109/Table 1-63109R2.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.remotexs.ntu.edu.sg/files/ftp_upload/63109/Table 2_63109R2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 3: Average glyphosate concentration <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63109/Table 3-63109R2.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.remotexs.ntu.edu.sg/files/ftp_upload/63109/Table 4-63109R2.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.remotexs.ntu.edu.sg/files/ftp_upload/63109/Table 5-63109R2.xlsx" target="_blank">Please click here to download this Table.</a>

Watch this Scientific Journal Video about Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes at JoVE.com

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

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

Nanyang Technological University

Research

JoVE Journal

Environment

4.1K Views.  University of Turku. Hier bieden we een workflow met de beste bio-informaticamethode om de experimenten uit te voeren om het effect van glyfosaat op microbiomen te beoordelen. Het belangrijkste voordeel is dat het een robuuste testbare hypothese biedt voor toekomstige veld- en laboratoriumexperimenten. De beschreven methodologie kan worden toegepast op verschillende systemen, waaronder planten, dieren en microben.Elk onderdeel van de methodologie is vrij eenvoudig. Misschien wel het meest uitdagende deel i...

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

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Growers often use fungicide sprays during bloom to protect crops against disease, which exposes bees to fungicide residues. Although considered &#34;bee-safe,&#34; there is mounting evidence that fungicide residues in pollen are associated with bee declines (for both honey and bumble bee species). While the mechanisms remain relatively unknown, researchers have speculated that bee-microbe symbioses are involved. Microbes play a pivotal role in the preservation and/or processing of pollen, which serves as nutrition for larval bees. By altering the microbial community, it is likely that fungicides disrupt these microbe-mediated services, and thereby compromise bee health. This manuscript describes the protocols used to investigate the indirect mechanism(s) by which fungicides may be causing colony decline. Cage experiments exposing bees to fungicide-treated flowers have already provided the first evidence that fungicides cause profound colony losses in a native bumble bee (Bombus impatiens). Using field-relevant doses of fungicides, a series of experiments have been developed to provide a finer description of microbial community dynamics of fungicide-exposed pollen. Shifts in the structural composition of fungal and bacterial assemblages within the pollen microbiome are investigated by next-generation sequencing and metagenomic analysis. Experiments developed herein have been designed to provide a mechanistic understanding of how fungicides affect the microbiome of pollen-provisions. Ultimately, these findings should shed light on the indirect pathway through which fungicides may be causing colony declines.

Microbial consortia within bumble bee hives enrich and preserve pollen for bee larvae. Using next generation sequencing, along with laboratory and field-based experiments, this manuscript describes protocols used to test the hypothesis that fungicide residues alter the pollen microbiome, and colony demographics, ultimately leading to colony loss.

Growers often use fungicide sprays during bloom to protect crops against disease, which exposes bees to fungicide residues. Although considered ...

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to abiotic stresses and diseases. As the plant microbiome can be highly complex, low-cost, high-throughput methods such as amplicon-based sequencing of the 16S rRNA gene are often preferred for characterizing its microbial composition and diversity. However, the selection of appropriate methodology when conducting such experiments is critical for reducing biases that can make analysis and comparisons between samples and studies difficult. This protocol describes in detail a standardized methodology for the collection and extraction of DNA from soil, rhizosphere, and root samples. Additionally, we highlight a well-established 16S rRNA amplicon sequencing pipeline that allows for the exploration of the composition of bacterial communities in these samples, and can easily be adapted for other marker genes. This pipeline has been validated for a variety of plant species, including sorghum, maize, wheat, strawberry, and agave, and can help overcome issues associated with the contamination from plant organelles.

Here, we describe a protocol to obtain amplicon sequence data of soil, rhizosphere, and root endosphere microbiomes. This information can be used to investigate the composition and diversity of plant-associated microbial communities, and is suitable for the use with a wide range of plant species.

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to ...

Genetics

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The purpose of this manuscript is to provide detail on how to rapidly sample soil, rhizosphere, and endosphere of replicated field trials and analyze changes that may occur in the microbial communities due to sample type, treatment, and plant genotype. The experiment used to demonstrate these methods consists of replicated field plots containing two, pure, warm-season grasses (Panicum virgatum and Andropogon gerardii) and a low-diversity grass mixture (A. gerardii, Sorghastrum nutans, and Bouteloua curtipendula). Briefly, plants are excavated, a variety of roots are cut and placed in phosphate buffer, and then shaken to collect the rhizosphere. Roots are brought to the laboratory on ice and surface sterilized with bleach and ethanol (EtOH). The rhizosphere is filtered and concentrated by centrifugation. Excavated soil from around the root ball is placed into plastic bags and brought to the lab where a small amount of soil is taken for DNA extractions. DNA is extracted from roots, soil, and rhizosphere and then amplified with primers for the V4 region of the 16S rRNA gene. Amplicons are sequenced, then analyzed with open access bioinformatics tools. These methods allow researchers to test how the microbial community diversity and composition varies due to sample type, treatment, and plant genotype. Using these methods along with statistical models, the representative results demonstrate there are significant differences in microbial communities of roots, rhizosphere, and soil. Methods presented here provide a complete set of steps for how to collect field samples, isolate, extract, quantify, amplify, and sequence DNA, and analyze microbial community diversity and composition in replicated field trials.

Excavation of plant roots from the field as well as processing of samples into endosphere, rhizosphere, and soil are described in detail, including DNA extraction and data analysis methods. This paper is designed to enable other laboratories to use these techniques for the study of soil, endosphere, and rhizosphere microbiomes.

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The ...

A Gnotobiotic System for Studying Microbiome Assembly in the Phyllosphere and in Vegetable Fermentation

The phyllosphere, the above ground portion of the plant that can be colonized by microbes, is a useful model system to identify processes of microbial community assembly. This protocol outlines a system for studying microbial community dynamics in the phyllosphere of Napa cabbage plants. It describes how to grow germ-free plants in test tubes with a calcined clay and nutrient broth substrate. Inoculation of germ-free plants with specific microbial cultures provides opportunities to measure microbial growth and community dynamics in the phyllosphere. Through the use of sterile vegetable extract produced from cabbages shifts in microbial communities that occur during fermentation can also be assessed. This system is relatively simple and inexpensive to set up in the lab and can be used to address key ecological questions in microbial community assembly. It also provides opportunities to understand how phyllosphere community composition can impact the microbial diversity and quality of vegetable fermentations. This approach for developing gnotobiotic cabbage phyllosphere communities could be applied to other wild and agricultural plant species.

A method of growing germ-free Napa cabbages has been developed which enables researchers to evaluate how single microbial species or multispecies microbial communities interact on cabbage leaf surfaces. A sterile vegetable extract is also presented which can be used to measure shifts in community composition during vegetable fermentation.

The phyllosphere, the above ground portion of the plant that can be colonized by microbes, is a useful model system to identify processes of microbial ...

Collection of Alfalfa Root Exudates to Study the Impact of Di(2-ethylhexyl) Phthalate on Metabolite Production

Root exudates are the main media of information communication and energy transfer between plant roots and the surrounding environment. The change in secretion of root exudates is usually an external detoxification strategy for plants under stress conditions. This protocol aims to introduce general guidelines for the collection of alfalfa root exudates to study the impact of di(2-ethylhexyl) phthalate (DEHP) on metabolite production. First, alfalfa seedlings are grown under DEHP stress in a hydroponic culture experiment. Second, the plants are transferred to centrifuge tubes containing 50 mL of sterilized ultrapure water for 6 h to collect root exudates. The solutions are then freeze-dried in a vacuum freeze dryer. The frozen samples are extracted and derivatized with bis(trimethylsilyl)) trifluoroacetamide (BSTFA) reagent. Subsequently, the derivatized extracts are measured using a gas chromatograph system coupled with a time-of-flight mass spectrometer (GC-TOF-MS). The acquired metabolite data are then analyzed based on bioinformatic methods. Differential metabolites and significantly changed metabolism pathways should be deeply explored to reveal the impact of DEHP on alfalfa in view of root exudates.

Collection of Alfalfa Root Exudates to Study the Impact of Di2-ethylhexyl Phthalate on Metabolite Production

The secretion of root exudates is usually an external detoxification strategy for plants under stress conditions. This protocol describes how to assess the impact of xenobiotics on alfalfa via nontargeted metabolomic analysis.

Root exudates are the main media of information communication and energy transfer between plant roots and the surrounding environment. The change in ...

Using TLC-Direct Bioautography Assay to Identify Natural Products for Biocontrol

Bioassay-Guided Identification of Natural Products for Biocontrol by Thin Layer Chromatography-Direct Bioautography

Thin layer chromatography-direct bioautography (TLC-DB) is a well-established bioassay used to separate and identify natural products (NPs) that are antagonistic against a target pathogen. It is a rapid, inexpensive, and simple option for the bioassay-guided isolation and identification of NPs that hinges on separation by TLC coupled with the direct application of a target pathogen to examine bioactivity. It is typically used for the analysis of bioactive plant extracts, detecting inhibitory activity against bacteria, fungi, and enzymes. That being said, it has great potential in bacterial NP discovery, particularly for evaluating bacterial NPs against pertinent agricultural pathogens, which is valuable for discovering and developing novel biopesticides for the agriculture industry. Furthermore, it is a tunable protocol that could be applied to other target pathogens or sources of NPs in research programs concerning the discovery and identification of bioactive compounds. Herein, we describe a model system for discovering and identifying biopesticide NPs using TLC-DB with Bacillus spp. and the agricultural pathogen Sclerotinia sclerotiorum.

Author Spotlight: Discovering New Biopesticides from Bioactive Soil Microbe-Derived Natural Products

We describe the use of thin-layer chromatography direct bioautography assay and liquid chromatography-mass spectrometry to identify microbial natural products that display antagonism against fungal pathogens using the pathogen Sclerotinia sclerotiorum and biopesticidal Bacillus isolates as a model system.

Thin layer chromatography-direct bioautography (TLC-DB) is a well-established bioassay used to separate and identify natural products (NPs) that are ...

Biochemistry

Quantitative Polymerase Chain Reaction-based Analyses of Murine Intestinal Microbiota After Oral Antibiotic Treatment

The gut microbiota has a central influence on human health. Microbial dysbiosis is associated with many common immunopathologies such as inflammatory bowel disease, asthma and arthritis. Thus, understanding the mechanisms underlying microbiota-immune system crosstalk is of crucial importance. Antibiotic administration, while aiding pathogen clearance, also induces drastic changes in the size and composition of intestinal bacterial communities which can have an impact on human health. Antibiotic treatment in mice recapitulates the impact and long-term changes in human microbiota from antibiotic treated patients, and enables investigation of the mechanistic links between changes in microbial communities and immune cell function. While several methods for antibiotic treatment of mice have been described, some of them induce severe dehydration and weight-loss complicating the interpretation of the data. Here, we provide two protocols for oral antibiotic administration which can be used for long-term treatment of mice without inducing major weight-loss. These protocols make use of a combination of antibiotics that target both Gram-positive and Gram-negative bacteria and can be provided either ad libitum in the drinking water or by oral gavage. Moreover, we describe a method for the quantification of microbial density in fecal samples by qPCR which can be used to validate the efficacy of the antibiotic treatment. The combination of these approaches provides a reliable methodology for the manipulation of the intestinal microbiota and the study of the effects of antibiotic treatment in mice.

Here we provide detailed protocols for the oral administration of antibiotics to mice, collection of fecal samples, DNA extraction and quantification of fecal bacteria by qPCR.

The gut microbiota has a central influence on human health. Microbial dysbiosis is associated with many common immunopathologies such as inflammatory ...

Immunology and Infection

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

The emerging role of the gut microbiome in several human diseases demands a breakthrough of new tools, techniques and technologies. Such improvements are needed to decipher the utilization of microbiome modulators for human health benefits. However, the large-scale screening and optimization of modulators to validate microbiome modulation and predict related health benefits may be practically difficult due to the need for large number of animals and/or human subjects. To this end, in vitro or ex vivo models can facilitate preliminary screening of microbiome modulators. Herein, it is optimized and demonstrated an ex vivo fecal microbiota culture system that can be used for examining the effects of various interventions of gut microbiome modulators including probiotics, prebiotics and other food ingredients, aside from nutraceuticals and drugs, on the diversity and composition of the human gut microbiota. Inulin, one of the most widely studied prebiotic compounds and microbiome modulators, is used as an example here to examine its effect on the healthy fecal microbiota composition and its metabolic activities, such as fecal pH and the fecal levels of organic acids including lactate and short-chain fatty acids (SCFAs). The protocol may be useful for studies aimed at estimating the effects of different interventions of modulators on fecal microbiota profiles and at predicting their health impacts.

This protocol describes an in vitro batch-culture fermentation system of human fecal microbiota, using inulin (a well-known prebiotic and one of the most widely studied microbiota modulators) to demonstrate the use of this system in estimating effects of specific interventions on fecal microbiota composition and metabolic activities.

The emerging role of the gut microbiome in several human diseases demands a breakthrough of new tools, techniques and technologies. Such improvements are ...

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome&#8217;s role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Described here is a simplified standard operating procedure for microbiome profiling using 16S rRNA metagenomic sequencing and analysis using freely available tools. This protocol will help researchers who are new to the microbiome field as well as those requiring updates on methods to achieve bacterial profiling at a higher resolution.

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the ...

Biology

Preparing and Rearing Axenic Insects with Tissue Cultured Seedlings for Host-Gut Microbiota Interaction Studies of the Leaf Beetle

Insect guts are colonized by diverse bacteria that can profoundly impact the host&#39;s physiological traits. Introducing a particular bacterial strain into an axenic insect is a powerful method to verify gut microbial function and elucidate the mechanisms underlying gut microbe-host interactions. Administering antibiotics or sterilizing egg surfaces are two commonly used methods to remove gut bacteria from insects. However, in addition to the potential adverse effects of antibiotics on insects, previous studies indicated that feeding antibiotics could not eliminate gut bacteria. Thus, germ-free artificial diets are generally employed to maintain axenic insects, which is a tedious and labor-intensive process that cannot fully resemble nutritional components in natural food. Described here is an efficient and simple protocol to prepare and maintain axenic larvae of a leaf beetle&#160;(Plagiodera versicolora). Specifically, surfaces of the beetle eggs were sterilized, following which germ-free poplar leaves were used to rear axenic larvae. The axenic status of the insects was further confirmed via culture-dependent and culture-independent assays. Collectively, by combining egg disinfection and germ-free cultivation, an efficient and convenient method was developed to obtain axenic P. versicolora, providing a readily transferable tool for other leaf-eating insects.

To obtain an axenic insect, its egg surface is sterilized, and the hatched larva is subsequently reared using axenic leaves. This method provides an efficient way for axenic insect preparation without administering antibiotics or developing an artificial diet, which can also be applied to other leaf-eating insects.

Insect guts are colonized by diverse bacteria that can profoundly impact the host&#39;s physiological traits. Introducing a particular bacterial strain ...

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

Samenvatting

Meer video's verkennen