This work was supported by the ERC HGTCODONUSE (ERC-2015-CoG-682819) to S.B. Data used in this work were (partly) produced through the GenSeq technical facilities of the Institut des Sciences de l&#39;Evolution de Montpellier with the support of LabEx CeMEB, an ANR &#34;Investissements d&#39;avenir&#34; program (ANR-10-LABX-04-01).

Setting up this protocol requires precise knowledge of the insertion, deletion, inversion, or duplication point within the ancestral sequence. This information is usually obtained by whole-genome sequencing (WGS) of the end or intermediate point samples. In the following protocol, the general principle for the case of an insertion mutation is given for each step, alongside a representative case where the frequency of an IS10 insertion in the mutS gene in an experimental evolution population of E. coli i...

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D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+determination+of+barriers+to+horizontal+gene+transfer.">Genome-wide determination of barriers to horizontal gene transfer.</a> bioRxiv. , (2022).</li><li>Slomka, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+evolution+of+Bacillus+subtilis+reveals+the+evolutionary+dynamics+of+horizontal+gene+transfer+and+suggests+adaptive+and+neutral+effects.">Experimental evolution of Bacillus subtilis reveals the evolutionary dynamics of horizontal gene transfer and suggests adaptive and neutral effects.</a> Genetics. 216 (2), 543-558 (2020).</li><li>Choudhury, D., Saini, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolution+of+Escherichia+coli+in+different+carbon+environments+for+2,000+generations.">Evolution of Escherichia coli in different carbon environments for 2,000 generations.</a> Journal of Evolutionary Biology. 32 (12), 1331-1341 (2019).</li><li>Tenaillon, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tempo+and+mode+of+genome+evolution+in+a+50,000-generation+experiment.">Tempo and mode of genome evolution in a 50,000-generation experiment.</a> Nature. 536 (7615), 165-170 (2016).</li><li>Behringer, M. 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The authors have no conflicts of interest to disclose.

Here, we have proposed a cost-effective method that allows the dynamics of emerging adaptive SV alleles in experimental evolution populations to be followed. This method couples classic PCR techniques and automated parallel capillary electrophoresis, allowing for the relative quantities of two alleles to be determined. Once set up, it permits the quantification of allele proportions in many samples in parallel, and is much less expensive than WGS. This method can be seen as an equivalent to amplicon sequencing for non-SN...

Structural variants (SVs) are alterations of the genomic sequence, generally affecting 50 bp or more. The four categories of described SVs are large insertions, large deletions, inversions, and duplications. Until recently, more attention has been devoted to single-nucleotide variants (SNVs) than to structural variants, in terms of their phenotypic effects and their role as genetic determinants of disease, or their contribution to adaptation. This is probably because it is easier to both detect SNVs and predict their phenotypic effects. However, short- and long-read deep sequencing technologies have strongly improved the detection of SVs, at least in single individual or clonal genomes1. In parallel, their phenotypic effects have been better characterized, and many examples of their implication as genetic determinants of human disease2,3 or adaptation to a new environment4 have been documented.
Deletions and insertions, often due to mobile genetic element (MGE) insertions, are much more disruptive than single nucleotide polymorphisms (SNPs) and lead to frameshift mutations and protein structure modifications. Deletions and MGE insertions within genes almost always result in gene inactivation, and insertions into non-coding regions can lead to repression or constitutive expression of adjacent genes when insertion sequences (ISs) contain promoter or termination sequences5. While the knockout of essential genes leads to clear detrimental effects on bacterial fitness, the loss of non-essential genes is beneficial in some cases. Despite their inherent costs, duplications can also be advantageous, and participate in adaptation as they lead to a change in gene dosage; an increase in the activity of a specific protein can be advantageous depending on the conditions6.
Microbial experimental evolution populations are usually started with clones. This initial absence of genetic diversity, combined with the &#34;closed environment&#34; characteristic of test tubes, leads to a very limited potential of evolution by gene gain through horizontal gene transfer and recombination. In these specific conditions, the contribution to adaptation of deletions, duplications, and intragenomic MGE insertion is particularly important; bacteria often adapt through loss-of-function mutations (mainly due to deletions or MGE insertions), affecting genes that are not useful in stable, often nutrient-rich, monoculture artificial environments7. In the longest running E. coli evolution experiment, IS150 insertions are particularly frequent amongst populations evolved after 50,000 generations, with IS elements representing 35% of mutations that reach high frequency in populations that retain their ancestral point mutation rate8.
Evolve and resequence studies couple experimental evolution and next-generation sequencing (NGS) technologies to investigate how bacteria adapt, at the phenotypic and genomic levels, to different environmental conditions and stresses, such as different carbon and energy sources, antibiotics, and osmotic stress9,10,11. These studies typically obtain genomic information on the evolved populations or clones solely at the experimental end point, and in some cases, at a number of intermediate time points12,13,14. These data provide insight into genes and pathways involved in the adaptation to a given environment, but rarely allow researchers to follow the dynamics of de novo emerging and sweeping alleles over time.
One approach to follow these dynamics is to choose a limited number of segregating alleles of interest (because of the function of the genes they affect, because they sweep in parallel in independent populations, etc.) and use amplicon sequencing to quantify the allele proportion, pooling many time points in the same sequencing run15. This method has been successfully used to follow the dynamics of small size variants (SNPs or 1 bp indels) in experimental16 and natural17 populations of microbes. However, in the case of larger indels or MGE insertions, the size difference of the amplicons induces PCR efficiency differences, which distort the relationship between read and allele proportions. In certain cases, the size difference between the two alleles is superior to the classical length of the amplicon. Here, we coupled a triplet PCR technique with automated parallel capillary electrophoresis to quantify the relative frequency of an insertion allele based on size discrimination. This approach allows the exploitation of underused experimental time points to determine the dynamics of an emerging mutant allele and to follow its frequency to fixation or loss, in a cost-effective manner. We applied this method to track emerging mutS-&#160;alleles, mutated through an IS10 insertion, providing the mutated genotype with a hypermutator phenotype.
This method requires two target alleles with a &#8805;5% difference in size. First, primer triplets are designed to produce similarly sized fragments, which share a common primer. Second, PCR conditions are optimized, and a calibration curve is produced using mixes of wild-type (WT) and mutant gDNA. Lastly, samples are amplified by PCR, and the relative frequency of each allele is quantified by parallel quantitative capillary electrophoresis.

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		<tr>
			<td>96 Well Skirted PCR Plate</td>
			<td>4titude</td>
			<td>4Ti - 0740</td>
			<td>PCR</td>
		</tr>
		<tr>
			<td>Agarose molecular biology grade</td>
			<td>Eurogentec</td>
			<td>EP-0010-05</td>
			<td>Agarose gel electrophoresis&#160;</td>
		</tr>
		<tr>
			<td>Agilent DNF-474 HS NGS Fragment Kit Quick Guide for the Fragment Analyzer Systems</td>
			<td>Agilent</td>
			<td></td>
			<td>PDF instruction guide</td>
		</tr>
		<tr>
			<td>Buffer TBE</td>
			<td>Panreac appliChem</td>
			<td>A4228,5000Pc</td>
			<td>Agarose gel electrophoresis&#160;</td>
		</tr>
		<tr>
			<td>Calibrated Disposable Inoculating Loops and Needles</td>
			<td>LABELIANS</td>
			<td>8175CSR40H</td>
			<td>Bacterial culture</td>
		</tr>
		<tr>
			<td>Dneasy Blood and Tissue Kit</td>
			<td>Qiagen</td>
			<td>69506</td>
			<td>DNA extraction</td>
		</tr>
		<tr>
			<td>Electrophoresis power supply</td>
			<td>Amilabo</td>
			<td>ST606T</td>
			<td>Agarose gel electrophoresis&#160;</td>
		</tr>
		<tr>
			<td>Fragment Analyzer Automated CE System</td>
			<td>Agilent</td>
			<td></td>
			<td>Parallel capillary electrophoresis</td>
		</tr>
		<tr>
			<td>Fragment DNA Ladder</td>
			<td>Agilent</td>
			<td>DNF-396, range 1-6000bp</td>
			<td>Parallel capillary electrophoresis</td>
		</tr>
		<tr>
			<td>GENTAMICIN SULFATE SALT BIOREAGENT</td>
			<td>Sigma-Aldrich</td>
			<td>G1264-1G</td>
			<td>Bacterial culture</td>
		</tr>
		<tr>
			<td>High Sensitivity diluent marker</td>
			<td>Agilent</td>
			<td>DNF-373</td>
			<td>Parallel capillary electrophoresis</td>
		</tr>
		<tr>
			<td>High Sensitivity NGS quantitative analysis kit</td>
			<td>Agilent</td>
			<td>DNF-474</td>
			<td>Parallel capillary electrophoresis</td>
		</tr>
		<tr>
			<td>Ladder quick load 1 kb plus DNA ladder</td>
			<td>NEB</td>
			<td>N0469S</td>
			<td>Agarose gel electrophoresis&#160;</td>
		</tr>
		<tr>
			<td>LB Broth, VegitoneNutriSelect Plus</td>
			<td>Millipore</td>
			<td>28713</td>
			<td>Bacterial culture</td>
		</tr>
		<tr>
			<td>Master Mix PCR High Fidelity Phusion Flash</td>
			<td>Thermo Fisher Scientific</td>
			<td>F548L</td>
			<td>PCR</td>
		</tr>
		<tr>
			<td>Primers</td>
			<td>Eurogentec</td>
			<td></td>
			<td>PCR</td>
		</tr>
		<tr>
			<td>Prosize data analysis software v.4</td>
			<td>Agilent</td>
			<td>V.4</td>
			<td>Parallel capillary electrophoresis</td>
		</tr>
		<tr>
			<td>Qubit assays</td>
			<td>Invitrogen</td>
			<td>MAN0010876</td>
			<td>DNA quantification</td>
		</tr>
		<tr>
			<td>Qubit dsDNA HS Assay Kit</td>
			<td>LIFE TECHNOLOGIES SAS</td>
			<td>Q32854</td>
			<td>DNA quantification</td>
		</tr>
		<tr>
			<td>Thermocycler</td>
			<td>Eppendorf</td>
			<td>Ep gradients</td>
			<td>PCR</td>
		</tr>
		<tr>
			<td>UVbox, eBOX VX5</td>
			<td>Vilber Lourmat</td>
			<td></td>
			<td>Agarose gel electrophoresis visualisation</td>
		</tr>
		<tr>
			<td>Water for injectable preparation</td>
			<td>Aguettant</td>
			<td>PROAMP</td>
			<td>PCR</td>
		</tr>
	</tbody>
</table>

following the dynamics of structural variants in experimentally evolved populations

Structural variants (SVs) (i.e., deletions, insertions, duplications, and inversions) are now known to play an important role in phenotypic variation, and consequently in processes such as disease determination or adaptation to a new environment. However, single-nucleotide variants receive much more attention than SVs, probably because they are easier to detect, and their phenotypic effects are easier to predict. The development of short- and long-read deep sequencing technologies have strongly improved the detection of SVs, but the quantification of their frequency from pooled sequencing (poolseq) data is still technically complex and expensive.
Here, we present a rather simple and inexpensive method, which allows researchers to follow the dynamics of SV allele frequency. As an example of application, we follow the frequency of an insertion sequence (IS) insertion in experimental evolution populations of bacteria. This method is based on the design of triplets of primers around the structural variant borders, such that the amplicons produced by amplification of the wild-type (WT) and derived alleles differ in size by at least 5%, and that their amplification efficiency is similar. The quantity of each amplicon is then determined by parallel capillary electrophoresis and normalized to a calibration curve. This method can be easily extended to the quantification of the frequency of other structural variants (deletions, duplications, and inversions) and to pool-seq approaches of natural populations, including within-patient pathogen populations.

Using DNA extracted from an ancestral clone and a hypermutator clone isolated from the S2.11 population at generation 1,000, we established the calibration curve shown in Figure 2. The actual mutant proportions from laboratory-prepared DNA mixes and measured by the parallel capillary electrophoresis instrument were linked by a linear relationship of slope 1.0706, with an R2 of 0.9705. Additionally, there was a good agreement between biological replicates; the standard deviation wa...

Watch this Scientific Journal Video about Following the Dynamics of Structural Variants in Experimentally Evolved Populations at JoVE.com

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

We developed a cost-effective method to follow non-single nucleotide polymorphism allele dynamics that can easily be adapted to experimental evolution frozen archives. A triplet PCR technique was coupled with automated parallel capillary electrophoresis to quantify the relative frequency of an insertion allele over the course of experimental evolution.

Structural variants (SVs) (i.e., deletions, insertions, duplications, and inversions) are now known to play an important role in phenotypic variation, and ...

Structural variants such as insertions, deletions, duplications, and inversions have in the past been more difficult to follow experimentally, and their frequencies cannot be accurately measured by amplicon sequencing. Here we provide a simple cost effective technique, which couples triplicate primer design and parallel capillary electropheresis to follow structural variant allele frequencies over time. This method was designed to complement endpoint sequencing in experimental evolution and to retrace the frequencies of emerging de novo alleles.We hope this method will also benefit those working with intra hosts pathogen data. Demonstrating the procedure will be Jeanne Hamet, a research assistant from our laboratory. The different steps of the methods will be shown in a case where the derived allele results from an IS10 insertion in a bacterial gene called mutS.First design a pair of primers to amplify a short amplicon on the wild type allele around the mutant insertion site. Design a third primer forward primer two within the insertion sequence to produce a slight size variable from the wild type amplicon. Begin by extracting DNA from 24-hour cultures of fixed wild type and mutant allele clones.After quantifying the DNA, dilute each DNA extract to five nanograms per microliter with water. Fix the two samples in a 50/50 ratio. Set up a 20-microliter reaction containing 10 nanograms of DNA sample, primers, and PCR master mix.Next, separate the PCR product by electrophoresis, using a 2%agarose gel. The size difference between the amplicons from the wild type and the mutant has to be verified on the gel. To obtain a calibration curve, begin by mixing the wild type DNA and mutant DNA in various ratios.Dilute the PCR products to 0.1 nanograms per microliter concentration. To prepare the separation gel, mix the fresh gel and dye. Replace the inlet buffer.Place the rinse buffer in the correct drawer location of the parallel capillary electrophoresis instrument. Add 22 microliters of the diluent marker to the wells of a 96 well plate. Now add two microliters of the diluted PCR products to each sample well.To one Well, add a DNA size ladder from a high sensitivity kit, ranging from one to 6, 000 base pairs in size. Place the microplate in the parallel capillary electrophoresis instrument in select run on the instrument software. Analyze the results using the data analysis software.First, identify the peaks based on their known size. Quantify each amplicon to produce a calibration curve. Finally, verify that the relative quantities of the two alleles are correctly measured.This calibration curve is then used to calculate the quantity of structural variants after PCR amplification and parallel capillary electrophoresis. These representative results follow a disruptive insertion in the mutS gene. Knockdowns and mutS lead to a hyper mutator phenotype, allowing bacteria to sample more mutations than their base mutation rate.Using the method presented, the mutS structural variant frequency was tracked across 1000 generations. A non-monotonic trajectory of the emerging mutant allele was revealed. The mutant allele was first detected at generation 680.The allele then rapidly increased in frequency, reaching 67%by generation 713. This increase then stagnated, increasing only 10%over the next 53 generations, to 76%Unexpectedly, the mutant allele frequency then decreased from 76%to 49%over the course of 13 generations, after which it increased to fixation. This method will benefit those working on experimental evolution.This protocol allows the user to take frozen archives and to explore evolutionary trajectories that are otherwise not observable with endpoint sequencing data.

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JoVE Journal

Biology

868 Views.  University of Montpellier, CNRS, EPHE, IRD, Montpellier, France. Structural variants such as insertions, deletions, duplications, and inversions have in the past been more difficult to follow experimentally, and their frequencies cannot be accurately measured by amplicon sequencing. Here we provide a simple cost effective technique, which couples triplicate primer design and parallel capillary electropheresis to follow structural variant allele frequencies over time. This method was designed to complement endpoint sequencing in experimental evolution and t...