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 is followed. In this population, WGS of the endpoint population identified the insertion of a 1,329 bp IS10 between positions 2,463 and 2,471, resulting in the duplication of this insertion site. This method is applicable to the three other SV types, and the specificities of each case are given in the discussion.
1. Design of triplet primers
<ol>
	<li>Use classic primer design practices (18-24 bp, 40%-60% GC content, start/end with G/C pairs where possible, Tm difference &#60; 5 &#176;C) to produce primers FW1 and RV1. Design primers to amplify a short amplicon on the WT allele around the mutant allele insertion site (Figure 1). 
	NOTE: The amplicon size can range from 100 bp to up to 3,000 bp, in line with the DNA size ladder used in the capillary electrophoresis. In this example, a 155 bp amplicon was amplified. The small fragment size chosen here prevents&#160;off-target amplification of the whole IS10 insertion sequence (see section 2).</li>
	<li>Design a second forward primer FW2 within the insertion sequence, to produce a second amplicon that is approximately 5% larger or smaller than the WT amplicon (Figure 1). This 5% size difference is the minimum size difference that the parallel capillary electrophoresis device could reliably distinguish. Therefore, design the primers such that the two amplicons have a difference in size, which is above but as close as possible to the relative threshold. 
	NOTE: Be sure to minimize the Tm difference and primer dimer formation. In this example, a second forward primer was designed to produce a 226 bp amplicon, 71 bp larger than the WT amplicon. In this representative example, the primer sequences are as follows: 
	FW1:AAAGCATTTCGCCGAACGCC 
	RV1: GCGATAAATCCACTCCAGCGCC 
	FW2: AGTTCGCTTAGGCATGGAAG</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64709/64709fig01.jpg" style="opacity: 0.9;" /> 
Figure 1: Schema of triplet primer design on mutS WT gene and mutant mutS IS10 insertion.&#160;The black triangle represents the IS10 insertion site in the&#160;mutS&#160;gene. The WT gene is in blue, and the IS10 is in orange. Primers FW1 and RV1 flag the IS10 insertion site and produce a 155 bp WT amplicon. The RV1 primer and the intra-IS10 primer FW2 produce a second 226 bp amplicon.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64709/64709fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Optimization of PCR conditions
<ol>
	<li>Grow an overnight culture of fixed WT and mutant allele clones.</li>
	<li>Extract the DNA using any kit.</li>
	<li>Quantify the DNA.</li>
	<li>Prepare the sample of DNA by diluting the extraction of WT and mutant DNA to 5 ng/&#181;L. Mix the two DNA samples in a 50/50 ratio.</li>
	<li>Amplify 10 ng of the three DNA samples (WT, mutant, 50/50 mix) using a 2x ready-to-use PCR master mix, 0.5 &#181;M FW1 primer, 1 &#181;M RV1 primer, and 0.5 &#181;M FW2 primer in a 20 &#181;L reaction volume. Use a 2% agarose gel to migrate the PCR product by classical electrophoresis, and determine the optimal PCR conditions. 
	NOTE: Elongation times should be minimized to prevent formation of the FW1 and RV1 amplicon on the mutant allele. The annealing temperature should be adjusted to minimize biased amplification of the alleles and non-specific amplification.
	<ol>
		<li>To follow the program in this example, use the following settings: 98 &#176;C for 10 s, followed by 25 cycles of 98 &#176;C for 1 s, 58 &#176;C for 15 s, 72 &#176;C for 8 s, and a final elongation step at 72 &#176;C for 1 min. 
		&#8203;NOTE: The elongation time was reduced to 8 s to prevent amplification of the &#62;1,000 bp product from the forward primer and the RV1 primer on the mutant allele (mutS with IS10 insertion).</li>
	</ol>
	</li>
</ol>
3. Calibration curve
<ol>
	<li>Mix the two DNA samples, WT and mutant, in 10/90, 25/75, 40/60, 50/50, 60/40, 75/25, and 90/10 ratios. 
	NOTE: Biological replicates are prepared from independent overnight bacterial cultures.</li>
	<li>Amplify using optimized PCR conditions (see section 2).</li>
	<li>Quantify the amplicon product.</li>
	<li>Dilute the PCR products to 0.1 ng/&#181;L.</li>
	<li>Prepare the parallel capillary electrophoresis instrument.
	<ol>
		<li>Mix fresh gel and dye (NGS quantitative analysis kit (22, 33, or 55); HS NGS fragment 1-6,000 bp for this example). 
		NOTE: See the parallel capillary electrophoresis HS NGS fragment guide for detailed instructions (see the Table of Materials).</li>
	</ol>
	</li>
	<li>Replace the capillary storage solution and the inlet buffer, and place the rinse buffer plate in the correct drawer locations of the parallel capillary electrophoresis instrument.</li>
	<li>Add 2 &#181;L of HS diluent marker to 22 &#181;L of each diluted sample in a 96-well plate.</li>
	<li>Add a size ladder (DNA size ladder; range 1-6,000 bp) from a HS NGS quantitative analysis kit to one well of the 96-well plate.</li>
	<li>Place the 96-well plate in the correct drawer of the parallel capillary electrophoresis instrument, and select Run on the parallel capillary electrophoresis instrument software.</li>
	<li>Analyze the results using the data analysis software, which detects and identifies each peak of the size ladder, assigning each peak of the samples to their known actual size.
	<ol>
		<li>When using a quantitative kit, use the software to determine the DNA quantity of each fragment by integrating the area under the peak, as in chromatography data analysis. Again, compare samples with known amounts in standards to quantify each peak from a sample, and calculate the ratios between the different peaks detected in the samples.</li>
		<li>Construct a calibration curve (Figure 2), linking the known proportion of the mutant allele (DNA mix) to the one measured using the parallel capillary electrophoresis instrument. This calibration curve allows the reliability of the method to be evaluated and corrected for minor amplification bias.</li>
	</ol>
	</li>
</ol>
4. Sample preparation
<ol>
	<li>Grow time point samples overnight in standard conditions.</li>
	<li>Extract the DNA.</li>
	<li>Quantify the DNA.</li>
	<li>Amplify the samples using optimized PCR conditions (see section 2).</li>
	<li>Run the samples in the parallel capillary electrophoresis instrument (see steps 3.5-3.10).</li>
</ol>
5. Allele quantification
<ol>
	<li>Extract mutant allele quantities from the parallel capillary electrophoresis instrument data with the software, and calculate the actual proportions by plotting these values on the calibration curve.</li>
</ol>

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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-SNP mutations&#160;and as a solution to the technical limitations of amplicon sequencing of large SVs. We have shown that the method accurately quantifies the proportion of non-SNP mutants, using a calibration curve that overcomes PCR bias (Figure 2).
Characterizing SV allele dynamics can be useful to identify linked mutations through their parallel temporal dynamics, or to calculate selection coefficients and identify their variations across time. In the example of application we have presented here, we identified, by end-point WGS, that hypermutators were segregating or fixed in various experimental evolution E. coli populations18. These populations were considered hypermutator as they had disruptive mutations (IS insertion) in one of the methyl mismatch repair (MMR)&#160;genes and had accumulated many more mutations over the 1,000 generations of experimental evolution than the ones not carrying mutations in the MMR genes (24.38 &#177; 1.75 vs. 126.7 &#177; 19.57). Mutations in MMR genes are known to lead to an increased mutation rate, allowing bacteria to sample many more mutations than their baseline mutation rates permit12,13. However, a large increase in mutation rate also translates to a faster accumulation of deleterious mutations and a greater mutational load14,15. Mutators have been predicted to be transiently advantageous in populations adapting to new environments, as the proportion of beneficial mutations is larger in this situation. The beneficial genetic backgrounds generated under high mutations rate&#160;allows mutators to propagate through a population by hitchhiking on the beneficial mutations they generate19. Temporal dynamics of hypermutator propagation have been investigated by simulations, and with the method we propose here, it is possible to follow the dynamics of IS-mediated mutator alleles in experimental populations19.
In our representative results, the SV is an IS insertion, but the method described here can be easily extended to the other types of SV (see Figure 4). The triplet primer design follows exactly the same logic for deletion quantification as for insertion quantification: two primers sit around the deletion point and the third primer sits within the deleted sequence. For inversions and duplications, two of the primers sit around one of the breakpoints and the third one sits inside the inverted/duplicated fragment, close to the other breakpoint and directed toward the outside of the fragment. For insertions, deletions, and inversions, each amplicon is specific to one of the alleles, ancestral or derived. For duplications, one of the amplicons is common to the two alleles, and the other one is specific to the derived allele. This has to be considered when calculating the proportion of alleles from the calibration curve.
Finally, we have developed this method to follow an SV in experimental evolution populations of bacteria, for which &#34;metagenomic&#34; sequencing at the population level is common practice. However, this method can also be applied to experimental evolution populations of multicellular organism in a pool-seq approach, or to intra-host viral or microbial pathogen sampling17.
This method has some limitations. First, the alleles being compared must differ in size by at least 5%. This prevents us from following the dynamics of alleles produced by very small insertions or deletions. Fortunately, these allele dynamics may be followed by amplicon sequencing. Second, the sequence of the derived allele must be known to allow for the design of the primers around the insertion or deletion or breakpoints. Third, primer design and optimization are necessary for each site. Thus, this method is appropriate for following a limited number of target allele pairs of interest. If many alleles are to be followed in the same population, we suggest NGS of multiple time points.

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.

<table><tbody><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&amp;nbsp;</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&amp;nbsp;</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&amp;nbsp;</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&amp;nbsp;</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 was between 0.61 and 17.74 across the nine points of the standard curve.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64709/64709fig02.jpg" style="opacity: 0.9; font-size: 14px;" /> 
Figure 2: Calibration standard curve.&#160;Observed versus expected proportion of WT/mutant DNA mix. Error bars represent the standard error between biological replicates.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64709/64709fig02large.jpg" style="font-size: 14px;" target="_blank">Please click here to view a larger version of this figure.</a>
DNA was extracted from 19 time points ranging from generation 356 to generation 990, amplified and quantified by parallel capillary electrophoresis. Prosize software was used to identify and quantify each amplicon in each sample. These results were converted to allele proportion using the calibration curve.
The results shown in Figure 3 reveal a nonmonotonic trajectory of the hypermutator-inducing mutS allele. Generation 680 (day 102) was the first time point at which the mutant allele was in sufficient quantity to be detected by parallel capillary electrophoresis. The mutant allele then increased rapidly in frequency and reached 66.7% at generation 713 (33 generations later). The mutant allele frequency then stagnated and was 76% by generation 766, despite having previously increased rapidly in frequency. Unexpectedly, the mutant allele frequency then reduced from 76% to 49% over the course of 13 generations (2 days), after which it increased to fixation.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/64709/64709fig03.jpg" /> 
Figure 3: Dynamics of ancestral (WT) and derived (mutS::IS10) alleles over the course of a 1,000-generation experimental evolution. The ancestral allele frequency is in gray, and the derived allele frequency is in blue. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64709/64709fig03large.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/64709/64709fig04.jpg" /> 
Figure 4: Primer design and amplicon position for quantification of the four types of structural variants. Amplified fragments are depicted in black and white. Arrows represent primers at their hybridization site; dotted arrows represent primers hybridizing but not participating in any fragment amplification, either because there is no paired primer (duplication and inversion) or the elongation time has been reduced to avoid the amplification of large amplicons (insertion and deletion). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64709/64709fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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 ...

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Biology

907 Views.  University of Montpellier, CNRS, EPHE, IRD, Montpellier, France. Structurele varianten zoals inserties, deleties, duplicaties en inversies zijn in het verleden moeilijker experimenteel te volgen geweest en hun frequenties kunnen niet nauwkeurig worden gemeten door ampliconsequencing. Hier bieden we een eenvoudige kosteneffectieve techniek, die drievoudig primerontwerp en parallelle capillaire elektroferese koppelt om structurele variant allelfrequenties in de loop van de tijd te volgen. Deze methode is ontworpen om endpoint sequencing in experimentele evol...

Video: Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Natural evolution involves genetic diversity such as environmental change and a selection between small populations. Adaptive laboratory evolution (ALE) refers to the experimental situation in which evolution is observed using living organisms under controlled conditions and stressors; organisms are thereby artificially forced to make evolutionary changes. Microorganisms are subject to a variety of stressors in the environment and are capable of regulating certain stress-inducible proteins to increase their chances of survival. Naturally occurring spontaneous mutations bring about changes in a microorganism's genome that affect its chances of survival. Long-term exposure to chemostat culture provokes an accumulation of spontaneous mutations and renders the most adaptable strain dominant. Compared to the colony transfer and serial transfer methods, chemostat culture entails the highest number of cell divisions and, therefore, the highest number of diverse populations. Although chemostat culture for ALE requires more complicated culture devices, it is less labor intensive once the operation begins. Comparative genomic and transcriptome analyses of the adapted strain provide evolutionary clues as to how the stressors contribute to mutations that overcome the stress. The goal of the current paper is to bring about accelerated evolution of microorganisms under controlled laboratory conditions.

Here, we present a protocol to obtain adaptive laboratory evolution of microorganisms under conditions using chemostat culture. Also, genomic analysis of the evolved strain is discussed.

Natural evolution involves genetic diversity such as environmental change and a selection between small populations. Adaptive laboratory evolution (ALE) ...

Genetics

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

RNA viruses use RNA dependent RNA polymerases to replicate their genomes. The intrinsically high error rate of these enzymes is a large contributor to the generation of extreme population diversity that facilitates virus adaptation and evolution. Increasing evidence shows that the intrinsic error rates, and the resulting mutation frequencies, of RNA viruses can be modulated by subtle amino acid changes to the viral polymerase. Although biochemical assays exist for some viral RNA polymerases that permit quantitative measure of incorporation fidelity, here we describe a simple method of measuring mutation frequencies of RNA viruses that has proven to be as accurate as biochemical approaches in identifying fidelity altering mutations. The approach uses conventional virological and sequencing techniques that can be performed in most biology laboratories. Based on our experience with a number of different viruses, we have identified the key steps that must be optimized to increase the likelihood of isolating fidelity variants and generating data of statistical significance. The isolation and characterization of fidelity altering mutations can provide new insights into polymerase structure and function1-3. Furthermore, these fidelity variants can be useful tools in characterizing mechanisms of virus adaptation and evolution4-7.

The present article describes the steps required to isolate and characterize RNA polymerase fidelity variants of RNA viruses and how to use mutation frequency data to confirm fidelity changes in tissue culture.

RNA viruses use RNA dependent RNA polymerases to replicate their genomes. The intrinsically high error rate of these enzymes is a large contributor to the ...

Immunology and Infection

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Continuous culture methods enable cells to be grown under quantitatively controlled environmental conditions, and are thus broadly useful for measuring fitness phenotypes and improving our understanding of how genotypes are shaped by selection. Extensive recent efforts to develop and apply niche continuous culture devices have revealed the benefits of conducting new forms of cell culture control. This includes defining custom selection pressures and increasing throughput for studies ranging from long-term experimental evolution to genome-wide library selections and synthetic gene circuit characterization. The eVOLVER platform was recently developed to meet this growing demand: a continuous culture platform with a high degree of scalability, flexibility, and automation. eVOLVER provides a single standardizing platform that can be (re)-configured and scaled with minimal effort to perform many different types of high-throughput or multi-dimensional growth selection experiments. Here, a protocol is presented to provide users of the eVOLVER framework a description for configuring the system to conduct a custom, large-scale continuous growth experiment. Specifically, the protocol guides users on how to program the system to multiplex two selection pressures &#8212; temperature and osmolarity &#8212; across many eVOLVER vials in order to quantify fitness landscapes of Saccharomyces cerevisiae mutants at fine resolution. We show how the device can be configured both programmatically, through its open-source web-based software, and physically, by arranging fluidic and hardware layouts. The process of physically setting up the device, programming the culture routine, monitoring and interacting with the experiment in real-time over the internet, sampling vials for subsequent offline analysis, and post experiment data analysis are detailed. This should serve as a starting point for researchers across diverse disciplines to apply eVOLVER in the design of their own complex and high-throughput cell growth experiments to study and manipulate biological systems.

The eVOLVER framework enables high-throughput continuous microbial culture with high resolution and dynamic control over experimental parameters. This protocol demonstrates how to apply the system to conduct a complex fitness experiment, guiding users on programming automated control over many individual cultures, measuring, collecting, and interacting with experimental data in real-time.

Continuous culture methods enable cells to be grown under quantitatively controlled environmental conditions, and are thus broadly useful for measuring ...

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Advances in sequencing technology have made whole-genome and whole-exome datasets more accessible for both clinical diagnosis and cutting-edge human genetics research. Although a number of in silico algorithms have been developed to predict the pathogenicity of variants identified in these datasets, functional studies are critical to determining how specific genomic variants affect protein function, especially for missense variants. In the Undiagnosed Diseases Network (UDN) and other rare disease research consortia, model organisms (MO) including Drosophila, C. elegans, zebrafish, and mice are actively used to assess the function of putative human disease-causing variants. This protocol describes a method for the functional assessment of rare human variants used in the Model Organisms Screening Center Drosophila Core of the UDN. The workflow begins with gathering human and MO information from multiple public databases, using the MARRVEL web resource to assess whether the variant is likely to contribute to a patient&#39;s condition as well as design effective experiments based on available knowledge and resources. Next, genetic tools (e.g., T2A-GAL4 and UAS-human cDNA lines) are generated to assess the functions of variants of interest in Drosophila. Upon development of these reagents, two-pronged functional assays based on rescue and overexpression experiments can be performed to assess variant function. In the rescue branch, the endogenous fly genes are &#34;humanized&#34;&#160;by replacing the orthologous Drosophila gene with reference or variant human transgenes. In the overexpression branch, the reference and variant human proteins are exogenously driven in a variety of tissues. In both cases, any scorable phenotype (e.g., lethality, eye morphology, electrophysiology) can be used as a read-out, irrespective of the disease of interest. Differences observed between reference and variant alleles suggest a variant-specific effect, and thus likely pathogenicity. This protocol allows rapid, in vivo assessments of putative human disease-causing variants of genes with known and unknown functions.

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

The goal of this protocol is to outline the design and performance of in vivo experiments in Drosophila melanogaster to assess the functional consequences of rare gene variants associated with human diseases.

Advances in sequencing technology have made whole-genome and whole-exome datasets more accessible for both clinical diagnosis and cutting-edge human ...

Measuring Microbial Mutation Rates with the Fluctuation Assay

Fluctuation assays are widely used for estimating mutation rates in microbes growing in liquid environments. Many cultures are each inoculated with a few thousand cells, each sensitive to a selective marker that can be assayed phenotypically. These parallel cultures grow for many generations in the absence of the phenotypic marker. A subset of cultures is used to estimate the total number of cells at risk of mutations (i.e., the population size at the end of the growth period, or Nt). The remaining cultures are plated onto the selective agar. The distribution of observed resistant mutants among parallel cultures is then used to estimate the expected number of mutational events, m, using a mathematical model. Dividing m by Nt gives the estimate of the mutation rate per locus per generation. The assay has three critical aspects: the chosen phenotypic marker, the chosen volume of parallel cultures, and ensuring that the surface on the selective agar is completely dry before the incubation. The assay is relatively inexpensive and only needs standard laboratory equipment. It is also less laborious than alternative approaches, such as mutation accumulation and single-cell assays. The assay works on organisms that go through many generations rapidly and it depends on assumptions about the fitness effects of markers and cell death. However, recently developed tools and theoretical studies mean these issues can now be addressed analytically. The assay allows mutation rate estimation of different phenotypic markers in cells with different genotypes growing in isolation or in a community. By conducting multiple assays in parallel, assays can be used to study how an organism&#39;s environmental context affects spontaneous mutation rate, which is crucial for understanding antimicrobial resistance, carcinogenesis, aging, and evolution.

Here, a protocol is presented to perform a fluctuation assay and estimate microbial mutation rate using phenotypic markers. This protocol will enable researchers to assay mutations in diverse microbes and environments, determining how genotype and ecological context affect spontaneous mutation rates.

Fluctuation assays are widely used for estimating mutation rates in microbes growing in liquid environments. Many cultures are each inoculated with a few ...

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried out by Public Health laboratories and regulatory agencies. Large volumes of publicly available WGS data can be used to study pathogenic populations at a large scale. Recently, a freely available computational platform called ProkEvo was published to enable reproducible, automated, and scalable hierarchical-based population genomic analyses using bacterial WGS data. This implementation of ProkEvo demonstrated the importance of combining standard genotypic mapping of populations with mining of accessory genomic content for ecological inference. In particular, the work highlighted here used ProkEvo-derived outputs for population-scaled hierarchical analyses using the R programming language. The main objective was to provide a practical guide for microbiologists, ecologists, and epidemiologists by showing how to: i) use a phylogeny-guided mapping of hierarchical genotypes; ii) assess frequency distributions of genotypes as a proxy for ecological fitness; iii) determine kinship relationships and genetic diversity using specific genotypic classifications; and iv) map lineage differentiating accessory loci. To enhance reproducibility and portability, R markdown files were used to demonstrate the entire analytical approach. The example dataset contained genomic data from 2,365 isolates of the zoonotic foodborne pathogen Salmonella Newport. Phylogeny-anchored mapping of hierarchical genotypes (Serovar -&#62; BAPS1 -&#62; ST -&#62; cgMLST) revealed the population genetic structure, highlighting sequence types (STs) as the keystone differentiating genotype. Across the three most dominant lineages, ST5 and ST118 shared a common ancestor more recently than with the highly clonal ST45 phylotype. ST-based differences were further highlighted by the distribution of accessory antimicrobial resistance (AMR) loci. Lastly, a phylogeny-anchored visualization was used to combine hierarchical genotypes and AMR content to reveal the kinship structure and lineage-specific genomic signatures. Combined, this analytical approach provides some guidelines for conducting heuristic bacterial population genomic analyses using pan-genomic information.

This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried ...

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

The Long-Term Evolution Experiment (LTEE) has followed twelve populations of Escherichia coli as they have adapted to a simple laboratory environment for more than 35 years and 77,000 bacterial generations. The setup and procedures used in the LTEE epitomize reliable and reproducible methods for studying microbial evolution. In this protocol, we first describe how the LTEE populations are transferred to fresh medium and cultured each day. Then, we describe how the LTEE populations are regularly checked for possible signs of contamination and archived to provide a permanent frozen "fossil record" for later study. Multiple safeguards included in these procedures are designed to prevent contamination, detect various problems when they occur, and recover from disruptions without appreciably setting back the progress of the experiment. One way that the overall tempo and character of evolutionary changes are monitored in the LTEE is by measuring the competitive fitness of populations and strains from the experiment. We describe how co-culture competition assays are conducted and provide both a spreadsheet and an R package (fitnessR) for calculating relative fitness from the results. Over the course of the LTEE, the behaviors of some populations have changed in interesting ways, and new technologies like whole-genome sequencing have provided additional avenues for investigating how the populations have evolved. We end by discussing how the original LTEE procedures have been updated to accommodate or take advantage of these changes. This protocol will be useful for researchers who use the LTEE as a model system for studying connections between evolution and genetics, molecular biology, systems biology, and ecology. More broadly, the LTEE provides a tried-and-true template for those who are beginning their own evolution experiments with new microbes, environments, and questions.

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

This protocol describes how to maintain the Escherichia coli Long-Term Evolution Experiment (LTEE) by performing its daily transfers and periodic freeze-downs and how to conduct competition assays to measure fitness improvements in evolved bacteria. These procedures can serve as a template for researchers starting their own microbial evolution experiments.

The Long-Term Evolution Experiment (LTEE) has followed twelve populations of Escherichia coli as they have adapted to a simple laboratory environment for ...

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Population and family-based genetic studies typically result in the identification of genetic variants that are statistically associated with a clinical disease or phenotype. For many diseases and traits, most variants are non-coding, and are thus likely to act by impacting subtle, comparatively hard to predict mechanisms controlling gene expression. Here, we describe a general strategic approach to prioritize non-coding variants, and screen them for their function. This approach involves computational prioritization using functional genomic databases followed by experimental analysis of differential binding of transcription factors (TFs) to risk and non-risk alleles. For both electrophoretic mobility shift assay (EMSA) and DNA affinity precipitation assay (DAPA) analysis of genetic variants, a synthetic DNA oligonucleotide (oligo) is used to identify factors in the nuclear lysate of disease or phenotype-relevant cells. For EMSA, the oligonucleotides with or without bound nuclear factors (often TFs) are analyzed by non-denaturing electrophoresis on a tris-borate-EDTA (TBE) polyacrylamide gel. For DAPA, the oligonucleotides are bound to a magnetic column and the nuclear factors that specifically bind the DNA sequence are eluted and analyzed through mass spectrometry or with a reducing sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) followed by Western blot analysis. This general approach can be widely used to study the function of non-coding genetic variants associated with any disease, trait, or phenotype.

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

We present a strategic plan and protocol for identifying non-coding genetic variants affecting transcription factor (TF) DNA binding. A detailed experimental protocol is provided for electrophoretic mobility shift assay (EMSA) and DNA affinity precipitation assay (DAPA) analysis of genotype-dependent TF DNA binding.

Population and family-based genetic studies typically result in the identification of genetic variants that are statistically associated with a clinical ...

An Overview of Genetic Analysis

An organism&#8217;s physical traits, or phenotype, are a product of its genotype, which is the combination of alleles (gene variants) inherited from its parents. To varying degrees, genes interact with each other and environmental factors to generate traits. The distribution of alleles and traits within a population is influenced by a number of factors, including natural selection, migration, and random genetic drift.In this video, JoVE introduces some of the foundational discoveries in genetics, from Gregor Mendel&#8217;s elucidation of the genetic basis of inheritance, to how natural processes affect allele distributions within populations, to the modern synthesis of biology that brought together Mendelian genetics and Darwinian evolution. We then review the questions asked by geneticists today regarding how genes influence traits, and some of the main tools used to answer these questions. Finally, several applications of techniques such as genetic crosses, screens and evolution experiments will be presented.

Genetic Analyses, Genetic Crosses and Screens

An organism&#8217;s physical traits, or phenotype, are a product of its genotype, which is the combination of alleles (gene variants) inherited from its ...

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Advanced Biology

Hardy-Weinberg and Genetic Drift

Evolutionary change is interesting and important to study, but changes in populations occur over long periods of time and in huge physical spaces and are therefore very difficult to measure. In general, studying phenomena like this requires the use of mathematical models which are built using parameters that can be conveniently measured. These models are then used to make predictions about how changes to the system might affect the outcome. 
For example, changes to the frequency of genetic alleles at individual loci in species are often observed over long time periods, but are not generally observable over short time periods. The use of computer models allows researchers to predict changes within a species&#8217; gene pool using observations that have already been gathered, and allow for simulations of a potentially unlimited number of generations of the population. This would certainly not be possible within a human lifetime.
<h4>The Hardy-Weinberg Equilibrium Principle</h4>
One equation used to model populations is the Hardy-Weinberg equilibrium equation. It was formulated independently in 1908 by both G. H. Hardy and Wilhelm Weinberg1,2. The simple equation describes the expected allele frequency of a population that is not evolving. Because most real-life populations are evolving in response to the forces of natural selection, this formula acts as a useful null hypothesis. It is used to test whether or not different types of selection are, or are not, occurring. If measured allele frequencies differ from those that were predicted using the Hardy-Weinberg equation, then the gene in question is undergoing evolutionary change.
The Hardy-Weinberg equation can be represented as p2 + 2pq + q2 = 1 where p represents the frequency of the dominant allele and q represents the frequency of the recessive allele for the gene in question. Each expression in the equation represents the predicted frequency of one of the three possible diploid genotypes. Homozygous dominant frequency is represented by p2, homozygous recessive by q2, and heterozygous by 2pq. If these frequencies are summed, it adds up to 1, which makes sense since the full population had been divided into the three available categories.
It is also possible to describe the population, or gene pool, in terms of the two alleles, without regard to how they are packaged into diploid individuals. This equation is represented as p + q = 1, that is, the frequencies of the dominant and recessive alleles must add up to 1 within the population. Again, this makes sense, since the model includes just two allele possibilities. Notice that the frequency of genotypes in the population is just the quadratic expansion of the frequency of alleles in the population because (p + q)2 = p2 + 2pq + q2.
The Hardy-Weinberg equation, like most other models, requires a series of assumptions. Usefully, if the real-life data differs from the predicted data, it is possible to hypothesize which of the starting assumptions was false. The assumptions of the Hardy-Weinberg equilibrium equations are: 1) the population is very large, 2) the population is closed, meaning that there are no individuals immigrating into or emigrating out of the population, 3) there are no mutations occurring on the gene in question, 4) individuals within the population are mating at random&#8212;individuals are not choosing their mates, 5) natural selection is not occurring. Again, keep in mind that the Hardy-Weinberg equilibrium equation is a null hypothesis. Frequently, real populations violate one or more of these assumptions. The equation is used to determine if and how evolution is happening.
<h4>Genetic Drift</h4>
In a relatively small population, a condition that violates the first Hardy-Weinberg assumption, it is possible for allele frequencies to have resulted from chance. This phenomenon is referred to as genetic drift. One version of this is referred to as the founder effect. If a small number of individuals move to an isolated location and start a new population, the specific genetics of those particular individuals will shape the future generations. This new small gene pool may have the same allele frequency as the original, but it is also possible, even likely, that it does not. Say the original population included 50% dominant alleles and 50% recessive. In the extreme, if all of the individuals in a migrating founder group are homozygous recessive, then the dominant allele has been lost completely and the recessive is now at 100%. A similar phenomenon, called the bottleneck effect, can occur if a population sustains a large drop in numbers due to a natural disaster, human intervention, or disease.
Importantly, genetic drift represents a type of evolution that is not necessarily adaptive. It does not specifically select for traits that are fit for the environment. Nonetheless, it is an important evolutionary force in shaping populations with a small number of individuals and will cause deviations from Hardy-Weinberg predictions. In populations that are larger, deviations from the predictions are more likely to mean that the population in question is undergoing evolution through natural selection. Under these circumstances, further consideration of the mechanisms causing this evolution can be explored.
Modern applications of the Hardy-Weinberg model include analysis of human and animal populations in terms of how their immune systems relate to their susceptibility to infectious disease3,4. Many research groups are cataloging genes which encode specific immune system molecules, such as CCR5 and the major histocompatibility complex (MHC, known in humans as the human leukocyte antigen (HLA), and correlating this information to epidemiological studies regarding disease and its progression3,5-6. From comparing these two types of data sets, patterns are emerging showing that some individuals are genetically resistant to particular infections while others are more likely to succumb to disease4. Hardy and Weinberg&#8217;s model has been essential to the analysis of this data and has contributed to our understanding of how infections have shaped evolution.
<h4>References</h4>
<ol class="references">
	<li>Hardy, G. H. (Jul 1908). 'Mendelian Proportions in a Mixed Population' (PDF). Science. 28 (706): 49&#8211;50. doi:10.1126/science.28.706.49. ISSN 0036-8075. PMID 17779291.</li>
	<li>Weinberg, W. (1908). '&#220;ber den Nachweis der Vererbung beim Menschen'. Jahreshefte des Vereins f&#252;r vaterl&#228;ndische Naturkunde in W&#252;rttemberg. 64: 368&#8211;382.</li>
	<li>Eguchi S, Matsuura M. Testing the Hardy-Weinberg equilibrium in the HLA system. Biometrics. 1990 Jun;46(2):415-26. PubMed PMID: 2364131.</li>
	<li>Phillips KP, Cable J, Mohammed RS, et al. Immunogenetic novelty confers a selective advantage in host-pathogen coevolution. Proc Natl Acad Sci U S A. 2018;115(7):1552-1557.</li>
	<li>Lim JK, Glass WG, McDermott DH, Murphy PM CCR5: no longer a 'good for nothing' gene--chemokine control of West Nile virus infection. Trends Immunol. 2006 Jul; 27(7):308-12.</li>
	<li><a target="__blank" href="https://hla-net.eu/tools/frequency-estimation/">https://hla-net.eu/tools/frequency-estimation/</a></li>
	<li>Santiago Rodriguez, Tom R. Gaunt and Ian N. M. Day. Hardy-Weinberg Equilibrium Testing of Biological Ascertainment for Mendelian Randomization Studies. American Journal of Epidemiology Advance Access published on January 6, 2009, DOI 10.1093/aje/kwn359. http://www.oege.org/software/hwe-mr-calc.html</li>
</ol>
<h4>Further Reading</h4>
Andrews, C. (2010) The Hardy-Weinberg Principle. Nature Education Knowledge 3(10):65 https://www.nature.com/scitable/knowledge/library/the-hardy-weinberg-principle-13235724

Hardy-Weinberg Equilibrium Principle and Genetic Drift - Concept

Evolutionary change is interesting and important to study, but changes in populations occur over long periods of time and in huge physical spaces and are ...

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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