We thank Richard Lenski and the many researchers who have studied and contributed to maintaining the Long-Term Evolution Experiment with E. coli, including especially Neerja Hajela. The LTEE is currently supported by the National Science Foundation (DEB-1951307).

No conflicts of interest declared.

Long-term resilience of the LTEE and its methods 
The E. coli Long-Term Evolution Experiment (LTEE) is now in its fourth decade. For a microbial evolution experiment of any duration, it is critical to maintain a reproducible environment, avoid contamination, archive samples, and accurately measure fitness. The LTEE demonstrates several time-tested strategies for achieving these objectives, including the use of well-shaken flasks that create a homogenous environment and a chemically defined growth medium that supports a low cell density. Moreover, the LTEE employs ancestor strains that differ in a genetic marker that gives a phenotype (colony color) that is both easily screened and selectively neutral in the evolution environment. This experimental design feature provides a means of identifying internal and external contamination and facilitates measuring fitness. However, not all of the procedures and safeguards that have been used by the LTEE since 1988 have proven equally robust. Some methods that were reliable when the LTEE began have become less effective as the E. coli populations have evolved. Fortunately, these problematic methods can now be augmented or replaced using technologies developed since the experiment&#39;s inception.
Detecting contamination 
Detection of contamination is critical to the LTEE. Contamination can be of two sorts: between LTEE populations (cross-contamination) and with microbes from the environment (outside contamination). For the most part, careful use of aseptic techniques and close attention during media preparation and the daily transfers prevent both types of contamination, but they do happen. Early in the experiment, plating on TA agar could be used to detect instances of cross-contamination because transfers have always alternated between Ara&#8722; and Ara+ populations. The fingerprint of sensitivity and resistance of these E. coli to certain bacteriophages was also intended to be a design feature that could differentiate the LTEE populations from commonly used E. coli laboratory strains that might contaminate them4. However, these genetic markers have become unreliable as the experiment has progressed (e.g., some populations no longer form colonies on TA agar)10,35. Fortunately, the populations have genetically diverged as they have experienced separate evolutionary histories during the experiment, which has created new genetic markers that can now be used to detect cross-contamination. For instance, each population has evolved a unique combination of mutations in the pykF and nadR genes14,36,37. We sometimes PCR amplify and Sanger sequence these two genes to test whether colonies with unusual morphologies or colors are due to cross-contamination. As the costs of whole-genome and whole-population sequencing continue to drop, routine sequencing of the LTEE populations may soon be possible, thereby presenting new opportunities to monitor them for signs of contamination.
Measuring competitive fitness 
Another case in which the LTEE has outgrown its original methods is that the fitness of the evolved E. coli has increased in the experimental environment to such a degree that one can no longer directly measure the fitness of the populations of today relative to their ancestors using the protocol described here. The evolved populations outcompete the ancestors to such an extent that few to no ancestor colonies remain to count after a one-day competition. One approach for dealing with this large fitness difference is to use unequal starting ratios of the strains, weighting the initial volumes that are mixed toward the less-fit competitor (e.g., 90 &#181;L ancestor and 10 &#181;L evolved competitor). A second approach is to identify an evolved Ara&#8722; clone that has a higher fitness than the LTEE ancestor, isolate a spontaneous Ara+ revertant mutant of it by selection on MA agar, and then verify that the revertant strain has the same fitness as its parent using a competition assay6,38. This new Ara&#8722;/Ara+ pair can then be used as a set of common competitor strains in lieu of REL606/REL607. Ideally, the evolved Ara&#8722; clone chosen as a common competitor (and its Ara+ revertant) will have intermediate fitness relative to all strains of interest in an experiment. Over the first 50,000 generations of the LTEE, these two approaches (using unequal starting ratios or a common competitor) did not produce meaningfully different fitness measurements versus the typical approach39.
These modifications to the competition protocol make certain simplifying assumptions that may not always be true. One is that fitness measurements are transitive. That is, if we compete two populations each versus a common competitor strain separately, then we can infer the relative fitness of the two populations to one another. This relationship has been found to be true for the LTEE40, for the most part, but it is not for other experiments41. One reason for this discrepancy can be the evolution of negative frequency-dependent fitness effects. This situation occurs when strains isolated from two different diverged lineages from population A&#8722;2 of the LTEE are competed against one another19,42. Each has an advantage when rare, due to cross-feeding, which stabilizes their co-existence. Sequencing data showing long-term co-existence of lineages with different sets of mutations suggests that similar interactions may also have arisen in other LTEE populations14,43, though it is not clear whether they are strong enough to noticeably alter fitness estimates. Finally, the evolution of aerobic growth on citrate in population A&#8722;3 of the LTEE32 means that the fitness of these cells now incorporates the use of a &#34;private&#34; resource when they are competed against cells that cannot use citrate, which complicates interpreting these results. Despite these exceptions, the use of a low glucose concentration and well-shaken environment has undoubtedly simplified making fitness comparisons between LTEE strains and populations.
At later generations, some of the LTEE populations no longer form colonies on TA agar, which makes performing competition experiments using even modified protocols difficult or impossible10. Alternative methods that do not require colony growth can potentially be used to determine the relative representation of two competitors, such as FREQ-seq which uses next-generation sequencing to count the proportion of reads containing two alternative alleles in an amplicon44. This method or a similar one could potentially be used with the Ara alleles or with newly evolved mutations, such as those in pykF and nadR, versus the ancestral sequence. Performing genetic modifications that introduce other types of neutral markers can also be used to measure relative fitness. For example, fluorescent protein genes have been inserted into the chromosomes of cells in LTEE offshoot experiments so that competitors can be counted using flow cytometry45. Another approach, which opens up the possibility of mixing together more than two strains in the same competition flask, is to insert barcodes that can be PCR amplified and sequenced into the genomes of different competitors. This approach has been used for lineage tracing in evolution experiments46. Both flow cytometry and barcode sequencing can accurately measure much more extreme ratios of two strains versus colony counting (because they can query &#62; 10,000 cells/genomes versus the &#60; 500 that can be counted on an agar plate), so using these methods also promises to increase the dynamic range in terms of fitness differences that can be measured relative to a common competitor.
Alternative designs for long-term microbial evolution experiments 
For all its virtues, the LTEE is not perfect. Certain aspects of its design make it labor intensive and susceptible to human error. For instance, each day a researcher must come into lab and pipette between Erlenmeyer flasks to continue the experiment. Competition experiments can also pose daunting logistical hurdles, given that requirements for sterile glassware, media, incubator space, and colony counting rapidly escalate when even a small number of competitors are being tested with modest replication. We are often asked why we don&#39;t take advantage of laboratory automation systems, such as pipetting robots that operate on 96-well microplates, or continuous culture systems, such as chemostats or turbidostats. The answer is simple: the LTEE is, in a sense, a prisoner of its own long history. We dare not deviate from 10 mL&#160;cultures shaking at a specific speed in 50 mL Erlenmeyer flasks because this would risk fundamentally changing the experiment. Subtle aspects of the environment to which these populations have been adapting for decades (e.g., the amount of aeration), would be altered in microplates or continuous culture systems. The population bottleneck at each transfer might also be different (smaller in microplates, for example), changing the evolutionary dynamics. In short, deviating from the methods described here would make the LTEE a different experiment, or at the very least risk introducing a discontinuity that would disrupt evolutionary trajectories.
Researchers designing new evolution experiments should consider these other ways of propagating microbial populations, while being aware of their potential benefits and drawbacks. Using pipetting robots to transfer populations in microwell plates is logistically simpler in some ways and can prove quite powerful due to the high numbers of replicate populations that can be propagated in this way47,48,49. However, automated transfers in most current setups do not take place under completely sterile conditions, which increases the likelihood of outside contamination. To prevent contamination, the growth medium is often supplemented with antibiotics, which become a feature of the environment that affects evolution. Transfers in microwell plates are also more prone to cross-contamination events. Finally, the environment of microwell plates&#160;&#8212; particularly if they are not shaken&#160;&#8212; tends to select for wall growth, aggregation, and other phenomena that can complicate evolution by creating multiple niches in one well. Using rich media or high concentrations of nutrients to keep population sizes large in small wells is likely to exacerbate these complexities. If such interactions arise, they can make measuring and interpreting fitness much more difficult.
Continuous culture systems for microbial evolution include chemostats, in which fresh medium&#160;is constantly pumped in and culture is pumped out, and turbidostats, in which cultures are periodically diluted through automated sensing and pumping to maintain cells in a state of constant growth. These systems are very useful when one wants to model microbial physiology and evolution because they avoid having microbes transition between growth and starvation by keeping them in an environment that always has nutrients50. One can even add sensors that make real-time measurements of optical density, O2 consumption, pH, and other aspects of a culture&#39;s environment and growth. However, current continuous culture systems either require expensive equipment purchases or specialized expertise to build custom setups51,52,53,54. Also, wall growth, in which cells escape dilution by adhering to the culture chamber, bedevils evolutionary dynamics in continuous culture systems unless they are periodically sterilized. Due to these constraints, most chemostat and turbidostat evolution experiments to date have been of limited duration and/or involved relatively few independently evolving populations compared to serial transfer evolution experiments.
Conclusion 
The methods we demonstrate here for the LTEE are critical for studying its unique historical record and continuing the open-ended evolution of these E. coli populations. They also provide a starting point for others who are considering new evolution experiments that may take advantage of laboratory automation or add back various elements of the complexity found in natural environments that were purposefully omitted from the LTEE. Since 1988, experimental evolution has flourished as a field. During this time, researchers in laboratories across the globe have demonstrated the immense flexibility of this approach for studying evolution, innovating by introducing creative experimental designs and monitoring the results using new technologies. The methods of the LTEE do not represent an endpoint, but we hope they will continue to inspire and provide a foundation for the field far into the future.

In February of 1988, Richard Lenski inoculated twelve flasks containing a defined glucose-limited growth medium with clonal cultures of Escherichia coli at the University of California, Irvine1. The following day, he transferred 1% of the culture from each flask to a set of new flasks containing fresh growth medium. This 1:100 dilution allowed the bacterial populations to expand 100-fold before exhausting the available glucose, corresponding to roughly 6&#8532; generations of cell divisions. This procedure was repeated the following day and has been every day since, with a few interruptions. These daily transfers have continued, even as the experiment was relocated, first to Michigan State University in 1992, and then to The University of Texas at Austin in 2022. All the while, new mutations have continuously generated genetic variation in these E. coli populations and natural selection has led to evolved cells outcompeting their ancestors.
Lenski designed this experiment, now known as the Long-Term Evolution Experiment (LTEE), to investigate the dynamics and repeatability of evolution. In order to answer these questions, he included several important features in the design of the experimental setup and its protocols2. One of these features was the careful choice of a model organism. The original twelve populations were all started from single colonies that shared an immediate common ancestor, Escherichia coli B strain REL606. This strain was chosen because it had already been commonly used in lab settings, reproduced completely asexually, and contained no plasmids or intact prophages3,4 &#8212; all of which make studying its evolution simpler. Another choice that simplified the experiment was to use a very low concentration of glucose in the growth medium to limit the density of cells in each flask after growth. Using a low cell density was intended to make it easier to analyze changes in population fitness by reducing the potential for the evolution of ecological interactions within populations (e.g., by cross-feeding)5.
REL606 is unable to use &#671;-arabinose as a carbon and energy source (Ara&#8722;) due to a point mutation in the araA gene. Prior to starting the LTEE, a spontaneous mutant with a restored araA sequence, designated REL607, was isolated from REL6066. REL607 is capable of growing on &#671;-arabinose (Ara+). REL606 was used to start six of the LTEE populations, and REL607 was used to start the other six. Arabinose is not present in the growth medium used during the LTEE, so REL607 behaves the same as REL606 under these conditions. However, when plated on tetrazolium arabinose (TA) agar, Ara&#8722; and Ara+ cells form red and white colonies, respectively. This method for discriminating between the two ancestral E. coli strains and their descendants is quite useful. It can be used to detect cross-contamination between LTEE populations. It also aids in measuring the fitness of an Ara&#8722; strain or population relative to an Ara+ one when they are competed against one another. Fitness is measured by setting up a co-culture of oppositely marked competitors and then monitoring how the frequencies of red and white colonies (obtained by spreading dilutions of the culture on TA plates) change between when the competitors are initially mixed and after one or more growth cycles under the same conditions as the LTEE. The representation of the more-fit cell type will increase during each growth cycle.
Another critical feature of the LTEE is that samples of the evolving populations are periodically archived. When mixed with a cryoprotectant such as glycerol, E. coli cells can be frozen and later revived7. As part of the LTEE protocol, every 75th day (which equates to roughly 500 generations), a portion of each population that was not transferred to a new flask is mixed with glycerol, split up between multiple vials, and stored in a freezer. This frozen &#34;fossil record&#34; enabled researchers to perform the first studies of the LTEE, in which they revived the evolved E. coli populations from various timepoints and competed them versus the ancestral strains to track how rapidly fitness was increasing1. Fitness evolution has been remeasured periodically as more &#34;strata&#34; of the frozen &#34;fossil record&#34; have been preserved. The overall conclusion from these measurements is that fitness continues to improve in the LTEE to this day, even after so many generations of evolution in the same environment8,9,10.
What has allowed the LTEE to continue for so long? Many of the same features that enabled its original questions to be asked and answered have also served as safety measures and fail-safes against inevitable disruptions due to bad luck, human error, and world events. Every day, when the cultures are transferred to&#160;fresh growth medium, the researcher performing the transfers alternates between Ara&#8722; and Ara+ populations. Then, when the populations are frozen, they can be plated on selective and indicator agar to check if any &#34;neighboring&#34; populations have been accidentally cross-contaminated or mixed up (e.g., white colonies are in a population that should only form red colonies) or contaminated with foreign microbes (e.g., unexpected colony morphologies or cell densities). In the event that a population has been compromised, its progenitor can be revived from the freezer and carried forward in its place. The Ara markers and the frozen archive thus serve dual purposes as both experimental resources and safety measures.
Because its history is so well preserved and easily accessible, LTEE samples have been studied using technologies that did not exist when the experiment began. For example, whole-genome sequencing has been used to examine the dynamics of mutations in the LTEE populations11,12,13,14,15, and transcriptomics and ribosomal profiling have been used to examine changes in gene expression16,17. Genetic tools have been used to reconstruct strains that differ by single mutations or combinations of several evolved mutations to understand their effects on fitness and various phenotypes18,19,20,21. Samples from the frozen &#34;fossil record&#34; are easily replenished so that parts of or entire copies of the experiment&#39;s history can be shipped to other laboratories. LTEE samples now exist on all continents except Antarctica, and they are being studied by researchers who are younger than the experiment itself. The robust methods of the LTEE and evolved E. coli samples and strains from its historical record have also served as starting points for evolution experiments examining other questions and environments22,23,24,25,26,27,28,29.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65342/65342fig01v2.jpg" /> 
Figure 1: Overview of LTEE procedures. <a href="https://www.jove.com/files/ftp_upload/65342/65342fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Here, we demonstrate three core protocols used in the E. coli Long-Term Evolution Experiment (Figure 1). We describe: (1) how to perform the daily transfers, (2) how to archive population samples and clonal isolates, and (3) how to perform and analyze co-culture competition assays to measure fitness differences. Our hope is that these protocols foster the continued use of LTEE resources and inform the design of new microbial evolution experiments.

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	<tbody>
		<tr>
			<td>2,3,5-Triphenyltetrazolium chloride (TTC)</td>
			<td>Sigma-Aldrich</td>
			<td>T8877</td>
			<td></td>
		</tr>
		<tr>
			<td>20 mL Glass Beaker</td>
			<td>Sigma-Aldrich</td>
			<td>CLS100020</td>
			<td></td>
		</tr>
		<tr>
			<td>50 mL Erlenmeyer Flasks</td>
			<td>Sigma-Aldrich</td>
			<td>CLS498050</td>
			<td></td>
		</tr>
		<tr>
			<td>Agar</td>
			<td>Sigma-Aldrich</td>
			<td>A1296</td>
			<td></td>
		</tr>
		<tr>
			<td>Ammonium Sulfate</td>
			<td>Sigma-Aldrich</td>
			<td>AX1385</td>
			<td></td>
		</tr>
		<tr>
			<td>Antifoam</td>
			<td>Sigma-Aldrich</td>
			<td>A5757</td>
			<td></td>
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		<tr>
			<td>Arabinose</td>
			<td>Sigma-Aldrich</td>
			<td>A3256</td>
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			<td>Freezer Box (2&#34;)</td>
			<td>VWR</td>
			<td>82007-142</td>
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			<td>Freezer Box (3&#34;)</td>
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			<td>82007-144</td>
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			<td>Freezer Box Cell Divider (49-place)</td>
			<td>VWR</td>
			<td>82007-150</td>
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		<tr>
			<td>Freezer Box Cell Divider (81-place)</td>
			<td>VWR</td>
			<td>82007-154</td>
			<td></td>
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		<tr>
			<td>Freezer Vials (1/2-Dram)</td>
			<td>VWR</td>
			<td>&#160;66009-816&#160;</td>
			<td></td>
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			<td>Freezer Vials (2-Dram)</td>
			<td>VWR</td>
			<td>&#160;66010-560&#160;</td>
			<td></td>
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			<td>Glucose</td>
			<td>Sigma-Aldrich</td>
			<td>G8270</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>Fisher Scientific</td>
			<td>G33</td>
			<td></td>
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		<tr>
			<td>Magnesium Sulfate</td>
			<td>Sigma-Aldrich</td>
			<td>M7506</td>
			<td></td>
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		<tr>
			<td>Metal Tray</td>
			<td>Winco</td>
			<td>SPJP-202</td>
			<td></td>
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			<td>Petri Dish</td>
			<td>Fisher Scientific</td>
			<td>FB0875712</td>
			<td></td>
		</tr>
		<tr>
			<td>Potassium Phosphate Dibasic Trihydrate</td>
			<td>Sigma-Aldrich</td>
			<td>P5504</td>
			<td></td>
		</tr>
		<tr>
			<td>Potassium Phosphate Monobasic</td>
			<td>Sigma-Aldrich</td>
			<td>P5379</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium Chloride</td>
			<td>Sigma-Aldrich</td>
			<td>M7506</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium Citrate Tribasic Dihydrate</td>
			<td>Sigma-Aldrich</td>
			<td>C7254</td>
			<td></td>
		</tr>
		<tr>
			<td>Test Tube Cap (18mm)</td>
			<td>VWR</td>
			<td>10200-142</td>
			<td></td>
		</tr>
		<tr>
			<td>Test Tube Rack (18mm, steel)</td>
			<td>Adamas-Beta</td>
			<td>N/A</td>
			<td>Test Tube Racks Stainless Steel Grid Arrangement 72 Holes (17-19 mm)</td>
		</tr>
		<tr>
			<td>Test Tubes (18 x 150 mm)</td>
			<td>VWR</td>
			<td>47729-583</td>
			<td></td>
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		<tr>
			<td>Thiamine, Hydrochloride</td>
			<td>Millipore</td>
			<td>5871</td>
			<td></td>
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		<tr>
			<td>Tryptone</td>
			<td>Gibco</td>
			<td>211705</td>
			<td></td>
		</tr>
		<tr>
			<td>Yeast Extract</td>
			<td>Gibco</td>
			<td>212750</td>
			<td></td>
		</tr>
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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.

Watch this Scientific Journal Video about Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli at JoVE.com

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.

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 ...

daily-transfers-archiving-populations-measuring-fitness-long-term

Research

JoVE Journal

Genetics

2.9K Views.  The University of Texas at Austin. 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. 

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

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) ...

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Microbes provide an intriguing system to study social interaction among individuals within a population. The short generation times and relatively simple genetic modification procedures of microbes facilitate the development of the sociomicrobiology field. To assess the fitness of certain microbial species, selected strains or their genetically modified derivatives within one population, can be fluorescently labelled and tracked using microscopy adapted with appropriate fluorescence filters. Expanding colonies of diverse microbial species on agar media can be used to monitor the spatial distribution of cells producing distinctive fluorescent proteins.
Here, we present a detailed protocol for the use of green- and red-fluorescent protein producing bacterial strains to follow spatial arrangement during surface colonization, including flagellum-driven community movement (swarming), exopolysaccharide- and hydrophobin-dependent growth mediated spreading (sliding), and complex colony biofilm formation. Non-domesticated isolates of the Gram-positive bacterium, Bacillus subtilis can be utilized to scrutinize certain surface spreading traits and their effect on two-dimensional distribution on the agar-solidified medium. By altering the number of cells used to initiate colony biofilms, the assortment levels can be varied on a continuous scale. Time-lapse fluorescent microscopy can be used to witness the interaction between different phenotypes and genotypes at a certain assortment level and to determine the relative success of either.

The role of assortment (spatial segregation) in evolutionary scenarios can be examined using simple microbial systems in the laboratory that allow controlled adjustment of spatial distribution. By modifying the founder cell density, various assortment levels can be visualized using fluorescently labelled bacterial strains in the colony biofilms of Bacillus subtilis.

Microbes provide an intriguing system to study social interaction among individuals within a population. The short generation times and relatively simple ...

Determination of the Optimal Chromosomal Location(s) for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In bacteria, the impact of the genetic context on the function of a genetic element can be difficult to assess. Several mechanisms, including topological effects, transcriptional interference from neighboring genes, and/or replication-associated gene dosage, may affect the function of a given genetic element. Here, we describe a method that permits the random integration of a DNA element into the chromosome of Escherichia coli and select the most favorable locations using a simple growth competition experiment. The method takes advantage of a well-described transposon-based system of random insertion, coupled with a selection of the fittest clone(s) by growth advantage, a procedure that is easily adjustable to experimental needs. The nature of the fittest clone(s) can be determined by whole-genome sequencing on a complex multi-clonal population or by easy gene walking for the rapid identification of selected clones. Here, the non-coding DNA region DARS2, which controls the initiation of chromosome replication in E. coli, was used as an example. The function of DARS2 is known to be affected by replication-associated gene dosage; the closer DARS2 gets to the origin of DNA replication, the more active it becomes. DARS2 was randomly inserted into the chromosome of a DARS2-deleted strain. The resultant clones containing individual insertions were pooled and competed against one another for hundreds of generations. Finally, the fittest clones were characterized and found to contain DARS2 inserted in close proximity to the original DARS2 location.

Determination of the Optimal Chromosomal Locations for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach

Here, the power of a transposon-mediated random insertion of a non-coding DNA element was used to resolve its optimal chromosomal position.

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In ...

Measuring Gene Expression in Bombarded Barley Aleurone Layers with Increased Throughput

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for germination or early seedling development are activated by gibberellin (GA), while genes associated with stress responses are activated by abscisic acid (ABA). The mechanisms of GA and ABA signaling can be interrogated by introducing reporter gene constructs into aleurone cells via particle bombardment, with the resulting transient expression measured using enzyme assays. An improved protocol is reported that partially automates and streamlines the grain homogenization step and the enzyme assays, allowing significantly more throughput than existing methods. Homogenization of the grain samples is carried out using an automated tissue homogenizer, and GUS (&#946;-glucuronidase) assays are carried out using a 96-well plate system. Representative results using the protocol suggest that phospholipase D activity may play an important role in the activation of HVA1 gene expression by ABA, through the transcription factor TaABF1.

An improved protocol is presented for the measurement of transient gene expression from reporter constructs in barley aleurone cells after particle bombardment. The combination of automated grain grinding with 96-well plate enzyme assays provides high throughput for the procedure.

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for ...

Testing the Role of Multicopy Plasmids in the Evolution of Antibiotic Resistance

Multicopy plasmids are extremely abundant in prokaryotes but their role in bacterial evolution remains poorly understood. We recently showed that the increase in gene copy number per cell provided by multicopy plasmids could accelerate the evolution of plasmid-encoded genes. In this work, we present an experimental system to test the ability of multicopy plasmids to promote gene evolution. Using simple molecular biology methods, we constructed a model system where an antibiotic resistance gene can be inserted into Escherichia coli MG1655, either in the chromosome or on a multicopy plasmid. We use an experimental evolution approach to propagate the different strains under increasing concentrations of antibiotics and we measure survival of bacterial populations over time. The choice of the antibiotic molecule and the resistance gene is so that the gene can only confer resistance through the acquisition of mutations. This &#34;evolutionary rescue&#34; approach provides a simple method to test the potential of multicopy plasmids to promote the acquisition of antibiotic resistance. In the next step of the experimental system, the molecular bases of antibiotic resistance are characterized. To identify mutations responsible for the acquisition of antibiotic resistance we use deep DNA sequencing of samples obtained from whole populations and clones. Finally, to confirm the role of the mutations in the gene under study, we reconstruct them in the parental background and test the resistance phenotype of the resulting strains.

Here we present an experimental method to test the role of multicopy plasmids in the evolution of antibiotic resistance.

Multicopy plasmids are extremely abundant in prokaryotes but their role in bacterial evolution remains poorly understood. We recently showed that the ...

Producing Gene Deletions in Escherichia coli by P1 Transduction with Excisable Antibiotic Resistance Cassettes

A first approach to study the function of an unknown gene in bacteria is to create a knock-out of this gene. Here, we describe a robust and fast protocol for transferring gene deletion mutations from one Escherichia coli strain to another by using generalized transduction with the bacteriophage P1. This method requires that the mutation be selectable (e.g., based on gene disruptions using antibiotic cassette insertions). Such antibiotic cassettes can be mobilized from a donor strain and introduced into a recipient strain of interest to quickly and easily generate a gene deletion mutant. The antibiotic cassette can be designed to include flippase recognition sites that allow the excision of the cassette by a site-specific recombinase to produce a clean knock-out with only a ~100-base-pair-long scar sequence in the genome. We demonstrate the protocol by knocking out the tamA gene encoding an assembly factor involved in autotransporter biogenesis and test the effect of this knock-out on the biogenesis and function of two trimeric autotransporter adhesins. Though gene deletion by P1 transduction has its limitations, the ease and speed of its implementation make it an attractive alternative to other methods of gene deletion.

Producing Gene Deletions in Escherichia coli by P1 Transduction with Excisable Antibiotic Resistance Cassettes

Here we present a protocol for the use of pre-existing antibiotic resistance-cassette deletion constructs as a basis for making deletion mutants in other E. coli strains. Such deletion mutations can be mobilized and inserted into the corresponding locus of a recipient strain using P1 bacteriophage transduction.

A first approach to study the function of an unknown gene in bacteria is to create a knock-out of this gene. Here, we describe a robust and fast protocol ...

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

A competitive index is a common method used to assess bacterial fitness and/or virulence. The utility of this approach is exemplified by its ease to perform and its ability to standardize the fitness of many strains to a wild-type organism. The technique is limited, however, by available phenotypic markers and the number of strains that can be assessed simultaneously, creating the need for a great number of replicate experiments. Concurrent with large numbers of experiments, the labor and material costs for quantifying bacteria based on phenotypic markers are not insignificant. To overcome these negative aspects while retaining the positive aspects, we have developed a molecular-based approach to directly quantify microorganisms after engineering genetic markers onto bacterial chromosomes. Unique, 25 base pair DNA barcodes were inserted at an innocuous locus on the chromosome of wild-type and mutant strains of Salmonella. In vitro competition experiments were performed using inocula consisting of pooled strains. Following the competition, the absolute numbers of each strain were quantified using digital PCR and the competitive indices for each strain were calculated from those values. Our data indicate that this approach to quantifying Salmonella is extremely sensitive, accurate, and precise for detecting both highly abundant (high fitness) and rare (low fitness) microorganisms. Additionally, this technique is easily adaptable to nearly any organism with chromosomes capable of modification, as well as to various experimental designs that require absolute quantification of microorganisms.

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

This molecular-based approach for determining bacterial fitness facilitates precise and accurate detection of microorganisms using unique genomic DNA barcodes that are quantified via digital PCR. The protocol describes calculating the competitive index for Salmonella strains; however, the technology is readily adaptable to protocols requiring absolute quantification of any genetically-malleable organism.

A competitive index is a common method used to assess bacterial fitness and/or virulence. The utility of this approach is exemplified by its ease to ...

A FACS-based Protocol to Isolate RNA from the Secondary Cells of Drosophila Male Accessory Glands

To understand the function of an organ, it is often useful to understand the role of its constituent cell populations. Unfortunately, the rarity of individual cell populations often makes it difficult to obtain enough material for molecular studies. For example, the accessory gland of the Drosophila male reproductive system contains two distinct secretory cell types. The main cells make up 96% of the secretory cells of the gland, while the secondary cells (SC) make up the remaining 4% of cells (about 80 cells per male). Although both cell types produce important components of the seminal fluid, only a few genes are known to be specific to the SCs. The rarity of SCs has, thus far, hindered transcriptomic analysis study of this important cell type. Here, a method is presented that allows for the purification of SCs for RNA extraction and sequencing. The protocol consists in first dissecting glands from flies expressing a SC-specific GFP reporter and then subjecting these glands to protease digestion and mechanical dissociation to obtain individual cells. Following these steps, individual, living, GFP-marked cells are sorted using a fluorescent activated cell sorter (FACS) for RNA purification. This procedure yields SC-specific RNAs from ~40 males per condition for downstream RT-qPCR and/or RNA sequencing in the course of one day. The rapidity and simplicity of the procedure allows for the transcriptomes of many different flies, from different genotypes or environmental conditions, to be determined in a short period of time.

A FACS-based Protocol to Isolate RNA from the Secondary Cells of Drosophila Male Accessory Glands

Here, we present a protocol to dissociate and sort a specific cell population from the Drosophila male accessory glands (secondary cells) for RNA sequencing and RT-qPCR. Cell isolation is accomplished through FACS purification of GFP-expressing secondary cells after a multistep-dissociation process requiring dissection, proteases digestion and mechanical dispersion.

To understand the function of an organ, it is often useful to understand the role of its constituent cell populations. Unfortunately, the rarity of ...

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 ...

Quantification of Plasmid-Mediated Antibiotic Resistance in an Experimental Evolution Approach

Plasmids play a major role in microbial ecology and evolution as vehicles of lateral gene transfer and reservoirs of accessory gene functions in microbial populations. This is especially the case under rapidly changing environments such as fluctuating antibiotics exposure. We recently showed that plasmids maintain antibiotic resistance genes in Escherichia coli without positive selection for the plasmid presence. Here we describe an experimental system that allows following both the plasmid genotype and phenotype in long-term evolution experiments. We use molecular techniques to design a model plasmid that is subsequently introduced to an experimental evolution batch system approach in an E. coli host. We follow the plasmid frequency over time by applying replica plating of the E. coli populations while quantifying the antibiotic resistance persistence. In addition, we monitor the conformation of plasmids in host cells by analyzing the extent of plasmid multimer formation by plasmid nicking and agarose gel electrophoresis. Such an approach allows us to visualize not only the genome size of evolving plasmids but also their topological conformation-a factor highly important for plasmid inheritance. Our system combines molecular strategies with traditional microbiology approaches and provides a set-up to follow plasmids in bacterial populations over a long time. The presented approach can be applied to study a wide range of mobile genetic elements in the future.

Our experimental approach provides a strategy to follow plasmid abundance and antibiotic resistance over time in bacterial populations.

Plasmids play a major role in microbial ecology and evolution as vehicles of lateral gene transfer and reservoirs of accessory gene functions in microbial ...