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.

<table><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></tr><tr><td>Arabinose</td><td>Sigma-Aldrich</td><td>A3256</td><td></td></tr><tr><td>Freezer Box (2&quot;)</td><td>VWR</td><td>82007-142</td><td></td></tr><tr><td>Freezer Box (3&quot;)</td><td>VWR</td><td>82007-144</td><td></td></tr><tr><td>Freezer Box Cell Divider (49-place)</td><td>VWR</td><td>82007-150</td><td></td></tr><tr><td>Freezer Box Cell Divider (81-place)</td><td>VWR</td><td>82007-154</td><td></td></tr><tr><td>Freezer Vials (1/2-Dram)</td><td>VWR</td><td>&amp;nbsp;66009-816&amp;nbsp;</td><td></td></tr><tr><td>Freezer Vials (2-Dram)</td><td>VWR</td><td>&amp;nbsp;66010-560&amp;nbsp;</td><td></td></tr><tr><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></tr><tr><td>Magnesium Sulfate</td><td>Sigma-Aldrich</td><td>M7506</td><td></td></tr><tr><td>Metal Tray</td><td>Winco</td><td>SPJP-202</td><td></td></tr><tr><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></tr><tr><td>Thiamine, Hydrochloride</td><td>Millipore</td><td>5871</td><td></td></tr><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></tbody></table>

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

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JoVE Journal

Genetics

3.1K Views.  The University of Texas at Austin. In 1988 begon Richard Lenski met een evolutie-experiment. Hij vulde 12 Erlenmeyers met een glucosebeperkt gedefinieerd groeimedium dat bekend staat als DM25. Hij inoculeerde zes van deze kolven met een E.coli-stam die bekend staat als REL606 en die niet in staat is om de suiker arabinose als voedingsstof te gebruiken.Deze populaties werden aangeduid als A-1 tot en met A-6. Hij inoculeerde de andere zes met een bijna identieke stam die bekend staat als REL607 en die arabinose kan gebrui...

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) ...

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 ...

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

The Replica Set method is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus allowing a single investigator to screen more treatments or conditions over the same amount of time without loss of data quality. The method requires common equipment found in most laboratories working with C. elegans and is thus simple to adopt. The approach centers on assaying independent samples of a population at each observation point, rather than a single sample over time as with traditional longitudinal methods. Scoring entails adding liquid to the wells of a multi-well plate, which stimulates C. elegans to move and facilitates quantifying changes in healthspan. Other major benefits of the Replica Set method include reduced exposure of agar surfaces to airborne contaminants (e.g. mold or fungus), minimal handling of animals, and robustness to sporadic mis-scoring (such as calling an animal as dead when it is still alive). To appropriately analyze and visualize the data from a Replica Set style experiment, a custom software tool was also developed. Current capabilities of the software include plotting of survival curves for both Replica Set and traditional (Kaplan-Meier) experiments, as well as statistical analysis for Replica Set. The protocols provided here describe the traditional experimental approach and the Replica Set method, as well as an overview of the corresponding data analysis.

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Here we describe the Replica Set method, an approach to quantitatively measure C. elegans lifespan/survival and healthspan in a high-throughput and robust manner, thus allowing screening of many conditions without sacrificing data quality. This protocol details the strategy and provides a software tool for analysis of Replica Set data.

The Replica Set method is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus ...

Resurrection of Dormant Daphnia magna: Protocol and Applications

Long-term studies enable the identification of eco-evolutionary processes that occur over extended time periods. In addition, they provide key empirical data that may be used in predictive modelling to forecast evolutionary responses of natural ecosystems to future environmental changes. However, excluding a few exceptional cases, long-term studies are scarce because of logistic difficulties associated with accessing temporal samples. Temporal dynamics are frequently studied in the laboratory or in controlled mesocosm experiments with exceptional studies that reconstruct the evolution of natural populations in the wild.
Here, a standard operating procedure (SOP) is provided to revive or resurrect dormant Daphnia magna, a widespread zooplankton keystone species in aquatic ecosystems, to dramatically advance the state-of-the-art longitudinal data collection in natural systems. The field of Resurrection Ecology was defined in 1999 by Kerfoot and co-workers, even though the first attempts at hatching diapausing zooplankton eggs date back to the late 1980s. Since Kerfoot&#39;s seminal paper, the methodology of resurrecting zooplankton species has been increasingly frequently applied, though propagated among laboratories only via direct knowledge transfer. Here, an SOP is described that provides a step-by-step protocol on the practice of resurrecting dormant Daphnia magna eggs.
Two key studies are provided in which the fitness response of resurrected Daphnia magna populations to warming is measured, capitalizing on the ability to study historical and modern populations in the same settings. Finally, the application of next generation sequencing technologies to revived or still dormant stages is discussed. These technologies provide unprecedented power in dissecting the processes and mechanisms of evolution if applied to populations that have experienced changes in selection pressure over time.

Resurrection of Dormant Daphnia magna: Protocol and Applications

Long-term studies are essential to understanding the process of evolution and the mechanisms of adaptation. Generally, these studies require commitments beyond the life-time of researchers. Here, a powerful method is described that dramatically advances state-of-the-art data collection to generate longitudinal data in natural systems.

Long-term studies enable the identification of eco-evolutionary processes that occur over extended time periods. In addition, they provide key empirical ...

Environment

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 ...

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

The protocol developed here offers a tool to enable computer tracking of Escherichia coli division and fluorescent levels over several hours. The process starts by screening for colonies that survive on minimal media, assuming that only Escherichia coli harboring the correct plasmid will be able to thrive in the specific conditions. Since the process of building large genetic circuits, requiring the assembly of many DNA parts, is challenging, circuit components are often distributed between multiple plasmids at different copy numbers requiring the use of several antibiotics. Mutations in the plasmid can destroy transcription of the antibiotic resistance genes and interject with resources management in the cell leading to necrosis. The selected colony is set on a glass-bottom Petri dish and a few focus planes are selected for microscopy tracking in both bright field and fluorescent domains. The protocol maintains the image focus for more than 12 hours under initial conditions that cannot be regulated, creating a few difficulties. For example, dead cells start to accumulate in the lenses' field of focus after a few hours of imaging, which causes toxins to buildup and the signal to blur and decay. Depletion of nutrients introduces new metabolic processes and hinder the desired response of the circuit. The experiment's temperature lowers the effectivity of inducers and antibiotics, which can further damage the reliability of the signal. The minimal media gel shrinks and dries, and as a result the optical focus changes over time. We developed this method to overcome these challenges in Escherichia coli, similar to previous works developing analogous methods for other micro-organisms. In addition, this method offers an algorithm to quantify the total stochastic noise in unaltered and altered cells, finding that the results are consistent with flow analyzer predictions as shown by a similar coefficient of variation (CV).

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

This work presents a microscopy method that allows live imaging of a single cell of Escherichia coli for analysis and quantification of the stochastic behavior of synthetic gene circuits.

The protocol developed here offers a tool to enable computer tracking of Escherichia coli division and fluorescent levels over several hours. The process ...

Bioengineering

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Phenotypes are determined by a complex series of physical (e.g. protein-protein) and functional (e.g. gene-gene or genetic) interactions (GI)1. While physical interactions can indicate which bacterial proteins are associated as complexes, they do not necessarily reveal pathway-level functional relationships1. GI screens, in which the growth of double mutants bearing two deleted or inactivated genes is measured and compared to the corresponding single mutants, can illuminate epistatic dependencies between loci and hence provide a means to query and discover novel functional relationships2. Large-scale GI maps have been reported for eukaryotic organisms like yeast3-7, but GI information remains sparse for prokaryotes8, which hinders the functional annotation of bacterial genomes. To this end, we and others have developed high-throughput quantitative bacterial GI screening methods9, 10.
Here, we present the key steps required to perform quantitative E. coli Synthetic Genetic Array (eSGA) screening procedure on a genome-scale9, using natural bacterial conjugation and homologous recombination to systemically generate and measure the fitness of large numbers of double mutants in a colony array format. Briefly, a robot is used to transfer, through conjugation, chloramphenicol (Cm) - marked mutant alleles from engineered Hfr (High frequency of recombination) 'donor strains' into an ordered array of kanamycin (Kan) - marked F- recipient strains. Typically, we use loss-of-function single mutants bearing non-essential gene deletions (e.g. the 'Keio' collection11) and essential gene hypomorphic mutations (i.e. alleles conferring reduced protein expression, stability, or activity9, 12, 13) to query the functional associations of non-essential and essential genes, respectively. After conjugation and ensuing genetic exchange mediated by homologous recombination, the resulting double mutants are selected on solid medium containing both antibiotics. After outgrowth, the plates are digitally imaged and colony sizes are quantitatively scored using an in-house automated image processing system14. GIs are revealed when the growth rate of a double mutant is either significantly better or worse than expected9. Aggravating (or negative) GIs often result between loss-of-function mutations in pairs of genes from compensatory pathways that impinge on the same essential process2. Here, the loss of a single gene is buffered, such that either single mutant is viable. However, the loss of both pathways is deleterious and results in synthetic lethality or sickness (i.e. slow growth). Conversely, alleviating (or positive) interactions can occur between genes in the same pathway or protein complex2 as the deletion of either gene alone is often sufficient to perturb the normal function of the pathway or complex such that additional perturbations do not reduce activity, and hence growth, further. Overall, systematically identifying and analyzing GI networks can provide unbiased, global maps of the functional relationships between large numbers of genes, from which pathway-level information missed by other approaches can be inferred9.

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Systematic, large-scale synthetic genetic (gene-gene or epistasis) interaction screens can be used to explore genetic redundancy and pathway cross-talk. Here, we describe a high-throughput quantitative synthetic genetic array screening technology, termed eSGA that we developed for elucidating epistatic relationships and exploring genetic interaction networks in Escherichia coli.

Phenotypes  are determined by a complex series of physical (e.g. protein-protein) and  functional (e.g. gene-gene or genetic) interactions (GI)1. While ...

Biology

The Use of Chemostats in Microbial Systems Biology

Cells regulate their rate of growth in response to signals from the external world. As the cell grows, diverse cellular processes must be coordinated including macromolecular synthesis, metabolism and ultimately, commitment to the cell division cycle. The chemostat, a method of experimentally controlling cell growth rate, provides a powerful means of systematically studying how growth rate impacts cellular processes - including gene expression and metabolism - and the regulatory networks that control the rate of cell growth. When maintained for hundreds of generations chemostats can be used to study adaptive evolution of microbes in environmental conditions that limit cell growth. We describe the principle of chemostat cultures, demonstrate their operation and provide examples of their various applications. Following a period of disuse after their introduction in the middle of the twentieth century, the convergence of genome-scale methodologies with a renewed interest in the regulation of cell growth and the molecular basis of adaptive evolution is stimulating a renaissance in the use of chemostats in biological research.

Cell growth rate is a regulated process and a primary determinant of cell physiology. Continuous culturing using chemostats enables extrinsic control of cell growth rate by nutrient limitation facilitating the study of molecular networks that control cell growth and how those networks evolve to optimize cell growth.

Cells regulate their rate of growth in response to signals from the external world. As the cell grows, diverse cellular processes must be coordinated ...

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.

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 ...

Experimental Evolution Protocol for Thermophile Adaptation Using Low-Cost Thermomixers

Adaptation at the Extremes of Life: Experimental Evolution with the Extremophile Archaeon Sulfolobus acidocaldarius

The archaeon Sulfolobus acidocaldarius has emerged as a promising thermophilic model system. Investigating how thermophiles adapt to changing temperatures is a key requirement, not only for understanding fundamental evolutionary processes but also for developing S. acidocaldarius as a chassis for bioengineering. One major obstacle to conducting experimental evolution with thermophiles is the expense of equipment maintenance and energy usage of traditional incubators for high-temperature growth. To address this challenge, a comprehensive experimental protocol for conducting experimental evolution in S. acidocaldarius is presented, utilizing low-cost and energy-efficient bench-top thermomixers. The protocol involves a batch culture technique with relatively small volumes (1.5 mL), enabling tracking of adaptation in multiple independent lineages. This method is easily scalable through the use of additional thermomixers. Such an approach increases the accessibility of S. acidocaldarius as a model system by reducing both initial investment and ongoing costs associated with experimental investigations. Moreover, the technique is transferable to other microbial systems for exploring adaptation to diverse environmental conditions.

Author Spotlight: Understanding Microbe Adaptation Using Innovative Techniques for Exploring Thermophilic Evolution

Here, we present an experimental evolution protocol for adaptation in thermophiles utilizing low-cost, energy-efficient bench-top thermomixers as incubators. The technique is demonstrated through the characterization of temperature adaptation in Sulfolobus acidocaldarius, an archaeon with an optimal growth temperature of 75 &#176;C.

The archaeon Sulfolobus acidocaldarius has emerged as a promising thermophilic model system. Investigating how thermophiles adapt to changing temperatures ...

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

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