This work was supported by a startup grant from the Department of Pathology, University of Utah to J.C.S.B.

1. Identifying Synergy Prediction Mutants from Chemical-genetics Dataset by the Overlap2 Method (O2M)
NOTE: This is the method for identifying synergy prediction mutants using the published dataset from Nichols et al.17 in E. coli. However, this can be done on any chemical-genetics dataset and microorganism. These data sets contain a library of knockout mutants grown in the presence of more than 100 small molecules, giving a quantitative growth score for each mutant in each small molecule. One synergistic pair must be known, and there should be growth scores for both small molecules included in the dataset.
<ol>
	<li>Calculate the mean growth score and standard deviation for all mutants grown in the presence of a single concentration of each small molecule. 
	NOTE: This can be done using any spreadsheet software. The Brown lab uses Excel and the functions =AVERAGE(B3:B3981) and =STDEV(B3:B3981) for the calculations of each column of small molecule.
	<ol>
		<li>Calculate the means and standard deviations from each column of small molecule, though this orientation may differ if using a different dataset.</li>
	</ol>
	</li>
	<li>Calculate Z scores for each concentration of small molecule using the same dataset from Nichols et al.17 in a spreadsheet program. For this, negative z scores will be Z(2.5) = mean -2.5*standard deviation, and positive z scores will be Z(2.5) = mean + 2.5*standard deviation.</li>
	<li>Determine which gene mutants contain significant Z scores in the small molecules that make up the known synergistic pair, being sure to look at all concentrations of those molecules. If the growth score of a mutant is less than the negative Z (2.5) score or is greater than the positive Z (2.5) score for that small molecule, it is considered significant.</li>
	<li>Determine if any of the significant gene mutants belong to an operon. 
	NOTE: For E. coli, the authors recommend checking &#34;Ecoli wiki&#34; for information regarding if genes are transcribed as part of a polycistronic RNA.</li>
	<li>Identify gene/operon mutants that are common to the majority of the small molecules of interest at different concentrations (e.g., when looking for molecules that synergize with the antibiotic trimethoprim, identify mutants that show a significant Z score when grown in the presence of trimethoprim and its known synergistic partners such as sulfamethizole or sulfamethoxazole). These are putative synergy prediction mutants.</li>
</ol>
2. Predicting Synergizers within the Chemical-genetics Dataset by O2M
<ol>
	<li>Returning to the dataset17, identify each concentration of small molecule that elicits a significant growth score from the synergy prediction mutant. This is done by looking at the Z scores calculated for each concentration of small molecules. Again, a significant growth score is less than the negative Z score or greater than the positive Z score for that concentration of small molecule. 
	NOTE: If a molecule appears at even one of its concentrations, the molecule is considered a predicted synergizer. Any molecule that does not elicit a significant growth score is a predicted non-synergizer.</li>
</ol>
3. Validation of Predicted Synergistic Interactions
<ol>
	<li>Finding minimal inhibitory concentration (MIC) of predicted synergizers.
	<ol>
		<li>Grow an overnight culture of E. coli in M9 minimal media at 37 &#176;C. 
		NOTE: Any overnight culture can be grown in appropriately defined medium for the organism of interest. The Brown lab does not recommend rich media like LB or YPD. M9 minimal media consists of 10.5 g/L M9 broth, 0.2% casamino acids, 0.1 M CaCl2, 0.4% glucose, 1 M MgSO4, 0.25% nicotinic acid, and 0.33% thiamine in H2O.</li>
		<li>If no MIC data for pathogen of interest is found in the literature upon receiving the small molecules, dissolve in dimethyl sulfoxide (DMSO) at a high concentration (&#62;10 mg/mL).</li>
		<li>Using a 96-well plate, add 100 &#181;L of M9 media to each well. Add an extra 100 &#181;L of M9 media in column 1 so it contains a total of 200 &#181;L.</li>
		<li>Add an arbitrary amount (~10 &#181;L) of concentrated small molecule solution in column 1. One small molecule per well of column 1 (8 small molecules per plate). 
		NOTE: These are the small molecules of interest, that are predicted to synergize. The Brown lab generally adds 10 &#181;L of highly concentrated small molecule solution, which is below the amount of 100% DMSO that can inhibit E. coli.</li>
		<li>Once the small molecules have been added, dilute the small molecules across the x-axis of the plate. Take 100 &#181;L of solution from column 1, place in column 2 and mix. Take 100 &#181;L from column 2, place in column 3 and mix. Continue this process until 100 &#181;L are placed in column 11. Mix column 11, then take 100 &#181;L from that column and discard. Leave column 12 with just media to normalize the data.</li>
		<li>Inoculate the plate. Obtain the optical density (OD600) from an overnight culture. Prepare a solution with the culture and M9 media to an OD600 of 0.002 (or appropriate concentration that represents 500 cells/&#181;L for the desired organism of interest). Pipette 2 &#181;L of culture solution into each well of the plate so there are 1,000 cells per well.</li>
		<li>Gently shake the plate and obtain the OD600 for the 0 h reading. Let the plate grow at 37 &#176;C for 24 h. Obtain the OD600 for the 24 h reading. 
		NOTE: Use appropriate growing conditions for other organisms of interest.</li>
		<li>Calculate the net growth for each well using a spreadsheet program.</li>
		<li>Average the net growth of column 12 to obtain the average no drug growth. Normalize the other wells to the average of no drug growth using a spreadsheet software. Look for any well with less than 10% growth to identify the MIC90. Any well with less than 50% growth indicates the MIC50. Calculate the concentration of small molecule in those wells.</li>
	</ol>
	</li>
	<li>Checkerboard Assays for synergistic interactions
	<ol>
		<li>Grow an overnight culture of E. coli in M9 minimal media as before. 
		NOTE: Use appropriate growing conditions for other organisms.</li>
		<li>Add 100 &#181;L of M9 media to each well of a 96 well plate and an extra 100 &#181;L in column 1.</li>
		<li>Add the test small molecule (at ~8x MIC) to each well in column 1 and add double the concentration (~16x MIC) to well A1 (one small molecule tested per plate). 
		NOTE: This amount is determined from the MIC test completed prior and differs for each potential synergizer.</li>
		<li>Create a gradient dilution on the x-axis by taking 100 &#181;L from column 1 and transferring to column 2. Continue this process until 100 &#181;L is added to column 11. Mix, then take 100 &#181;L from column 11 and dispose. Do not add any drug to column 12 (Figure 1A).</li>
		<li>Set up the second drug gradient across the y-axis for the input drug. Add 100 &#181;L of media containing 2x MIC of the input molecule in row A. Mix and dilute as before, taking 100 &#181;L from row A and placing in B. Continue until 100 &#181;L is placed in row G. Mix, take 100 &#181;L and dispose, ensuring not to add any input drug in row H. This allows row H and column 12 to contain individual MICs of the drug pair tested (Figure 1B).</li>
		<li>Inoculate the plate. Add 2 &#181;L of a E. coli culture of OD600 0.002 to each well (1,000 cells per well).</li>
		<li>Measure the OD600 of each well in the plate for the 0 h reading.</li>
		<li>Incubate the plate for 24 h at 37 &#176;C.</li>
		<li>Measure the OD600 of each well at 24 h.</li>
	</ol>
	</li>
	<li>Calculating the fractional inhibitory concentration index (FICI) from Checkerboards
	<ol>
		<li>Calculate the net growth for each well of the plate by subtracting the OD600 at 0 h from the OD600 at 24 h in a spreadsheet program.</li>
		<li>Normalize the growth in the software by dividing each well by well H12, which contains no small molecule.</li>
		<li>Find the MIC90 of the input molecule in column 12 and the MIC90 of the potential synergist in row H.</li>
		<li>Look for all wells that inhibit 90% of growth.</li>
		<li>Calculate the FICI90 for the well that is either the lowest below (synergistic) or highest above (antagonistic) both MIC90 values, using the equation: 
		<img alt="Equation 1" src="/files/ftp_upload/57241/57241eq1.jpg" /></li>
		<li>Consider any FICI &#8804; 0.5 as synergistic and FICI &#8805; 4 as antagonistic (Figure 2). 
		NOTE: This can also be done with MIC50 (50% inhibition of growth) to get an FICI50 based on those MIC values.</li>
	</ol>
	</li>
	<li>Bliss Independence Assay 
	NOTE: Bliss Independence is run with any potential synergist that did not have an MIC on its own.
	<ol>
		<li>Grow an overnight culture of E. coli in M9 media at 37 &#176;C, as before. 
		NOTE: Again, appropriate growing conditions for other organisms of interest can be used in this step.</li>
		<li>Prepare 96-well plates for testing the potential synergistic molecule, the input molecule, the combination, and a non-treated control.</li>
		<li>Create the potential synergist plate by adding 50 &#181;L of M9 media to each well. Add 50 &#181;L of media containing a set concentration (10 &#181;M or 100 &#181;M) of the potential synergist to each well of a row. Test 8 molecules per plate.</li>
		<li>Create the input molecule plate by adding 50 &#181;L of media to each well. Add 50 &#181;L containing 4x MIC of the input drug to each well in column 1. Create a gradient dilution along the x-axis similar to MIC plates, taking 50 &#181;L from column 1, dispersing and mixing in column 2. Continue this process until column 12, disposing 50 &#181;L from column 12. Add an additional 50 &#181;L of media to each well so the end total for each well is 100 &#181;L.</li>
		<li>Creating the combination plate by using a 96 well plate, add 50 &#181;L of media to each well. Add 50 &#181;L containing 4x MIC of the input drug to each well in column 1. Dilute along the x-axis similar to MIC plates, taking 50 &#181;L from column 1, dispersing and mixing in column 2. Continue this the whole way to column 12, disposing 50 &#181;L from column 12. Add 50 &#181;L of media containing the potential synergist at 10 &#181;M or 100 &#181;M to each well of a row. Again, there should be one concentration or drug per row.</li>
		<li>Create the non-treated control by adding 100 &#181;L of M9 media to all wells of the plate.</li>
		<li>Inoculate all plates by adding 2 &#181;L of bacterial culture with an OD600 of 0.002 or 1,000 cells to each well as before.</li>
		<li>Measure the OD600 of each well at 0 and 24 h.</li>
	</ol>
	</li>
	<li>Calculating the Bliss Independence Score
	<ol>
		<li>Calculate the net growth for each well of the plate by subtracting the OD600 at 0 h from the growth at 24 h using a spreadsheet program.</li>
		<li>Normalize the wells to the average growth from the no drug plate in a spreadsheet program.</li>
		<li>Using spreadsheet software, calculate the inhibition by subtracting the net growth from 1.</li>
		<li>Average the columns in the control plate containing only the input gradient in the spreadsheet software.</li>
		<li>Calculate the bliss score for each well by the equation: 
		Bliss Score = (well X + input column X) &#8211; Combined well X</li>
		<li>Look for negative scores in wells below the MIC90 of the input. 
		NOTE: Negative scores across multiple concentrations would indicate a synergistic interaction.</li>
	</ol>
	</li>
</ol>
4. High-throughput Screen with Synergy Prediction Mutants to Identify Novel Synergistic Pairs
<ol>
	<li>Identify synergy prediction mutants 
	NOTE: Step 1.5 identified putative synergy prediction mutants. Here, we determine which of these mutants will function best in a high-throughput screen for synergistic drugs. We performed this assay in a Bioscreen machine, but 96 well plate readers should also be acceptable.
	<ol>
		<li>Grow each putative synergy prediction mutants and wild-type cells in M9 minimal media.</li>
		<li>Set up the 100 well honeycomb plate (or 96 well plate) with the appropriate growth medium, growth medium + input drug, growth medium + known synergistic partner of the input drug, and growth medium + known non-synergistic drug. Ensure to include vehicle controls. 
		NOTE: We recommend four replicate wells for each strain/drug/concentration.</li>
		<li>Inoculate 1,000 cells per well, including blank control wells.</li>
		<li>Grow 48 h at 37 &#176;C, with shaking, and read OD600 every 20 min.</li>
		<li>Determine drug concentration and time point that shows the largest growth difference between wild-type and synergy prediction mutant cells in the presence of the input drug and its known synergistic partners. At the optimal time point and concentration, there should not be much growth difference between wild-type and synergy prediction mutant cells when grown in the presence of drugs known to not act synergistically with the input drug (Figure 3).</li>
	</ol>
	</li>
	<li>High-throughput screen for synergistic drugs
	<ol>
		<li>Grow wild-type cells and synergy prediction mutant cells in M9 minimal media. 
		NOTE: Use appropriate growth medium for organism of interest.</li>
		<li>Add drugs from the library to media so each well which contains 100 &#181;L media with small molecules at set concentration which was determined in the previous step. Include blank wells (without cells) and wells with vehicle controls (e.g., DMSO) to account for any growth inhibition by the drug dilution vehicle.
		<ol>
			<li>Prepare at least four vehicle control wells per plate, ideally scattered across the plate to account for well position effects. If assays are variable, then include at least one vehicle control well per row/column and use each row/column control well as the control to calculate Z scores for each row/column instead of for an entire plate (step 4.2.5).</li>
		</ol>
		</li>
		<li>Add 2 &#181;L of culture to each well as before. (One plate for wild type bacteria and one plate for predicted synergy mutant bacteria).</li>
		<li>Assess the OD600 at 0 and 18 h for E. coli or 48 h for C. neoformans.</li>
		<li>Using spreadsheet software, calculate the net growth by subtracting the OD600 of blank wells from the vehicle control wells. Identify wells with a Z-score of -2.5 (Z score = mean - 2.5*standard deviation). These wells contain a potential synergist small molecule.</li>
		<li>Validate screen hits using the methods outlined in step 3.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

Synergistic small molecule pairs can be a powerful tool in treating microbial infections, yet they have not reached their full clinical potential because synergistic pairs are challenging to identify. This paper describes a method for identifying synergistic pairs much faster than simple pairwise combinations. By using chemical-genetics datasets, O2M identifies mutants with gene knockouts that can then be used as a readout to screen large libraries of small molecules in order to predict synergistic pairs. The ability to ...

Antibiotic-resistant bacteria cause more than 2 million infections and 23,000 deaths annually in the United States according to the CDC1. New treatments are needed to overcome these infections. Strategies to identify these new treatments include the development of new antimicrobial drugs or the repurposing of small molecules approved for other conditions to treat microbial infections2,3,4. However, new drug discovery is very costly and time-consuming. Repurposing drugs may not identify novel drugs or drug targets5,6. Our lab focuses on a third strategy known as synergistic combination therapies. Synergistic combinations occur when two small molecules together have an efficacy greater than the additive effect of their individual efficacies7. Additionally, synergistic combinations can be effective against a pathogen resistant to one of the small molecules in the pair in addition to having less unwanted off-target effects, rendering them great potential8,9,10.
Synergistic pairs are rare, occurring in approximately 4-10% of drug combinations11,12,13. Thus, traditional techniques such as pairwise screens are challenging and time-consuming, with thousands of potential combinations from a small library of a hundred molecules. Moreover, synergistic interactions usually cannot be predicted from the activity of the compounds14. However, the authors developed a high-throughput approach to screen for synergistic pairs, called the Overlap2 Method (O2M)12. This method, described here, allows for faster, more efficient identification of these synergistic pairs. O2M requires the use of a known synergistic pair and a chemical-genetics dataset. Chemical-genetics datasets are generated when a library of knockout mutants is grown in the presence of many different small molecules. If one molecule in a known synergistic pair induces the same phenotype from a particular knockout mutant as the second synergistic molecule, any other small molecule that elicits the phenotype from that same mutant should also synergize with each member of the known synergistic pair. This rationale has been used in the Brown lab to identify synergistic antibiotic pairs active against Escherichia coli (E. coli) and synergistic antifungal drug pairs active against the pathogenic fungus Cryptococcus neoformans (C. neoformans)11,12. O2M is not only adaptable for various pathogens, but allows for the screening of large libraries of molecules to identify synergistic pairs easily and rapidly. Screening with the genetic mutant identified by O2M allows us to validate only those small molecules predicted for synergy. Thus, testing a 2,000-molecule library pairwise would take months, whereas if there were only 20 molecules in that library predicted to synergize, testing for synergy now takes a matter of days. O2M does not require programming skills, and the required equipment is available in most labs or core facilities. In addition to researchers interested in drug combinations, O2M analysis is of interest to anyone who has completed a drug screen and wants to expand their hits by identifying important drug-drug interactions. Below is the protocol for identifying synergistic small molecules in bacteria, as well as validating the predicted synergistic interactions in well-known assays15,16.

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			<td height="22" style="height:22px;width:179px;">Bioscreen C</td>
			<td style="width:88px;">instrument</td>
			<td style="width:136px;">Growth Curves USA</td>
			<td style="width:117px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Synergy H1</td>
			<td>instrument</td>
			<td>BioTek</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">M9 broth</td>
			<td>reagent</td>
			<td>Amresco</td>
			<td>J863-500G</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Casamino Acids</td>
			<td>reagent</td>
			<td>Fisher Scientific</td>
			<td>BP1424-500</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Glucose</td>
			<td>reagent</td>
			<td>Sigma</td>
			<td>G7021-10KG</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Nicotinic Acid</td>
			<td>reagent</td>
			<td>Alfa Aesar</td>
			<td>A12683</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Thiamine</td>
			<td>reagent</td>
			<td>Acros Organics</td>
			<td>148991000</td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;">CaCl2 Dihydrate</td>
			<td>reagent</td>
			<td>Fisher</td>
			<td>C79-500</td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;">MgSO4 Heptahydrate</td>
			<td>reagent</td>
			<td>Fisher</td>
			<td>M63-500</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">chemical-genetics dataset</td>
			<td>dataset</td>
			<td>examples include Nichols et al., Cell, 2011, Brown et al, Cell, 2014, and others cited in the text.</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">trimethoprim (example input drug; any can be used)</td>
			<td>reagent</td>
			<td>Fisher Scientific</td>
			<td>ICN19552701</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">sulfamethoxazole (example test drug; any can be used)</td>
			<td>reagent</td>
			<td>Fisher Scientific</td>
			<td>ICN15671125</td>
		</tr>
	</tbody>
</table>

high-throughput identification of synergistic drug combinations by the overlap2 method

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our entire society's ability to treat systemic infections. In the United States alone, antibiotic-resistant infections kill approximately 23,000 people a year and cost around 20 billion USD in additional healthcare. One approach to combat antimicrobial resistance is combination therapy, which is particularly useful in the critical early stage of infection, before the infecting organism and its drug resistance profile have been identified. Many antimicrobial treatments use combination therapies. However, most of these combinations are additive, meaning that the combined efficacy is the same as the sum of the individual antibiotic efficacy. Some combination therapies are synergistic: the combined efficacy is much greater than additive. Synergistic combinations are particularly useful because they can inhibit the growth of antimicrobial drug resistant strains. However, these combinations are rare and difficult to identify. This is due to the sheer number of molecules needed to be tested in a pairwise manner: a library of 1,000 molecules has 1 million potential combinations. Thus, efforts have been made to predict molecules for synergy. This article describes our high-throughput method for predicting synergistic small molecule pairs known as the Overlap2 Method (O2M). O2M uses patterns from chemical-genetic datasets to identify mutants that are hypersensitive to each molecule in a synergistic pair but not to other molecules. The Brown lab exploits this growth difference by performing a high-throughput screen for molecules that inhibit the growth of mutant but not wild-type cells. The lab's work previously identified molecules that synergize with the antibiotic trimethoprim and the antifungal drug fluconazole using this strategy. Here, the authors present a method to screen for novel synergistic combinations, which can be altered for multiple microorganisms.

Checkerboard assays are a semi-quantitative method for measuring synergistic interactions. The final score output, FICI, determines if a drug combination is considered synergistic (FICI &#8804;0.5), non-interacting (0.5 &#60;FICI &#60;4), or antagonistic (FICI &#8805;4.0). Figure 1 illustrates how to set up the drug gradients in a checkerboard assay. Figure 2 illustrates common outcomes. Consider growth (purple wells) which displ...

Watch this Scientific Journal Video about High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method at JoVE.com

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Synergistic drug combinations are difficult and time-consuming to identify empirically. Here, we describe a method for identifying and validating synergistic small molecules.

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our ...

high-throughput-identification-synergistic-drug-combinations-overlap2

Research

JoVE Journal

Genetics

11.6K Views.  University of Utah. Synergistic drug combinations are difficult and time-consuming to identify empirically. Here, we describe a method for identifying and validating synergistic small molecules. 

Video: High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

A Robotic Platform for High-throughput Protoplast Isolation and Transformation

Over the last decade there has been a resurgence in the use of plant protoplasts that range from model species to crop species, for analysis of signal transduction pathways, transcriptional regulatory networks, gene expression, genome-editing, and gene-silencing. Furthermore, significant progress has been made in the regeneration of plants from protoplasts, which has generated even more interest in the use of these systems for plant genomics. In this work, a protocol has been developed for automation of protoplast isolation and transformation from a &#39;Bright Yellow&#39; 2 (BY-2) tobacco suspension culture using a robotic platform. The transformation procedures were validated using an orange fluorescent protein (OFP) reporter gene (pporRFP) under the control of the Cauliflower mosaic virus 35S promoter (35S). OFP expression in protoplasts was confirmed by epifluorescence microscopy. Analyses also included protoplast production efficiency methods using propidium iodide. Finally, low-cost food-grade enzymes were used for the protoplast isolation procedure, circumventing the need for lab-grade enzymes that are cost-prohibitive in high-throughput automated protoplast isolation and analysis. Based on the protocol developed in this work, the complete procedure from protoplast isolation to transformation can be conducted in under 4 hr, without any input from the operator. While the protocol developed in this work was validated with the BY-2 cell culture, the procedures and methods should be translatable to any plant suspension culture/protoplast system, which should enable acceleration of crop genomics research.

A high-throughput, automated, tobacco protoplast production and transformation methodology is described. The robotic system enables massively parallel gene expression and discovery in the model BY-2 system that should be translatable to non-model crops.

Over the last decade there has been a resurgence in the use of plant protoplasts that range from model species to crop species, for analysis of signal ...

High-Throughput Robotically Assisted Isolation of Temperature-sensitive Lethal Mutants in Chlamydomonas reinhardtii

Systematic identification and characterization of genetic perturbations have proven useful to decipher gene function and cellular pathways. However, the conventional approaches of permanent gene deletion cannot be applied to essential genes. We have pioneered a unique collection of ~70 temperature-sensitive (ts) lethal mutants for studying cell cycle regulation in the unicellular green algae Chlamydomonas reinhardtii1. These mutations identify essential genes, and the ts alleles can be conditionally inactivated by temperature shift, providing valuable tools to identify and analyze essential functions. Mutant collections are much more valuable if they are close to comprehensive, since scattershot collections can miss important components. However, this requires the efficient collection of a large number of mutants, especially in a wide-target screen. Here, we describe a robotics-based pipeline for generating ts lethal mutants and analyzing their phenotype in Chlamydomonas. This technique can be applied to any microorganism that grows on agar. We have collected over 3000 ts mutants, probably including mutations in most or all cell-essential pathways, including about 200 new candidate cell cycle mutations. Subsequent molecular and cellular characterization of these mutants should provide new insights in plant cell biology; a comprehensive mutant collection is an essential prerequisite to ensure coverage of a broad range of biological pathways. These methods are integrated with downstream genetics and bioinformatics procedures for efficient mapping and identification of the causative mutations that are beyond the scope of this manuscript.

High-Throughput Robotically Assisted Isolation of Temperature-sensitive Lethal Mutants in Chlamydomonas reinhardtii

Temperature-sensitive (ts) lethal mutants are valuable tools to identify and analyze essential functions. Here we describe methods to generate and classify ts lethal mutants in high throughput.

Systematic identification and characterization of genetic perturbations have proven useful to decipher gene function and cellular pathways. However, the ...

Controllable Ion Channel Expression through Inducible Transient Transfection

Transfection, the delivery of foreign nucleic acids into a cell, is a powerful tool in protein research. Through this method, ion channels can be investigated through electrophysiological analysis, biochemical characterization, mutational studies, and their effects on cellular processes. Transient transfections offer a simple protocol in which the protein becomes available for analysis within a few hours to days. Although this method presents a relatively straightforward and time efficient protocol, one of the critical components is calibrating the expression of the gene of interest to physiological relevant levels or levels that are suitable for analysis. To this end, many different approaches that offer the ability to control the expression of the gene of interest have emerged. Several stable cell transfection protocols provide a way to permanently introduce a gene of interest into the cellular genome under the regulation of a tetracycline-controlled transcriptional activation. While this technique produces reliable expression levels, each gene of interest requires a few weeks of skilled work including calibration of a killing curve, selection of cell colonies, and overall more resources. Here we present a protocol that uses transient transfection of the Transient Receptor Potential cation channel subfamily V member 1 (TRPV1) gene in an inducible system as an efficient way to express a protein in a controlled manner which is essential in ion channel analysis. We demonstrate that using this technique, we are able to perform calcium imaging, whole cell, and single channel analysis with controlled channel levels required for each type of data collection with a single transfection. Overall, this provides a replicable technique that can be used to study ion channels structure and function.

Studying ion channels through a heterologously expressing system has become a core technique in biomedical research. In this manuscript, we present a time efficient method to achieve tightly controlled ion channel expression by performing transient transfection under the control of an inducible promoter.

Transfection, the delivery of foreign nucleic acids into a cell, is a powerful tool in protein research. Through this method, ion channels can be ...

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution based analysis methodology (nucleosome density ChIP-seq; ndChIP-seq) that enables the generation of combined measurements of micrococcal nuclease (MNase) accessibility with histone modification genome-wide. Nucleosome position and local density, and the posttranslational modification of their histone subunits, act in concert to regulate local transcription states. Combinatorial measurements of nucleosome accessibility with histone modification generated by ndChIP-seq allows for the simultaneous interrogation of these features. The ndChIP-seq methodology is applicable to small numbers of primary cells inaccessible to cross-linking based ChIP-seq protocols. Taken together, ndChIP-seq enables the measurement of histone modification in combination with local nucleosome density to obtain new insights into shared mechanisms that regulate RNA transcription within rare primary cell populations.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) methodology for the generation of sequence datasets suitable for a nucleosome density ChIP-seq analytical framework integrating micrococcal nuclease (MNase) accessibility with histone modification measurements.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution ...

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have been directed toward elucidation of the location and timing of initiation of genome duplication rather than its completion. However, it is critical that we understand regions of the genome that are last to complete replication, because these regions suffer elevated levels of chromosomal breakage and mutation, and they have been associated with both disease and aging. Here we describe how we have extended a technique that has been used to monitor replication initiation to instead identify those regions of the genome last to complete replication. This approach is based on a combination of flow cytometry and high throughput sequencing. Although this report focuses on the application of this technique to yeast, the approach can be used with any cells that can be sorted in a flow cytometer according to DNA content.

We describe a technique for combining flow cytometry and high throughput sequencing to identify late replicating regions of the genome.

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have ...

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with a number of challenges including sample preservation quality, degraded DNA, and destructive sampling of rare specimens. In order to more effectively use herbarium material in large sequencing projects, a dependable and scalable method of DNA isolation and library preparation is needed. This paper demonstrates a robust, beginning-to-end protocol for DNA isolation and high-throughput library construction from herbarium specimens that does not require modification for individual samples. This protocol is tailored for low quality dried plant material and takes advantage of existing methods by optimizing tissue grinding, modifying library size selection, and introducing an optional reamplification step for low yield libraries. Reamplification of low yield DNA libraries can rescue samples derived from irreplaceable and potentially valuable herbarium specimens, negating the need for additional destructive sampling and without introducing discernible sequencing bias for common phylogenetic applications. The protocol has been tested on hundreds of grass species, but is expected to be adaptable for use in other plant lineages after verification. This protocol can be limited by extremely degraded DNA, where fragments do not exist in the desired size range, and by secondary metabolites present in some plant material that inhibit clean DNA isolation. Overall, this protocol introduces a fast and comprehensive method that allows for DNA isolation and library preparation of 24 samples in less than 13 h, with only 8 h of active hands-on time with minimal modifications.

This article demonstrates a detailed protocol for DNA isolation and high-throughput sequencing library construction from herbarium material including rescue of exceptionally poor-quality DNA.

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with ...

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 ...

High-throughput DNA Extraction and Genotyping of 3dpf Zebrafish Larvae by Fin Clipping

Zebrafish (Danio rerio) possess orthologues for 84% of the genes known to be associated with human diseases. In addition, these animals have a short generation time, are easy to handle, display a high reproductive rate, low cost, and are easily amenable to genetic manipulations by microinjection of DNA in embryos. Recent advances in gene editing tools are enabling precise introduction of mutations and transgenes in zebrafish. Disease modeling in zebrafish often leads to larval phenotypes and early death which can be challenging to interpret if genotypes are unknown. This early identification of genotypes is also needed in experiments requiring sample pooling, such as in gene expression or mass spectrometry studies. However, extensive genotypic screening is limited by traditional methods, which in most labs are performed only on adult zebrafish or in postmortem larvae. We addressed this problem by adapting a method for the isolation of PCR-ready genomic DNA from live zebrafish larvae that can be achieved as early as 72 h post-fertilization (hpf). This time and cost-effective technique, improved from a previously published genotyping protocol, allows the identification of genotypes from microscopic fin biopsies. The fins quickly regenerate as the larvae develop. Researchers are then able to select and raise the desired genotypes to adulthood by utilizing this high-throughput PCR-based genotyping procedure.

Zebrafish have been used as reliable genetic model organisms in biomedical research, especially with the advent of gene-editing technologies. When larval phenotypes are expected, DNA extraction and genotype identification can be challenging. Here, we describe an efficient genotyping procedure for zebrafish larvae, by tail clipping, as early as 72-h post-fertilization.

Zebrafish (Danio rerio) possess orthologues for 84% of the genes known to be associated with human diseases. In addition, these animals have a short ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing (RIPiT-Seq)

RNA immunoprecipitation in tandem (RIPiT) is a method for enriching RNA footprints of a pair of proteins within an RNA:protein (RNP) complex. RIPiT employs two purification steps. First, immunoprecipitation of a tagged RNP subunit is followed by mild RNase digestion and subsequent non-denaturing affinity elution. A second immunoprecipitation of another RNP subunit allows for enrichment of a defined complex. Following a denaturing elution of RNAs and proteins, the RNA footprints are converted into high-throughput DNA sequencing libraries. Unlike the more popular ultraviolet (UV) crosslinking followed by immunoprecipitation (CLIP) approach to enrich RBP binding sites, RIPiT is UV-crosslinking independent. Hence RIPiT can be applied to numerous proteins present in the RNA interactome and beyond that are essential to RNA regulation but do not directly contact the RNA or UV-crosslink poorly to RNA. The two purification steps in RIPiT provide an additional advantage of identifying binding sites where a protein of interest acts in partnership with another cofactor. The double purification strategy also serves to enhance signal by limiting background. Here, we provide a step-wise procedure to perform RIPiT and to generate high-throughput sequencing libraries from isolated RNA footprints. We also outline RIPiT&#39;s advantages and applications and discuss some of its limitations.

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq

Here, we present a protocol to enrich endogenous RNA binding sites or &#34;footprints&#34;&#160;of RNA:protein (RNP) complexes from mammalian cells. This approach involves two immunoprecipitations of RNP subunits and is therefore dubbed RNA immunoprecipitation in tandem (RIPiT).

RNA immunoprecipitation in tandem (RIPiT) is a method for enriching RNA footprints of a pair of proteins within an RNA:protein (RNP) complex. RIPiT ...