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 quant...

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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.

<table border="0" cellpadding="0" cellspacing="0" width="520">
	<tbody>
		<tr height="22">
			<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 ...

The overall goal of this methodology is to identify the synergistic molecules, which amplify each others activity, when combined through High-throughput screening. Synergistic drug pairs are a promising strategy, for combating drug resistant infections but they are difficult to identify. This method can identify molecules that interact with existing antimicrobial drugs used to treat infectious diseases.In the long term these combinations, can enter preclinical assessment and eventually improve patient care. The main advantage of this technique is that it can be done with any organism of interest and with any with any library of small molecules. The implications of this technique, extend towards therapy of multiple infectious diseases as it works with any organism of interest.Use a standard spreadsheet to calculate the mean growth score and standard deviation for all the mutants, in the presence of a particular concentration of the small molecule added. Then, calculate the Z scores for each of the small molecule concentration. Then, highlight the gene mutants with significant Z scores.First, grow an E.coli culture in M9 minimal medium, for overnight at 37 degrees Celsius. Then, dissolve the small molecules in dimethyl sulphoxide at a high concentration above 10 milligrams per milliliter. Next, add 100 microliters of M9 medium to each well in a 96 well plate.Pipette an additional 100 microliters of M9 medium in column one such that the volume in that column is 200 microliters. Then, add approximately 10 microliters of the concentrated small molecule solution, in the column one. After adding the small molecules, start diluting it across the X axis of the plate.Continue diluting till there is, 100 microliters in column 11. After mixing column 11, discard 100 microliters from that column. Let there be only medium in column 12 to normalize the data.Next, record the optical density value at 600 nanometers of the overnight culture grown in M9 minimal medium. Then, add two microliters of the culture into each well. Such that there are 1000 cells per well.Then, shake the plate gently. And note the optical density reading at 600 nanometers for zero hour time point. Once the reading is recorded, incubate the plate at 37 degrees Celsius for a day.Again, measure the optical density value at 600 nanometers for 24 hour time point. To perform the checkerboard assay, grow an E.coli culture in M9 minimal medium for overnight at 37 degrees Celsius. Then, add 100 microliters of M9 medium to each well and add an extra 100 microliters of the same medium in column one of a 96 well plate.Next, add the tests small molecules, in each well in column one. And add double the concentration in well A1.Then, create a dilution gradient along the X axis by transferring 100 microliters, from column one to column two. Continue the dilution process, until 100 microliters is added in column 11.After mixing column 11, discard 100 microliters from that column and leave column 12 with only medium without any drugs. Create another gradient for the second drug dilution. To do this, add 100 microliters of the medium containing two X, minimal inhibitory concentration of the input molecule in row A.Then, transfer 100 microliters from row A to B.Continue the dilution process, until 100 microliters is added in row G.After mixing row G, discard 100 microliters from the well.Then inoculate the plate with two microliters of the culture into each well. Such that there are 1000 cells per well. Then, measure the optical density reading at 600 nanometers for zero hour time point.Once the reading is recorded, incubate the plate at 37 degrees Celsius for 24 hours. And then, measure the optical density value at 600 nanometers for 24 hour time point. To conduct High-throughput screening, for synergistic drugs, culture both wild type and synergy prediction mutant cells in M9 minimal medium.Then, add the drugs from the library to the medium. Next, inoculate one plate of library drugs with the wild type and one other plate with synergy prediction mutant bacteria. This plate represents the checkerboard assay used to investigate synergistic drug combinations.In this plate, well F9 with the lowest FICI score of 0.07 indicates that the small molecule pair is synergistic. This plate represents the checkerboard assay to investigate non interacting drug combination. In this plate, the well C3 with the lowest FICI score of 1.0 indicates that the small molecule pair, does not interact with each other.This plate represent the checkerboard assay used to investigate antagonistic drug combinations. In this plate, well A1 with the lowest FICI score of 8.0 indicates that the interaction between, the drug molecules is antagonistic. Next Befeldin A, a small molecule drug in Fluconazole, is used to study the growth of C and U Formins.The plot shows that the growth of both the wild type and the synergy prediction mutant is inhibited slightly and to the same extent. This indicates that Befeldin A and Fluconazole are non-synergistic drug partners. On the contrary, the growth of synergy prediction mutant of C and U Formins is inhibited significantly, in the presence of Refromisyn and Fluconazole.Indicating that the drugs are synergistic partners. Interestingly, the wild type growth is not inhibited at all. Here, the greatest growth difference is observed, between 32 and 49 hours post inoculation.Once mastered, this technique can shorten the time to identify synergistic drug interactions. While carrying out this procedure, it's important to remember not to combine drugs in the last column in the last row of the checkerboard assay. Following this procedure, other methods of synergistic molecule identification, can be performed in order to identify additional synergistic drug interactions.After watching this video, you should have a good understanding of how to use the O2M methodology to identify putative synergy markers in order to find synergistic drug interactions with your organism of interest. Don't forget that while working with some small molecules can be hazardous and any precautions in the MSDS, should be followed while performing this protocol.

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11.4K vistas.  University of Utah. The overall goal of this methodology is to identify the synergistic molecules, which amplify each others activity, when combined through High-throughput screening. Synergistic drug pairs are a promising strategy, for combating drug resistant infections but they are difficult to identify. This method can identify molecules that interact with existing antimicrobial drugs used to treat infectious diseases.In the long term these combinations, can enter preclinical assessment and eventually...