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...

<ol>
	<li>Michael, C. A., Dominey-Howes, D., Labbate, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Antimicrobial+Resistance+Crisis:+Causes,+Consequences,+and+Management.">The Antimicrobial Resistance Crisis: Causes, Consequences, and Management.</a> Front Public Health. 2, 145 (2014).</li><li>Butts, A., Krysan, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antifungal+Drug+Discovery:+Something+Old+and+Something+New.">Antifungal Drug Discovery: Something Old and Something New.</a> PLOS Pathogens. 8 (9), e1002870 (2012).</li><li>Roemer, T., Krysan, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antifungal+Drug+Development:+Challenges,+Unmet+Clinical+Needs,+and+New+Approaches.">Antifungal Drug Development: Challenges, Unmet Clinical Needs, and New Approaches.</a> Cold Spring Harb Perspect Med. 4 (5), a019703 (2014).</li><li>Oprea, T. I., Mestres, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drug+Repurposing:+Far+Beyond+New+Targets+for+Old+Drugs.">Drug Repurposing: Far Beyond New Targets for Old Drugs.</a> AAPS J. 14 (4), 759-763 (2012).</li><li>Scannell, J. W., Blanckley, A., Boldon, H., Warrington, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnosing+the+decline+in+pharmaceutical+R&amp;D+efficiency.">Diagnosing the decline in pharmaceutical R&amp;D efficiency.</a> Nat Rev Drug Discov. 11 (3), 191-200 (2012).</li><li>Rangel-Vega, A., Bernstein, L. R., Mandujano-Tinoco, E. A., Garc&#237;a-Contreras, S. J., Garc&#237;a-Contreras, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drug+repurposing+as+an+alternative+for+the+treatment+of+recalcitrant+bacterial+infections.">Drug repurposing as an alternative for the treatment of recalcitrant bacterial infections.</a> Front Microbiol. 6, 282 (2015).</li><li>Torella, J. P., Chait, R., Kishony, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimal+Drug+Synergy+in+Antimicrobial+Treatments.">Optimal Drug Synergy in Antimicrobial Treatments.</a> PLoS Comput Biol. 6 (6), e1000796 (2010).</li><li>Cowen, L. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Harnessing+Hsp90+function+as+a+powerful,+broadly+effective+therapeutic+strategy+for+fungal+infectious+disease.">Harnessing Hsp90 function as a powerful, broadly effective therapeutic strategy for fungal infectious disease.</a> P Natl Acad Sci. 106 (8), 2818-2823 (2009).</li><li>Zuo, G. -. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synergistic+Antibacterial+and+Antibiotic+Effects+of+Bisbenzylisoquinoline+Alkaloids+on+Clinical+Isolates+of+Methicillin-Resistant+Staphylococcus+Aureus+(MRSA).">Synergistic Antibacterial and Antibiotic Effects of Bisbenzylisoquinoline Alkaloids on Clinical Isolates of Methicillin-Resistant Staphylococcus Aureus (MRSA).</a> Molecules. 16 (12), 9819 (2011).</li><li>Jia, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanisms+of+drug+combinations:+interaction+and+network+perspectives.">Mechanisms of drug combinations: interaction and network perspectives.</a> Nat Rev Drug Discov. 8 (2), 111-128 (2009).</li><li>Wambaugh, M. A., Shakya, V. P. S., Lewis, A. J., Mulvey, M. A., Brown, J. C. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+identification+and+rational+design+of+synergistic+small-molecule+pairs+for+combating+and+bypassing+antibiotic+resistance.">High-throughput identification and rational design of synergistic small-molecule pairs for combating and bypassing antibiotic resistance.</a> PLOS Biol. 15 (6), e2001644 (2017).</li><li>Brown, J. C. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unraveling+the+biology+of+a+fungal+meningitis+pathogen+using+chemical+genetics.">Unraveling the biology of a fungal meningitis pathogen using chemical genetics.</a> Cell. 159 (5), 1168-1187 (2014).</li><li>Cokol, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+exploration+of+synergistic+drug+pairs.">Systematic exploration of synergistic drug pairs.</a> Mol Syst Biol. 7, 544-544 (2011).</li><li>Borisy, A. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+discovery+of+multicomponent+therapeutics.">Systematic discovery of multicomponent therapeutics.</a> Proc Natl Acad Sci USA. 100 (13), 7977-7982 (2003).</li><li>Tang, J., Wennerberg, K., Aittokallio, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+is+synergy?+The+Saariselk&#228;+agreement+revisited.">What is synergy? The Saariselk&#228; agreement revisited.</a> Front Pharmacol. 6, 181 (2015).</li><li>Hsieh, M. H., Yu, C. M., Yu, V. L., Chow, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synergy+assessed+by+checkerboard.+A+critical+analysis.">Synergy assessed by checkerboard. A critical analysis.</a> Diagn Microbiol Infect Dis. 16 (4), 343-349 (1993).</li><li>Nichols, R. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phenotypic+Landscape+of+a+Bacterial+Cell.">Phenotypic Landscape of a Bacterial Cell.</a> Cell. 144 (1), 143-156 (2011).</li><li>Chandrasekaran, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemogenomics+and+orthology-based+design+of+antibiotic+combination+therapies.">Chemogenomics and orthology-based design of antibiotic combination therapies.</a> Mol Syst Biol. 12 (5), (2016).</li><li>Pradhan, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemogenomic+profiling+of+Plasmodium+falciparum+as+a+tool+to+aid+antimalarial+drug+discovery.">Chemogenomic profiling of Plasmodium falciparum as a tool to aid antimalarial drug discovery.</a> Sci Rep. 5, 15930 (2015).</li><li>Pethe, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+chemical+genetic+screen+in+Mycobacterium+tuberculosis+identifies+carbon-source-dependent+growth+inhibitors+devoid+of+in+vivo+efficacy.">A chemical genetic screen in Mycobacterium tuberculosis identifies carbon-source-dependent growth inhibitors devoid of in vivo efficacy.</a> Nat Commun. 1 (5), 1-8 (2010).</li><li>Diezmann, S., Michaut, M., Shapiro, R. S., Bader, G. D., Cowen, L. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+the+Hsp90+Genetic+Interaction+Network+in+Candida+albicans+Reveals+Environmental+Contingency+and+Rewired+Circuitry.">Mapping the Hsp90 Genetic Interaction Network in Candida albicans Reveals Environmental Contingency and Rewired Circuitry.</a> PLoS Genetics. 8 (3), e1002562 (2012).</li><li>Robbins, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+Antifungal+Combination+Matrix+Identifies+a+Rich+Pool+of+Adjuvant+Molecules+that+Enhance+Drug+Activity+Against+Diverse+Fungal+Pathogens.">An Antifungal Combination Matrix Identifies a Rich Pool of Adjuvant Molecules that Enhance Drug Activity Against Diverse Fungal Pathogens.</a> Cell reports. 13 (7), 1481-1492 (2015).</li><li>Wildenhain, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+Synergism+from+Chemical-Genetic+Interactions+by+Machine+Learning.">Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning.</a> Cell Syst. 1 (6), 383-395 (2015).</li><li>Spitzer, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cross-species+discovery+of+syncretic+drug+combinations+that+potentiate+the+antifungal+fluconazole.">Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole.</a> Mol Syst Biol. 7, 499-499 (2011).</li></ol>

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 ビュー.  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...

ビデオ: 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 ...