Name: genome-wide profiling of transcription factor-dna binding interactions in candida albicans: a comprehensive cut&amp;run method and data analysis workflow
Uploaded: 2022-04-01T14:45:00
Description: Watch this Scientific Journal Video about Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow at JoVE.

We thank all past and present members of the Nobile and Hernday laboratories for feedback on the manuscript.&#160;This work was supported by the National Institutes of Health (NIH) National Institute of General Medical Sciences (NIGMS) award number R35GM124594 and by the Kamangar family in the form of an endowed chair to C.J.N. This work was also supported by the NIH National Institute of Allergy and Infectious Diseases (NIAID) award number R15AI137975 to A.D.H. C.L.E. was supported by the NIH National Institute of Dental and Craniofacial Research (NIDCR) fellowship number F31DE028488. The content is the sole responsibility of the authors and does not represent the views of the funders. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

1. Epitope Tagging of C. albicans strains
<ol>
	<li>Upload the gene of interest, along with its 1 kb upstream and downstream flanking sequences, from the Candida Genome Database to the primer design tool (see the Table of Materials). Design a guide RNA (gRNA) by highlighting 50 bp upstream and downstream from the stop codon, and click the gRNA selection tool on the right. Select Design and Analyze Guides. Use the Ca22 (Candida alb...

<ol>
	<li>Gulati, M., Nobile, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Candida+albicans+biofilms:+development,+regulation,+and+molecular+mechanisms.">Candida albicans biofilms: development, regulation, and molecular mechanisms.</a> Microbes and Infection. 18 (5), 310-321 (2016).</li><li>Lohse, M. B., Gulati, M., Johnson, A. D., Nobile, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+regulation+of+single-and+multi-species+Candida+albicans+biofilms.">Development and regulation of single-and multi-species Candida albicans biofilms.</a> Nature Reviews Microbiology. 16 (1), 19-31 (2018).</li><li>Nobile, C. J., Johnson, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Candida+albicans+biofilms+and+human+disease.">Candida albicans biofilms and human disease.</a> Annual Review of Microbiology. 69 (1), 71-92 (2015).</li><li>Nobile, C. 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=A+recently+evolved+transcriptional+network+controls+biofilm+development+in+Candida+albicans.">A recently evolved transcriptional network controls biofilm development in Candida albicans.</a> Cell. 148 (1-2), 126-138 (2012).</li><li>Iracane, E., Vega-Est&#233;vez, S., Buscaino, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+and+off:+epigenetic+regulation+of+C.+albicans+morphological+switches.">On and off: epigenetic regulation of C. albicans morphological switches.</a> Pathogens. 10 (11), 1463 (2021).</li><li>Qasim, M. N., Valle Arevalo, A., Nobile, C. J., Hernday, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+roles+of+chromatin+accessibility+in+regulating+the+Candida+albicans+white-opaque+phenotypic+switch.">The roles of chromatin accessibility in regulating the Candida albicans white-opaque phenotypic switch.</a> Journal of Fungi. 7 (1), 37 (2021).</li><li>Mancera, 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=Evolution+of+the+complex+transcription+network+controlling+biofilm+formation+in+candida+species.">Evolution of the complex transcription network controlling biofilm formation in candida species.</a> eLife. 10, 64682 (2021).</li><li>Lohse, M. B., Johnson, A. 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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chromatin+immunoprecipitation+and+microarray-based+analysis+of+protein+location.">Chromatin immunoprecipitation and microarray-based analysis of protein location.</a> Nature Protocols. 1 (2), 729-748 (2006).</li><li>Zentner, G. E., Kasinathan, S., Xin, B., Rohs, R., Henikoff, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ChEC-seq+kinetics+discriminates+transcription+factor+binding+sites+by+DNA+sequence+and+shape+in+vivo.">ChEC-seq kinetics discriminates transcription factor binding sites by DNA sequence and shape in vivo.</a> Nature Communications. 6, 8733 (2015).</li><li>Schmid, M., Durussel, T., Laemmli, U. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ChIC+and+ChEC:+Genomic+mapping+of+chromatin+proteins.">ChIC and ChEC: Genomic mapping of chromatin proteins.</a> Molecular Cell. 16 (1), 147-157 (2004).</li><li>Skene, P. J., Henikoff, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+efficient+targeted+nuclease+strategy+for+high-resolution+mapping+of+DNA+binding+sites.">An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites.</a> eLife. 6, 21856 (2017).</li><li>Skene, P. J., Henikoff, J. G., Henikoff, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeted+in+situ+genome-wide+profiling+with+high+efficiency+for+low+cell+numbers.">Targeted in situ genome-wide profiling with high efficiency for low cell numbers.</a> Nature Protocols. 13 (5), 1006-1019 (2018).</li><li>Nguyen, N., Quail, M. M. F., Hernday, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+efficient,+rapid,+and+recyclable+system+for+CRISPR-mediated+genome+editing+in+Candida+albicans.">An efficient, rapid, and recyclable system for CRISPR-mediated genome editing in Candida albicans.</a> American Society For Microbiology. 2 (2), 00149 (2017).</li><li>Ennis, C. L., Hernday, A. D., Nobile, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+markerless+CRISPR-mediated+system+for+genome+editing+in+Candida+auris+reveals+a+conserved+role+for+Cas5+in+the+caspofungin+response.">A markerless CRISPR-mediated system for genome editing in Candida auris reveals a conserved role for Cas5 in the caspofungin response.</a> Microbiology Spectrum. 9 (3), 0182021 (2021).</li><li>Meers, M. P., Bryson, T. D., Henikoff, J. G., Henikoff, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+CUT&amp;RUN+chromatin+profiling+tools.">Improved CUT&amp;RUN chromatin profiling tools.</a> eLife. 8, 46314 (2019).</li><li>Skrzypek, M. 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=The+Candida+Genome+Database+(CGD):+Incorporation+of+Assembly+22,+systematic+identifiers+and+visualization+of+high+throughput+sequencing+data.">The Candida Genome Database (CGD): Incorporation of Assembly 22, systematic identifiers and visualization of high throughput sequencing data.</a> Nucleic Acids Research. 45, 592-596 (2017).</li><li>Quinlan, A. R., Hall, I. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BEDTools:+a+flexible+suite+of+utilities+for+comparing+genomic+features.">BEDTools: a flexible suite of utilities for comparing genomic features.</a> Bioinformatics. 26 (6), 841-842 (2010).</li><li>Landt, S. G., 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=ChIP-seq+guidelines+and+practices+of+the+ENCODE+and+modENCODE+consortia.">ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.</a> Genome Research. 22 (9), 1813-1831 (2012).</li><li>Boyd, J., Rodriguez, P., Schjerven, H., Frietze, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ssvQC:+an+integrated+CUT&amp;RUN+quality+control+workflow+for+histone+modifications+and+transcription+factors.">ssvQC: an integrated CUT&amp;RUN quality control workflow for histone modifications and transcription factors.</a> BMC Research Notes. 14 (1), 366 (2021).</li><li>Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S., Karolchik, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BigWig+and+BigBed:+enabling+browsing+of+large+distributed+datasets.">BigWig and BigBed: enabling browsing of large distributed datasets.</a> Bioinformatics. 26 (17), 2204-2207 (2010).</li><li>Homann, O. R., Johnson, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MochiView:+Versatile+software+for+genome+browsing+and+DNA+motif+analysis.">MochiView: Versatile software for genome browsing and DNA motif analysis.</a> BMC Biology. 8, 49 (2010).</li><li>Hainer, S. J., Fazzio, T. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+chromatin+profiling+using+CUT&amp;RUN.">High-resolution chromatin profiling using CUT&amp;RUN.</a> Current Protocols in Molecular Biology. 126 (1), 85 (2019).</li><li>Kaya-Okur, H. 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=CUT&amp;Tag+for+efficient+epigenomic+profiling+of+small+samples+and+single+cells.">CUT&amp;Tag for efficient epigenomic profiling of small samples and single cells.</a> Nature Communications. 10 (1), 1930 (2019).</li><li>Rodriguez, D. L., Quail, M. M., Hernday, A. D., Nobile, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcriptional+circuits+regulating+developmental+processes+in+Candida+albicans.">Transcriptional circuits regulating developmental processes in Candida albicans.</a> Frontiers in Cellular and Infection Microbiology. 10, 605711 (2020).</li><li>Zhu, Q., Liu, N., Orkin, S. H., Yuan, G. -. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CUT&amp;amp;RUNTools:+a+flexible+pipeline+for+CUT&amp;amp;RUN+processing+and+footprint+analysis.">CUT&amp;amp;RUNTools: a flexible pipeline for CUT&amp;amp;RUN processing and footprint analysis.</a> Genome Biology. 20 (1), 192 (2019).</li><li>Teytelman, L., Thurtle, D. M., Rine, J., Van Oudenaarden, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+expressed+loci+are+vulnerable+to+misleading+ChIP+localization+of+multiple+unrelated+proteins.">Highly expressed loci are vulnerable to misleading ChIP localization of multiple unrelated proteins.</a> Proceedings of the National Academy of Sciences of the United States of America. 110 (46), 18602-18607 (2013).</li></ol>

This protocol presents a comprehensive experimental and computational pipeline for genome-wide localization of regulatory TFs in C. albicans. It is designed to be highly accessible to anyone with standard microbiology and molecular biology training. By leveraging the high dynamic range and low sample input requirements of the CUT&amp;RUN assay and including optimizations for the localization of TF-DNA binding interactions in C. albicans biofilm and planktonic cultures, this protocol presents a powerful ...

Candida albicans is a clinically relevant, polymorphic human fungal pathogen that exists in a variety of different modes of growth, such as the planktonic (free-floating) mode of growth and as communities of tightly adhered cells protected by an extracellular matrix, known as the biofilm mode of growth1,2,3. Similar to other developmental and cellular processes, biofilm development is an important C. albicans virulence trait that is known to be controlled at the transcriptional level by regulatory transcription factors (TFs) that bind to DNA in a sequence-specific manner4. Recently, chromatin regulators and histone modifiers have also emerged as important regulators of C. albicans biofilm formation5 and morphogenesis6 by mediating DNA accessibility. To understand the complex biology of this important fungal pathogen, effective methods to determine the genome-wide localization of specific TFs during distinct developmental and cellular processes is valuable.
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a widely used method to investigate protein-DNA interactions in C. albicans5,6 and has largely replaced the more classical chromatin immunoprecipitation followed by microarray (ChIP-chip)9 method. Both ChIP-seq and ChIP-chip methods, however, require a large number of input cells10, which can be a complicating factor when investigating TFs in the context of specific samples and modes of growth, such as biofilms collected from patients or animal models of infection. In addition, the chromatin immunoprecipitation (ChIP) assay often yields a significant amount of background signal throughout the genome, requiring a high level of enrichment for the target of interest to sufficiently separate signal from noise. While the ChIP-chip assay is largely outdated today, the sequencing depths necessary for ChIP-seq make this assay prohibitively expensive for many researchers, particularly those studying multiple TFs and/or chromatin-associated proteins.

Cleavage under targets and release using nuclease (CUT&#38;RUN) is an attractive alternative to ChIP-seq. It was developed by the Henikoff lab in 2017 to circumvent the limitations of ChIP-seq and chromatin endogenous cleavage followed by sequencing ChEC-seq11,12, another method to identify protein-DNA interactions on a genome-wide level, while providing high-resolution, genome-wide mapping of TFs and chromatin-associated proteins13. CUT&#38;RUN relies on the targeted digestion of chromatin within permeabilized nuclei using tethered micrococcal nucleases, followed by sequencing of the digested DNA fragments9,10. As DNA fragments are specifically generated at the loci that are bound by a protein of interest, rather than being generated throughout the genome via random fragmentation as in ChIP assays, the CUT&#38;RUN approach results in greatly reduced background signals and, thus, requires 1/10th&#160;of the sequencing depth compared to ChIP-seq11,13,14. These improvements ultimately lead to significant reductions in sequencing costs and reductions in the total number of input cells needed as starting material for each sample.
Here, a robust CUT&#38;RUN protocol is described that has been adapted and optimized for determining the genome-wide localization of TFs in C. albicans cells isolated from biofilms and planktonic cultures. A thorough data analysis pipeline is also presented, which enables the processing and analysis of the resulting sequence data and requires users to have minimal expertise in coding or bioinformatics. Briefly, this protocol describes epitope tagging of TF-coding genes, harvesting of biofilm and planktonic cells, isolation of intact permeabilized nuclei, incubation with primary antibodies against the specific protein or epitope-tagged protein of interest, tethering of the chimeric A/G-micrococcal nuclease (pAG-MNase) fusion proteins to the primary antibodies, genomic DNA recovery after chromatin digestion, and preparation of genomic DNA libraries for sequencing.
The experimental CUT&#38;RUN protocol is followed by a purpose-built data analysis pipeline, which takes raw DNA sequencing reads in FASTQ format and implements all required processing steps to provide a complete list of significantly enriched loci bound by the TF of interest (targeted by the primary antibody). Note that multiple steps of the described library preparation protocol have been specifically adapted and optimized for CUT&#38;RUN analysis of TFs (as opposed to nucleosomes). While the data presented in this manuscript were generated using TF-specific adaptations of a commercial CUT&#38;RUN kit, these protocols have also been validated using individually sourced components (i.e., pAG-MNase enzyme and magnetic DNA purification beads) and in-house-prepared buffers, which can significantly reduce experimental cost. The comprehensive experimental and data analysis protocols are described in detail below in a step-by-step format. All reagents and critical equipment, as well as buffer and media recipes, are listed in the Table of Materials and Supplementary File 1, respectively.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>0.22 &#956;m filter</td>
			<td>Millipore Sigma</td>
			<td>SLGPM33RS</td>
			<td></td>
		</tr>
		<tr>
			<td>0.65 mL low-adhesion tubes</td>
			<td>VWR</td>
			<td>490003-190</td>
			<td></td>
		</tr>
		<tr>
			<td>1 M CaCl2</td>
			<td>Fisher Scientific</td>
			<td>50-152-341</td>
			<td></td>
		</tr>
		<tr>
			<td>1 M PIPES</td>
			<td>Fisher Scientific</td>
			<td>AAJ61224AK</td>
			<td></td>
		</tr>
		<tr>
			<td>12-well untreated cell culture plates</td>
			<td>Corning</td>
			<td>351143</td>
			<td></td>
		</tr>
		<tr>
			<td>2-mercaptoethanol</td>
			<td>Sigma-Aldrich</td>
			<td>60-24-2</td>
			<td></td>
		</tr>
		<tr>
			<td>2% Digitonin</td>
			<td>Fisher Scientific</td>
			<td>CHR103MI</td>
			<td></td>
		</tr>
		<tr>
			<td>50 mL conical tubes</td>
			<td>VWR</td>
			<td>89039-658</td>
			<td></td>
		</tr>
		<tr>
			<td>5x phusion HF buffer</td>
			<td>Fisher Scientific</td>
			<td>F530S</td>
			<td>Item part of the Phusion high fidelity DNA polymerase; referred to in text as &#34;DNA polymerase buffer&#34;</td>
		</tr>
		<tr>
			<td>Agar</td>
			<td>Criterion</td>
			<td>C5001</td>
			<td></td>
		</tr>
		<tr>
			<td>Agencourt AMPure XP magnetic beads</td>
			<td>Beckman Coulter</td>
			<td>A63880</td>
			<td></td>
		</tr>
		<tr>
			<td>Agilent Bioanalyzer</td>
			<td>Agilent</td>
			<td>G2939BA</td>
			<td>Referred to in the text as &#34;capillary electrophoresis instrument&#34;; user-dependepent</td>
		</tr>
		<tr>
			<td>Amplitube PCR reaction strips with attached caps, Simport Scientific</td>
			<td>VWR</td>
			<td>89133-910</td>
			<td></td>
		</tr>
		<tr>
			<td>Bacto peptone</td>
			<td>BD Biosciences</td>
			<td>211677</td>
			<td></td>
		</tr>
		<tr>
			<td>Benchling primer design tool</td>
			<td>Benchling</td>
			<td></td>
			<td>https://www.benchling.com/molecular-biology/; Referred to in the text as &#34;the primer design tool&#34;</td>
		</tr>
		<tr>
			<td>Betaine</td>
			<td>Fisher Scientific</td>
			<td>AAJ77507AB</td>
			<td></td>
		</tr>
		<tr>
			<td>Calcofluor white stain</td>
			<td>Sigma-Aldrich</td>
			<td>18909-100ML-F</td>
			<td></td>
		</tr>
		<tr>
			<td>Candida Genome Database</td>
			<td></td>
			<td></td>
			<td>http://www.candidagenome.org/</td>
		</tr>
		<tr>
			<td>Concanavalin A (ConA) conjugated paramagnetic beads</td>
			<td>Polysciences</td>
			<td>&#160;86057-3</td>
			<td></td>
		</tr>
		<tr>
			<td>Conda software</td>
			<td></td>
			<td></td>
			<td>https://docs.conda.io/en/latest/miniconda.html</td>
		</tr>
		<tr>
			<td>curl tool</td>
			<td></td>
			<td></td>
			<td>http://www.candidagenome.org/download/sequence/C_albicans_SC5314/Assembly21/current/C_albicans_SC5314_A21_current_ 
			chromosomes.fasta.gz</td>
		</tr>
		<tr>
			<td>CUTANA ChIC/CUT&#38;RUN kit</td>
			<td>Epicypher</td>
			<td>14-1048</td>
			<td>Referred to in the text as &#34;the CUT&#38;RUN kit&#34;</td>
		</tr>
		<tr>
			<td>Deoxynucleotide (dNTP) solution mix (10 mM)</td>
			<td>New England Biolabs</td>
			<td>N0447S</td>
			<td></td>
		</tr>
		<tr>
			<td>Dextrose (D-glucose)</td>
			<td>Fisher Scientific</td>
			<td>D163</td>
			<td></td>
		</tr>
		<tr>
			<td>Difco D-mannitol</td>
			<td>&#160;BD Biosciences</td>
			<td>217020</td>
			<td></td>
		</tr>
		<tr>
			<td>Disposable cuvettes</td>
			<td>Fisher Scientific</td>
			<td>14-955-127</td>
			<td></td>
		</tr>
		<tr>
			<td>Disposable transfer pipets</td>
			<td>Fisher Scientific</td>
			<td>13-711-20</td>
			<td></td>
		</tr>
		<tr>
			<td>DNA Gel Loading Dye (6x)</td>
			<td>Fisher Scientific</td>
			<td>R0611</td>
			<td></td>
		</tr>
		<tr>
			<td>DreamTaq green DNA polymerase</td>
			<td>Fisher Scientific</td>
			<td>EP0713</td>
			<td>Referred to in the text as &#34;cPCR DNA polymerase&#34;</td>
		</tr>
		<tr>
			<td>DreamTaq green DNA polymerase buffer</td>
			<td>Fisher Scientific</td>
			<td>EP0713</td>
			<td>Item part of the DreamTaq green DNA polymerase; referred to in the text as &#34;cPCR DNA polymerase buffer&#34;</td>
		</tr>
		<tr>
			<td>E. coli spike-in DNA</td>
			<td>Epicypher</td>
			<td>18-1401</td>
			<td></td>
		</tr>
		<tr>
			<td>ELMI Microplate incubator</td>
			<td>ELMI</td>
			<td>TRMS-04</td>
			<td>Referred to in the text as &#34;microplate incubator&#34;</td>
		</tr>
		<tr>
			<td>End Prep Enzyme Mix</td>
			<td></td>
			<td></td>
			<td>Item part of the NEBNext Ultra II DNA Library Prep kit</td>
		</tr>
		<tr>
			<td>End Prep Reaction Buffer</td>
			<td></td>
			<td></td>
			<td>Item part of the NEBNext Ultra II DNA Library Prep kit</td>
		</tr>
		<tr>
			<td>Ethanol 200 proof</td>
			<td>VWR</td>
			<td>89125-170</td>
			<td></td>
		</tr>
		<tr>
			<td>FastDigest MssI</td>
			<td>Fisher Scientific</td>
			<td>FD1344</td>
			<td>Referred to in the text as &#34;restriction enzyme&#34;</td>
		</tr>
		<tr>
			<td>FastDigest MssI Buffer</td>
			<td>Fisher Scientific</td>
			<td>FD1344</td>
			<td>Item part of the FastDigest MssI kit; referred to in the text as &#34;restriction enzyme buffer&#34;</td>
		</tr>
		<tr>
			<td>Ficoll 400</td>
			<td>Fisher BioReagents</td>
			<td>BP525-25</td>
			<td></td>
		</tr>
		<tr>
			<td>Fluorescence microscope</td>
			<td></td>
			<td></td>
			<td>User-dependent</td>
		</tr>
		<tr>
			<td>Gel electrophoresis apparatus</td>
			<td></td>
			<td></td>
			<td>User-dependent</td>
		</tr>
		<tr>
			<td>GeneRuler low range DNA ladder</td>
			<td>Fisher Scientific</td>
			<td>FERSM1192</td>
			<td></td>
		</tr>
		<tr>
			<td>GitBash workflow</td>
			<td></td>
			<td></td>
			<td>https://gitforwindows.org/</td>
		</tr>
		<tr>
			<td>GitHub source code</td>
			<td></td>
			<td></td>
			<td>https://github.com/akshayparopkari/cut_run_analysis</td>
		</tr>
		<tr>
			<td>HEPES-KOH pH 7.5</td>
			<td>Boston BioProducts</td>
			<td>BBH-75-K</td>
			<td></td>
		</tr>
		<tr>
			<td>High-speed centrifuge</td>
			<td></td>
			<td></td>
			<td>User-dependent</td>
		</tr>
		<tr>
			<td>Isopropanol</td>
			<td>Sigma-Aldrich</td>
			<td>PX1830-4</td>
			<td></td>
		</tr>
		<tr>
			<td>Lens paper</td>
			<td>VWR</td>
			<td>52846-001</td>
			<td></td>
		</tr>
		<tr>
			<td>Ligation Enhancer</td>
			<td></td>
			<td></td>
			<td>Item part of the NEBNext Ultra II DNA Library Prep kit</td>
		</tr>
		<tr>
			<td>Lithium acetate dihydrate</td>
			<td>MP Biomedicals</td>
			<td>215525683</td>
			<td></td>
		</tr>
		<tr>
			<td>Living Colors Full-Length GFP polyclonal antibody</td>
			<td>Takara</td>
			<td>632592</td>
			<td>User-dependent</td>
		</tr>
		<tr>
			<td>MACS2</td>
			<td></td>
			<td></td>
			<td>https://pypi.org/project/MACS2/</td>
		</tr>
		<tr>
			<td>Magnetic separation rack, 0.2 mL tubes</td>
			<td>Epicypher</td>
			<td>10-0008</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnetic separation rack, 1.5 mL tubes</td>
			<td>Fisher Scientific</td>
			<td>MR02</td>
			<td></td>
		</tr>
		<tr>
			<td>MgCl2</td>
			<td>Sigma-Aldrich</td>
			<td>M8266</td>
			<td></td>
		</tr>
		<tr>
			<td>Microcentrifuge tubes 1.5 mL</td>
			<td>Fisher Scientific</td>
			<td>05-408-129</td>
			<td></td>
		</tr>
		<tr>
			<td>Microplate and cuvette spectrophotometer</td>
			<td>BioTek</td>
			<td>EPOCH2TC</td>
			<td>Referred to in the text as &#34;spectrophotometer&#34;; user-dependent</td>
		</tr>
		<tr>
			<td>MochiView</td>
			<td></td>
			<td></td>
			<td>http://www.johnsonlab.ucsf.edu/mochiview-downloads</td>
		</tr>
		<tr>
			<td>MOPS</td>
			<td>Sigma-Aldrich</td>
			<td>M3183</td>
			<td></td>
		</tr>
		<tr>
			<td>NaCl</td>
			<td>VWR</td>
			<td>470302-522</td>
			<td></td>
		</tr>
		<tr>
			<td>NaOH</td>
			<td>Fisher Scientific</td>
			<td>S318-500</td>
			<td></td>
		</tr>
		<tr>
			<td>NCBI GEO</td>
			<td></td>
			<td></td>
			<td>https://www.ncbi.nlm.nih.gov/geo/</td>
		</tr>
		<tr>
			<td>NEBNext Adaptor for Illumina</td>
			<td></td>
			<td></td>
			<td>Item part of the NEBNext Multiplex Oligos for Illumina (Index Primers Set 1); referred to in the text as &#34;Adapter&#34;</td>
		</tr>
		<tr>
			<td>NEBNext Index X Primer for Illumina</td>
			<td></td>
			<td></td>
			<td>Item part of the NEBNext Multiplex Oligos for Illumina (Index Primers Set 1); referred to in the text as &#34;Reverse Uniquely Indexed Library Amplification Primer&#34;</td>
		</tr>
		<tr>
			<td>NEBNext Multiplex Oligos for Illumina (Index Primers Set 1)</td>
			<td>New England Biolabs</td>
			<td>E7335S</td>
			<td></td>
		</tr>
		<tr>
			<td>NEBNext Ultra II DNA Library Prep kit</td>
			<td>New England Biolabs</td>
			<td>E7645S</td>
			<td>Referred to in the text as &#34;library prep kit&#34;</td>
		</tr>
		<tr>
			<td>NEBNext Universal PCR Primer for Illumina</td>
			<td></td>
			<td></td>
			<td>Item part of the NEBNext Multiplex Oligos for Illumina (Index Primers Set 1); referred to in the text as &#34;Universal Forward Library Amplification Primer&#34;</td>
		</tr>
		<tr>
			<td>Nourseothricin sulfate (NAT)</td>
			<td>Goldbio</td>
			<td>N-500-2</td>
			<td></td>
		</tr>
		<tr>
			<td>Novex TBE Gels, 10%, 15 well</td>
			<td>Fisher Scientific</td>
			<td>EC62755BOX</td>
			<td></td>
		</tr>
		<tr>
			<td>Nutating mixer</td>
			<td>VWR</td>
			<td>82007-202</td>
			<td></td>
		</tr>
		<tr>
			<td>Nutrient broth</td>
			<td>Criterion</td>
			<td>C6471</td>
			<td></td>
		</tr>
		<tr>
			<td>pADH110</td>
			<td>Addgene</td>
			<td>90982</td>
			<td>Referred to in the text as &#34;plasmid repository ID# 90982&#34;</td>
		</tr>
		<tr>
			<td>pADH119</td>
			<td>Addgene</td>
			<td>90985</td>
			<td>Referred to in the text as &#34;plasmid repository ID# 90985&#34;</td>
		</tr>
		<tr>
			<td>pADH137</td>
			<td>Addgene</td>
			<td>90986</td>
			<td>Referred to in the text as &#34;plasmid repository ID# 90986&#34;</td>
		</tr>
		<tr>
			<td>pADH139</td>
			<td>Addgene</td>
			<td>90987</td>
			<td>Referred to in the text as &#34;plasmid repository ID# 90987&#34;</td>
		</tr>
		<tr>
			<td>pADH140</td>
			<td>Addgene</td>
			<td>90988</td>
			<td>Referred to in the text as &#34;plasmid repository ID# 90988&#34;</td>
		</tr>
		<tr>
			<td>pAG-MNase</td>
			<td>Epicypher</td>
			<td>15-1016 or 15-1116</td>
			<td>50 rxn or 250 rxn</td>
		</tr>
		<tr>
			<td>pCE1</td>
			<td>Addgene</td>
			<td>174434</td>
			<td>Referred to in the text as &#34;plasmid repository ID# 174434&#34;</td>
		</tr>
		<tr>
			<td>Petri dishes with clear lid</td>
			<td>Fisher Scientific</td>
			<td>FB0875712</td>
			<td></td>
		</tr>
		<tr>
			<td>Phusion high fidelity DNA polymerase</td>
			<td>Fisher Scientific</td>
			<td>F530S</td>
			<td>Referred to in the text as &#34;DNA polymerase&#34;</td>
		</tr>
		<tr>
			<td>Polyethylene glycol (PEG) 3350</td>
			<td>VWR</td>
			<td>10791-816</td>
			<td></td>
		</tr>
		<tr>
			<td>Potassium phosphate monobasic</td>
			<td>Fisher Scientific</td>
			<td>P285-500</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit 1x dsDNA HS assay kit</td>
			<td>Invitrogen</td>
			<td>Q33230</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit fluorometer</td>
			<td>Life Technologies</td>
			<td>Q33216</td>
			<td>Referred to in the text as &#34;fluorometer&#34;; user-dependent</td>
		</tr>
		<tr>
			<td>Rabbit IgG negative control antibody</td>
			<td>Epicypher</td>
			<td>13-0042</td>
			<td></td>
		</tr>
		<tr>
			<td>RNase A</td>
			<td>Sigma-Aldrich</td>
			<td>10109169001</td>
			<td></td>
		</tr>
		<tr>
			<td>Roche Complete protease inhibitor (EDTA-free) tablets</td>
			<td>Sigma-Aldrich</td>
			<td>5056489001</td>
			<td></td>
		</tr>
		<tr>
			<td>RPMI-1640</td>
			<td>Sigma-Aldrich</td>
			<td>R6504</td>
			<td></td>
		</tr>
		<tr>
			<td>Shaking incubator</td>
			<td>Eppendorf</td>
			<td>M12820004</td>
			<td>User-dependent</td>
		</tr>
		<tr>
			<td>Sorbitol</td>
			<td>Sigma-Aldrich</td>
			<td>S1876-500G</td>
			<td></td>
		</tr>
		<tr>
			<td>Spin-X centrifuge tube filters</td>
			<td>Fisher Scientific</td>
			<td>07-200-385</td>
			<td></td>
		</tr>
		<tr>
			<td>Sterile inoculating loops</td>
			<td>VWR</td>
			<td>30002-094</td>
			<td></td>
		</tr>
		<tr>
			<td>SYBR Gold nucleic acid gel stain</td>
			<td>Fisher Scientific</td>
			<td>S11494</td>
			<td></td>
		</tr>
		<tr>
			<td>SYTO 13 nucleic acid stain</td>
			<td>Fisher Scientific</td>
			<td>S7575</td>
			<td>Referred to in the text as &#34;nucleic acid gel stain&#34;</td>
		</tr>
		<tr>
			<td>Thermocycler</td>
			<td></td>
			<td></td>
			<td>User-dependent</td>
		</tr>
		<tr>
			<td>ThermoMixer C</td>
			<td>Eppendorf</td>
			<td>5382000023</td>
			<td></td>
		</tr>
		<tr>
			<td>Tris (hydroxymethyl) aminomethane</td>
			<td>Sigma-Aldrich</td>
			<td>252859-100G</td>
			<td></td>
		</tr>
		<tr>
			<td>Ultra II Ligation Master Mix</td>
			<td></td>
			<td></td>
			<td>Item part of the NEBNext Ultra II DNA Library Prep kit; referred to in the text as &#34;Ligation Master Mix&#34;</td>
		</tr>
		<tr>
			<td>Ultra II Q5 Master Mix</td>
			<td></td>
			<td></td>
			<td>Item part of the NEBNext Ultra II DNA Library Prep kit; referred to in the text as &#34;High Fidelity DNA Polymerase Master Mix &#34;</td>
		</tr>
		<tr>
			<td>UltraPure salmon sperm DNA solution</td>
			<td>Invitrogen</td>
			<td>15632011</td>
			<td></td>
		</tr>
		<tr>
			<td>USER Enzyme</td>
			<td></td>
			<td></td>
			<td>Item part of the NEBNext Ultra II DNA Library Prep kit; referred to in the text as &#34;Uracil Excision Enzyme&#34;</td>
		</tr>
		<tr>
			<td>Vortex mixer</td>
			<td>VWR</td>
			<td>10153-834</td>
			<td></td>
		</tr>
		<tr>
			<td>wget tool</td>
			<td></td>
			<td></td>
			<td>http://www.candidagenome.org/download/sequence/C_albicans_SC5314/Assembly21/current/C_albicans_SC5314_A21_current_ 
			chromosomes.fasta.gz</td>
		</tr>
		<tr>
			<td>Yeast extract</td>
			<td>Criterion</td>
			<td>C7341</td>
			<td></td>
		</tr>
		<tr>
			<td>Zymolyase 100T (lyticase, yeast lytic enzyme)</td>
			<td>Fisher Scientific</td>
			<td>NC0439194</td>
			<td></td>
		</tr>
	</tbody>
</table>

genome-wide profiling of transcription factor-dna binding interactions in candida albicans: a comprehensive cut&amp;run method and data analysis workflow

Regulatory transcription factors control many important biological processes, including cellular differentiation, responses to environmental perturbations and stresses, and host-pathogen interactions. Determining the genome-wide binding of regulatory transcription factors to DNA is essential to understanding the function of transcription factors in these often complex biological processes. Cleavage under targets and release using nuclease (CUT&amp;RUN) is a modern method for genome-wide mapping of in vivo protein-DNA binding interactions that is an attractive alternative to the traditional and widely used chromatin immunoprecipitation followed by sequencing (ChIP-seq) method. CUT&amp;RUN is amenable to a higher-throughput experimental setup and has a substantially higher dynamic range with lower per-sample sequencing costs than ChIP-seq. Here, a comprehensive CUT&amp;RUN protocol and accompanying data analysis workflow tailored for genome-wide analysis of transcription factor-DNA binding interactions in the human fungal pathogen Candida albicans are described. This detailed protocol includes all necessary experimental procedures, from epitope tagging of transcription factor-coding genes to library preparation for sequencing; additionally, it includes a customized computational workflow for CUT&amp;RUN data analysis.

This robust CUT&#38;RUN protocol was adapted and optimized for investigating the genome-wide localization of specific TFs in C. albicans biofilms and planktonic cultures (see Figure 2 for an overview of the experimental approach). A thorough data analysis pipeline is also included to facilitate analysis of the resulting CUT&#38;RUN sequencing data and requires users to have minimal expertise in coding or bioinformatics (see Figure 3 for an overview of t...

Watch this Scientific Journal Video about Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow at JoVE.

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&amp;RUN Method and Data Analysis Workflow

This protocol describes an experimental method and data analysis workflow for cleavage under targets and release using nuclease (CUT&amp;RUN) in the human fungal pathogen Candida albicans.

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&amp;RUN Method and Data Analysis Workflow

Regulatory transcription factors control many important biological processes, including cellular differentiation, responses to environmental perturbations ...

This protocol details all experimental procedures, from epitope tagging of transcription factor coding genes through computational data analysis, to perform genome-wide profiling of transcription factor-DNA binding interactions in Candida albicans via CUT&RUN. This is a more accessible, faster, more sensitive, less expensive technique, and provides higher quality data than traditional ChIP-chip and ChIP-seq approaches. Although our protocol is developed for Candida albicans cells isolated from biofilms or planktonic cultures, it should be practical to adapt it to virtually any good form of Candida albicans cells.After incubating the cell suspension, transfer a five-microliter aliquot into a new PCR tube. To five microliters of isolated nuclei and a five-microliter aliquot of intact cells previously stored at four degree Celsius, add one microliter each of calcofluor-white and SYTO 13. Incubate at 30 degrees Celsius in the dark for 30 minutes.Visually inspect the integrity and purity of the isolated nuclei using a fluorescence microscope. Look for isolated nuclei showing prominent SYTO 13 staining and ensure no cell wall staining by the calcofluor-white dye. Repeat the inspection with intact cells as a control for cell wall staining with calcofluor-white dye.Place the tubes on a magnetic rack and wait until the slurry is completely clear. Discard the supernatant using a pipette. Add 50 microliters of the antibody buffer and gently mix by pipetting.Add three microliters of the anti-GFP polyclonal antibody to the tubes and incubate the tubes on a nutating mixer at 4 degrees Celsius for two hours. Briefly centrifuge the tubes at 100 times g at room temperature for five seconds and place the tubes on a magnetic rack. Once the slurry is clear, discard the supernatant using a pipette.While the tubes with the beads are still on the magnetic rack, add 200 microliters of ice-cold cell permeabilization buffer directly onto the beads. Discard the supernatant using a pipette. Repeat two washes with the ice-cold cell permeabilization buffer.Add 50 microliters of ice-cold cell permeabilization buffer to each tube and gently mix by pipetting. Add 2.5 microliters of the protein AG-MNase to each sample and mix by pipetting. Place the samples on a nutating mixer at 4 degrees Celsius and incubate the samples for one hour.Briefly centrifuge the strip tubes at 100 times g at room temperature for five seconds, then place the tubes on a magnetic rack. Once the slurry is clear, discard the supernatant using a pipette. While the tubes with the beads are still on the magnetic rack, add 200 microliters of ice-cold cell permeabilization buffer directly onto the beads.Discard the supernatant using a pipette. Repeat two washes with the ice-cold cell permeabilization buffer. Add 100 microliters of the ice-cold cell permeabilization buffer to the samples and gently pipette up and down five times.Remove the gel cast from the gel box and open the gel cast per the manufacturer's instructions. Gently remove the gel from the gel cast and place it inside a gel-holding tray containing 100 milliliters of single-strength TBE. Add 10 microliters of the nucleic acid gel stain to the tray and gently swirl.Cover with foil to protect from light and incubate statically at room temperature for 10 minutes. Then rinse the gel twice with 100 milliliters of deionized tap water. Image the gel under blue light illumination using an amber filter cover.For each library, cut the gel slightly above the approximately 125 base pair prominent adapter dimer band and below the 400 base pair ladder mark. Puncture the bottom of a 0.65-milliliter tube using a 22-gauge needle and place the punctured tube inside a sterile two-milliliter microfuge tube. Transfer the gel slice to the punctured tube inside the two-milliliter microfuge tube.Centrifuge the two-milliliter microfuge tube containing the 0.65-milliliter punctured tube and sample at 10, 000 times g at room temperature for two minutes to collect the gel slurry inside the two-milliliter microfuge tube. Add 300 microliters of ice-cold gel elution buffer to the gel slurry. Mix on a nutator at room temperature for a minimum of three hours or overnight.Transfer all liquid and gel slurry to a 0.22 micrometer filter column and centrifuge at 10, 000 times g at room temperature for one minute. Download the source code for the CUT&RUN analysis from the GitHub page by clicking on the green Code button, followed by Download ZIP option. Unzip the folder to a relevant location on the local machine.Install Conda Environment and run only once. Once Conda is installed, create a virtual environment provided with the relevant command. Activate the virtual environment every time this workflow is executed.Cell wall digestion and nuclear integrity were assessed by visualizing both control intact cells and isolated nuclei stained with fluorescent cell wall and nucleic acid stains. In contrast to the isolated intact nuclei where cell wall staining is not observed, both the nuclei and the cell walls are fluorescently labeled in the intact control cells. The figure shows CUT&RUN TF libraries analyzed using a capillary electrophoresis instrument.Successful CUT&RUN TF libraries are enriched for short fragments smaller than 200 base pairs. Suboptimal CUT&RUN TF libraries show enrichment for large DNA fragments. The figure herein shows that Ndt80 DNA-binding motifs are enriched across all the Ndt80-bound loci identified by CUT&RUN, indicating that the additional peaks identified by this methodology are likely bonafide Ndt80-bound sites.Comparative analysis indicated that the CUT&RUN protocol identified most of the previously known binding events for Ndt80 and Efg1 during biofilm formation. Overall, both Ndt80 and Efg1 bound to loci overlap with previously published chromatin immune precipitation ChIP data and are identified only using the CUT&RUN. It enables us to significantly increase the scope and pace of our research focused on transcriptional regulatory networks and Candida albicans.

genome-wide-profiling-transcription-factor-dna-binding-interactions

Research

JoVE Journal

Biology

Genome-wide Analysis of Aminoacylation (Charging) Levels of tRNA Using Microarrays

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both prokaryotic and eukaryotic cells lead to translational regulation which is a major cellular mechanism of stress response. Familiar examples are the stringent response in E. coli and the Gcn2 stress response pathway in yeast ([2-6]). Recent work in E. coli and S. cerevisiae have shown that tRNA charging patterns are highly dynamic and depends on the type of stress experienced by cells [1, 6, 7]. The highly dynamic, variable nature of tRNA charging makes it essential to determine changes in tRNA charging levels at the genomic scale, in order to fully elucidate cellular response to environmental variations. In this review we present a method for simultaneously measuring the relative charging levels of all tRNAs in S. cerevisiae . While the protocol presented here is for yeast, this protocol has been successfully applied for determining relative charging levels in a wide variety of organisms including E. coli and human cell cultures[7, 8].

Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays

We describe a method for microarray analysis to determine relative aminoacylation levels of all tRNAs from S. cerivisiae.

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both ...

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

A Quantitative Fitness Analysis Workflow

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. QFA can be applied to focused observations of single cultures but is most useful for genome-wide genetic interaction or drug screens investigating up to thousands of independent cultures. The central experimental method is the inoculation of independent, dilute liquid microbial cultures onto solid agar plates which are incubated and regularly photographed. Photographs from each time-point are analyzed, producing quantitative cell density estimates, which are used to construct growth curves, allowing quantitative fitness measures to be derived. Culture fitnesses can be compared to quantify and rank genetic interaction strengths or drug sensitivities. The effect on culture fitness of any treatments added into substrate agar (e.g. small molecules, antibiotics or nutrients) or applied to plates externally (e.g. UV irradiation, temperature) can be quantified by QFA.
The QFA workflow produces growth rate estimates analogous to those obtained by spectrophotometric measurement of parallel liquid cultures in 96-well or 200-well plate readers. Importantly, QFA has significantly higher throughput compared with such methods. QFA cultures grow on a solid agar surface and are therefore well aerated during growth without the need for stirring or shaking.
QFA throughput is not as high as that of some Synthetic Genetic Array (SGA) screening methods5,6. However, since QFA cultures are heavily diluted before being inoculated onto agar, QFA can capture more complete growth curves, including exponential and saturation phases3. For example, growth curve observations allow culture doubling times to be estimated directly with high precision, as discussed previously1.
Here we present a specific QFA protocol applied to thousands of S. cerevisiae cultures which are automatically handled by robots during inoculation, incubation and imaging. Any of these automated steps can be replaced by an equivalent, manual procedure, with an associated reduction in throughput, and we also present a lower throughput manual protocol. The same QFA software tools can be applied to images captured in either workflow.
We have extensive experience applying QFA to cultures of the budding yeast S. cerevisiae but we expect that QFA will prove equally useful for examining cultures of the fission yeast S. pombe and bacterial cultures.

Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. ...

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work.
Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized.
Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types.
To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.

Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of ...

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Transposon mutagenesis and single-gene deletion are two methods applied in genome-wide gene knockout in bacteria 1,2. Although transposon mutagenesis is less time consuming, less costly, and does not require completed genome information, there are two weaknesses in this method: (1) the possibility of a disparate mutants in the mixed mutant library that counter-selects mutants with decreased competition; and (2) the possibility of partial gene inactivation whereby genes do not entirely lose their function following the insertion of a transposon. Single-gene deletion analysis may compensate for the drawbacks associated with transposon mutagenesis. To improve the efficiency of genome-wide single gene deletion, we attempt to establish a high-throughput technique for genome-wide single gene deletion using Streptococcus sanguinis as a model organism. Each gene deletion construct in S. sanguinis genome is designed to comprise 1-kb upstream of the targeted gene, the aphA-3 gene, encoding kanamycin resistance protein, and 1-kb downstream of the targeted gene. Three sets of primers F1/R1, F2/R2, and F3/R3, respectively, are designed and synthesized in a 96-well plate format for PCR-amplifications of those three components of each deletion construct. Primers R1 and F3 contain 25-bp sequences that are complementary to regions of the aphA-3 gene at their 5' end. A large scale PCR amplification of the aphA-3 gene is performed once for creating all single-gene deletion constructs. The promoter of aphA-3 gene is initially excluded to minimize the potential polar effect of kanamycin cassette. To create the gene deletion constructs, high-throughput PCR amplification and purification are performed in a 96-well plate format. A linear recombinant PCR amplicon for each gene deletion will be made up through four PCR reactions using high-fidelity DNA polymerase. The initial exponential growth phase of S. sanguinis cultured in Todd Hewitt broth supplemented with 2.5% inactivated horse serum is used to increase competence for the transformation of PCR-recombinant constructs. Under this condition, up to 20% of S. sanguinis cells can be transformed using ~50 ng of DNA. Based on this approach, 2,048 mutants with single-gene deletion were ultimately obtained from the 2,270 genes in S. sanguinis excluding four gene ORFs contained entirely within other ORFs in S. sanguinis SK36 and 218 potential essential genes. The technique on creating gene deletion constructs is high throughput and could be easy to use in genome-wide single gene deletions for any transformable bacteria.

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

An efficient genome-wide single gene mutation method has been established using Streptococcus sanguinis as a model organism. This method has achieved via high throughput recombinant PCRs and transformations.

Transposon  mutagenesis and single-gene deletion are two methods applied in genome-wide  gene knockout in bacteria 1,2. Although transposon mutagenesis is ...

Polysome Fractionation and Analysis of Mammalian Translatomes on a Genome-wide Scale

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. Accordingly, dysregulation of mRNA translation is considered to play a major role in a variety of pathological states including cancer. Ribosomes also host chaperones, which facilitate folding of nascent polypeptides, thereby modulating function and stability of newly synthesized polypeptides. In addition, emerging data indicate that ribosomes serve as a platform for a repertoire of signaling molecules, which are implicated in a variety of post-translational modifications of newly synthesized polypeptides as they emerge from the ribosome, and/or components of translational machinery. Herein, a well-established method of ribosome fractionation using sucrose density gradient centrifugation is described. In conjunction with the in-house developed &#8220;anota&#8221; algorithm this method allows direct determination of differential translation of individual mRNAs on a genome-wide scale. Moreover, this versatile protocol can be used for a variety of biochemical studies aiming to dissect the function of ribosome-associated protein complexes, including those that play a central role in folding and degradation of newly synthesized polypeptides.

Ribosomes play a central role in protein synthesis. Polyribosome (polysome) fractionation by sucrose density gradient centrifugation allows direct determination of translation efficiencies of individual mRNAs on a genome-wide scale. In addition, this method can be used for biochemical analysis of ribosome- and polysome-associated factors such as chaperones and signaling molecules.

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. ...

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC (Crosslinking of Small Molecules to Isolate Chromatin)

The genome is the target of some of the most effective chemotherapeutics, but most of these drugs lack DNA sequence specificity, which leads to dose-limiting toxicity and many adverse side effects. Targeting the genome with sequence-specific small molecules may enable molecules with increased therapeutic index and fewer off-target effects. N-methylpyrrole/N-methylimidazole polyamides are molecules that can be rationally designed to target specific DNA sequences with exquisite precision. And unlike most natural transcription factors, polyamides can bind to methylated and chromatinized DNA without a loss in affinity. The sequence specificity of polyamides has been extensively studied in vitro with cognate site identification (CSI) and with traditional biochemical and biophysical approaches, but the study of polyamide binding to genomic targets in cells remains elusive. Here we report a method, the crosslinking of small molecules to isolate chromatin (COSMIC), that identifies polyamide binding sites across the genome. COSMIC is similar to chromatin immunoprecipitation (ChIP), but differs in two important ways: (1) a photocrosslinker is employed to enable selective, temporally-controlled capture of polyamide binding events, and (2) the biotin affinity handle is used to purify polyamide&#8211;DNA conjugates under semi-denaturing conditions to decrease DNA that is non-covalently bound. COSMIC is a general strategy that can be used to reveal the genome-wide binding events of polyamides and other genome-targeting chemotherapeutic agents.

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC Crosslinking of Small Molecules to Isolate Chromatin

Identifying the direct targets of genome-targeting molecules remains a major challenge. To understand how DNA-binding molecules engage the genome, we developed a method that relies on crosslinking of small molecules to isolate chromatin (COSMIC).

The genome is the target of some of the most effective chemotherapeutics, but most of these drugs lack DNA sequence specificity, which leads to ...

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone function is mediated by extensive post-translational modification by a myriad of nuclear proteins. These modifications are critical for nuclear integrity as they regulate chromatin structure and recruit enzymes involved in gene regulation, DNA repair and chromosome condensation. Even though a large part of the scientific community adopts antibody-based techniques to characterize histone PTM abundance, these approaches are low throughput and biased against hypermodified proteins, as the epitope might be obstructed by nearby modifications. This protocol describes the use of nano liquid chromatography (nLC) and mass spectrometry (MS) for accurate quantification of histone modifications. This method is designed to characterize a large variety of histone PTMs and the relative abundance of several histone variants within single analyses. In this protocol, histones are derivatized with propionic anhydride followed by digestion with trypsin to generate peptides of 5 - 20 aa in length. After digestion, the newly exposed N-termini of the histone peptides are derivatized to improve chromatographic retention during nLC-MS. This method allows for the relative quantification of histone PTMs spanning four orders of magnitude.

This protocol outlines a fully integrated workflow for characterizing histone post-translational modifications using mass spectrometry (MS). The workflow includes histone purification from cell cultures or tissues, histone derivatization and digestion, MS analysis using nano-flow liquid chromatography and instructions for data analysis. The protocol is designed for completion within 2 - 3 days.

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone ...

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the prediction of family functions based on several features, such as the presence of sequence motifs or of particular sites for post-translational modification and the expression profile of family members in different conditions. This work describes a detailed protocol for gene family characterization. Here, the procedure is applied to the characterization of the Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligase family in grapevine. The methods include the genome-wide identification of family members, the characterization of gene localization, structure, and duplication, the analysis of conserved protein motifs, the prediction of protein localization and phosphorylation sites as well as gene expression profiling across the family in different datasets. Such procedure, which could be extended to further analyses depending on experimental purposes, could be applied to any gene family in any plant species for which genomic data are available, and it provides valuable information to identify interesting candidates for functional studies, giving insights into the molecular mechanisms of plant adaptation to their environment.

This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligases.

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the ...

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Recent advances in mass spectrometry have resulted in deep proteomic&#160;analysis along with the generation of&#160;robust and&#160;reproducible datasets. However, despite the considerable technical advancements, sample preparation from biospecimens such as patient blood, CSF, and tissue still poses considerable challenges. For identifying biomarkers, tissue proteomics often provides an attractive sample source to translate the research findings from the bench to the clinic. It can reveal potential candidate biomarkers for early diagnosis of cancer and neurodegenerative diseases such as Alzheimer&#39;s disease, Parkinson&#39;s disease, etc. Tissue proteomics also yields a wealth of systemic information based on the abundance of proteins and helps to address interesting biological questions.
Quantitative proteomics analysis can be grouped into two broad categories: a label-based and a label-free approach. In the label-based approach, proteins or peptides are labeled using stable isotopes such as SILAC (stable isotope labeling with amino acids in cell culture) or by chemical tags such as ICAT (isotope-coded affinity tags), TMT (tandem mass tag) or iTRAQ (isobaric tag for relative and absolute quantitation). Label-based approaches have the advantage of more accurate quantitation of proteins and using isobaric labels, multiple samples can be analyzed in a single experiment. The label-free approach provides a cost-effective alternative to label-based approaches. Hundreds of patient samples belonging to a particular cohort can be analyzed and compared with other cohorts based on clinical features. Here, we have described an optimized quantitative proteomics workflow for tissue samples using label-free and label-based proteome profiling methods, which is crucial for applications in life sciences, especially biomarker discovery-based projects.

The described protocol provides an optimized quantitative proteomics analysis of tissue samples using two approaches: label-based and label free quantitation. Label-based approaches have the advantage of more accurate quantitation of proteins, while a label-free approach is more cost-effective and used to analyze hundreds of samples of a cohort.