We thank J. Roop, R. Hackley, I. Grigoriev, A. Arkin and J. Skerker for their contributions to the original study, F. AlZaben, A. Flury, G. Geiselman, J. Hong, J. Kim, M. Maurer, and L. Oltrogge for technical assistance, D. Savage for his generosity with microscopy resources, and B. Blackman, S. Coradetti, A. Flamholz, V. Guacci, D. Koshland, C. Nelson, and A. Sasikumar for discussions; we also thank J. Dueber (Department of Bioengineering, UC Berkeley) for the PiggyBac plasmid. This work was supported by R01 GM120430-A1 and by Community Sequencing Project 1460 to RBB at the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility. The work conducted by the latter was supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

<ol>
	<li>Flint, J., Mott, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Finding+the+molecular+basis+of+quantitative+traits:+successes+and+pitfalls.">Finding the molecular basis of quantitative traits: successes and pitfalls.</a> Nature Reviews Genetics. 2, 437-445 (2001).</li><li>Allen Orr, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+genetics+of+species+differences.">The genetics of species differences.</a> Trends in Ecology and Evolution. 16, 343-350 (2001).</li><li>Weiss, C. V., 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=Genetic+dissection+of+interspecific+differences+in+yeast+thermotolerance.">Genetic dissection of interspecific differences in yeast thermotolerance.</a> Nature Genetics. 50, 1501-1504 (2018).</li><li>Stern, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+loci+that+cause+phenotypic+variation+in+diverse+species+with+the+reciprocal+hemizygosity+test.">Identification of loci that cause phenotypic variation in diverse species with the reciprocal hemizygosity test.</a> Trends in Genetics. 30, 547-554 (2014).</li><li>Steinmetz, L. 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=Dissecting+the+architecture+of+a+quantitative+trait+locus+in+yeast.">Dissecting the architecture of a quantitative trait locus in yeast.</a> Nature. 416, 326-330 (2002).</li><li>Scannell, D. R., 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+Awesome+Power+of+Yeast+Evolutionary+Genetics:+New+Genome+Sequences+and+Strain+Resources+for+the+Saccharomyces+sensu+stricto+Genus.">The Awesome Power of Yeast Evolutionary Genetics: New Genome Sequences and Strain Resources for the Saccharomyces sensu stricto Genus.</a> G3 (Bethesda). 1, 11-25 (2011).</li><li>Wetmore, K. 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=Rapid+quantification+of+mutant+fitness+in+diverse+bacteria+by+sequencing+randomly+bar-coded+transposons.">Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons.</a> MBio. 6, e00306-e00315 (2015).</li><li>Love, M. I., Huber, W., Anders, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Moderated+estimation+of+fold+change+and+dispersion+for+RNA-seq+data+with+DESeq2.">Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.</a> Genome Biology. 15, 550 (2014).</li><li>Wilkening, 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=An+evaluation+of+high-throughput+approaches+to+QTL+mapping+in+Saccharomyces+cerevisiae.">An evaluation of high-throughput approaches to QTL mapping in Saccharomyces cerevisiae.</a> Genetics. 196, 853-865 (2014).</li><li>Kim, H. S., Huh, J., Riles, L., Reyes, A., Fay, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+noncomplementation+screen+for+quantitative+trait+alleles+in+saccharomyces+cerevisiae.">A noncomplementation screen for quantitative trait alleles in saccharomyces cerevisiae.</a> G3 (Bethesda). 2, 753-760 (2012).</li></ol>

The authors have nothing to disclose.

The advantages of RH-seq over previous statistical-genetic methods are several-fold. In contrast to linkage and association analysis, RH-seq affords single-gene mapping resolution; as such, it will likely be of significant utility even in studies of trait variation across individuals of a given species, as well as interspecific differences. Also, previous attempts at genome-wide reciprocal hemizygosity analysis used collections of gene deletion mutants, some of which harbor secondary mutations that can lead to false posi...

Since the dawn of the field, it has been a prime goal in genetics to understand the mechanistic basis of variation across wild individuals. As we map loci underlying a trait of interest, the emergent genes can be of immediate use as targets for diagnostics and drugs, and can shed light on the principles of evolution. The industry standard toward this end is to test for a relationship between genotype and phenotype across a population via linkage or association1. Powerful as these approaches are, they have one key limitation&mdash;they rely on large panels of recombinant progeny from crosses between interfertile individuals. They are of no use in the study of species that cannot mate to form progeny in the first place. As such, the field has had little capacity for unbiased dissection of trait differences between reproductively isolated species2.
In this work we report the technical underpinnings of a new method, RH-seq3, for genome-scale surveys of the genetic basis of trait variation between species. This approach is a massively parallel version of the reciprocal hemizygote test4,5, which was first conceived as a way to evaluate the phenotypic effects of allelic differences between two genetically distinct backgrounds at a particular locus (Figure 1A). In this scheme, the two divergent individuals are first mated to form a hybrid, half of whose genome comes from each of the respective parents. In this background, multiple strains are generated, each containing an interrupted or deleted copy of each parent&rsquo;s allele of the locus. These strains are hemizygous since they remain diploid everywhere in the genome except at the locus of interest, where they are considered haploid, and are referred to as reciprocal since each lacks only one parent&rsquo;s allele, with its remaining allele derived from the other parent. By comparing the phenotypes of these reciprocal hemizygote strains, one can conclude whether DNA sequence variants at the manipulated locus contribute to the trait of interest, since variants at the locus are the only genetic difference between the reciprocal hemizygote strains. In this way, it is possible to link genetic differences between species to a phenotypic difference between them in a well-controlled experimental setup. To date the applications of this test have been in a candidate-gene framework&mdash;that is, cases in which the hypothesis is already in hand that natural variation at a candidate locus might impact a trait.
In what follows, we lay out the protocol for a genome-scale reciprocal hemizygosity screen, using yeast as a model system. Our method creates a genomic complement of hemizygote mutants, by generating viable, sterile F1 hybrids between species and subjecting them to transposon mutagenesis. We pool the hemizygotes, measure their phenotypes in sequencing-based assays, and test for differences in frequency between clones of the pool bearing the two parents&rsquo; alleles of a given gene. The result is a catalog of loci at which variants between species influence the trait of interest. We implement the RH-seq workflow to elucidate the genetic basis of thermotolerance differences between two budding yeast species, Saccharomyces cerevisiae and S. paradoxus, which diverged ~5 million years ago6.

<table>
	<tbody>
		<tr>
			<td>1-2 plasmid Gigaprep kits</td>
			<td>Zymo Research</td>
			<td>D4204</td>
			<td>The number of kits required depends on how efficient your preps are in each kit. This kit comes with 5 individual plasmid prep columns. Run 1 L of saturated E. coli culture through each prep column, as using more than 1 L per column can cause clogging of the prep filter, leading to low yield and poor quality DNA.</td>
		</tr>
		<tr>
			<td>10X Tris-EDTA (TE) buffer (100 mM Tris-HCl and 10 mM EDTA)</td>
			<td>Any</td>
			<td>N/A</td>
			<td>Filter sterilize through a 0.22 &#956;m filter before use.</td>
		</tr>
		<tr>
			<td>1M LiOAc</td>
			<td>Any</td>
			<td>N/A</td>
			<td>Filter sterilize through a 0.22 &#956;m filter before use.</td>
		</tr>
		<tr>
			<td>300 mg/mL Geneticin (G418)</td>
			<td>Gibco</td>
			<td>11811023</td>
			<td></td>
		</tr>
		<tr>
			<td>52% polyethylene glycol (PEG) 3350</td>
			<td>Sigma</td>
			<td>1546547</td>
			<td>Dissolve in water and filter sterilize through a 0.22 &#956;m filter before use. 1X trafo mix: 228 uL 52% PEG, 36 uL 1M LiOAc, 36 uL 10X TE buffer</td>
		</tr>
		<tr>
			<td>Autoclaved LB liquid broth</td>
			<td>BD Difco</td>
			<td>244620</td>
			<td>Make LB liquid broth using your powder from any brand, and milliQ water. Autoclave it before use.</td>
		</tr>
		<tr>
			<td>Carbenicillin stock in water (100 mg/mL)</td>
			<td>Any</td>
			<td>N/A</td>
			<td>Filter sterilize through a 0.22 &#956;m filter before use.</td>
		</tr>
		<tr>
			<td>Complete synthetic agar plates (24.1cm x 24.1cm) with 5-fluoroorotic acid (5-FOA) [0.2% drop-out amino acid mix without uracil or yeast nitrogen base (YNB), 0.005% uracil , 2% D-glucose, 0.67% YNB without amino acids, 0.075% 5-FOA]</td>
			<td>5-FOA: Zymo Research, Drop-out mix: US Biological, Uracil: Sigma, D-glucose: Sigm), YNB: Difco</td>
			<td>5-FOA: F9001-5, Drop-out mix: D9535, Uracil: U0750, D-glucose: G8270, YNB: DF0919</td>
			<td></td>
		</tr>
		<tr>
			<td>DMSO</td>
			<td>Any</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>E. coli strain carrying pJR487 (CEN-/ARS+ piggyBac-containing plasmid)</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Request from Brem lab.</td>
		</tr>
		<tr>
			<td>Hybrid yeast strain JR507 (S. cerevisiae DBVPG1373 x S. paradoxus Z1, URA-/URA-)</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Request from Brem lab.</td>
		</tr>
		<tr>
			<td>Illumina Hiseq 2500</td>
			<td></td>
			<td></td>
			<td>used for SE-150 reads</td>
		</tr>
		<tr>
			<td>Large shaking incubators with variable temperature settings</td>
			<td>Any</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>LB + carbenicillin agar plates (100 &#956;g/mL)</td>
			<td>Agar: BD Difco</td>
			<td>Agar: 214010</td>
			<td>Make LB agar plates as normal and add carbenicillin to 100 &#956;g/mL before drying.</td>
		</tr>
		<tr>
			<td>Nanodrop spectrophotometer</td>
			<td>Thermo Scientific</td>
			<td>ND-2000</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit Fluorimeter</td>
			<td>Thermo Scientific</td>
			<td>Q33240</td>
			<td></td>
		</tr>
		<tr>
			<td>Salmon sperm DNA</td>
			<td>Invitrogen</td>
			<td>15632011</td>
			<td></td>
		</tr>
		<tr>
			<td>Water bath at 39&#176;C</td>
			<td>Any</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>Yeast fungal gDNA prep kit</td>
			<td>Zymo Research</td>
			<td>D6005</td>
			<td></td>
		</tr>
		<tr>
			<td>Yeast peptone dextrose (YPD) liquid media</td>
			<td>BD Difco</td>
			<td>Peptone: 211677, Yeast Extract: 212750</td>
			<td>Add filter-sterilized D-glucose to 2% after autoclaving.</td>
		</tr>
		<tr>
			<td>YPD + G418 agar plates (300 &#956;g/mL)</td>
			<td>Agar: BD Difco</td>
			<td>Agar: 214010</td>
			<td>Make YPD agar plates as normal and add G418 to 300 &#956;g/mL before drying.</td>
		</tr>
		<tr>
			<td>YPD agar plates</td>
			<td>Agar: BD Difco</td>
			<td>Agar: 214010</td>
			<td></td>
		</tr>
	</tbody>
</table>

genetic mapping of thermotolerance differences between species of saccharomyces yeast via genome-wide reciprocal hemizygosity analysis

A central goal of modern genetics is to understand how and why organisms in the wild differ in phenotype. To date, the field has advanced largely on the strength of linkage and association mapping methods, which trace the relationship between DNA sequence variants and phenotype across recombinant progeny from matings between individuals of a species. These approaches, although powerful, are not well suited to trait differences between reproductively isolated species. Here we describe a new method for genome-wide dissection of natural trait variation that can be readily applied to incompatible species. Our strategy, RH-seq, is a genome-wide implementation of the reciprocal hemizygote test. We harnessed it to identify the genes responsible for the striking high temperature growth of the yeast Saccharomyces cerevisiae relative to its sister species S. paradoxus. RH-seq utilizes transposon mutagenesis to create a pool of reciprocal hemizygotes, which are then tracked through a high-temperature competition via high-throughput sequencing. Our RH-seq workflow as laid out here provides a rigorous, unbiased way to dissect ancient, complex traits in the budding yeast clade, with the caveat that resource-intensive deep sequencing is needed to ensure genomic coverage for genetic mapping. As sequencing costs drop, this approach holds great promise for future use across eukaryotes.

We mated S. cerevisiae and S. paradoxus to form a sterile hybrid, which we subjected to transposon mutagenesis. Each mutagenized clone was a hemizygote, a diploid hybrid in which one allele of one gene is disrupted (Figure 1A, Figure 2). We competed the hemizygotes against one another by growth at 39 &#176;C and, in a separate experiment as a control, at 28 &#176;C (Figure 1B), and we isolated DNA ...

Watch this Scientific Journal Video about Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis at JoVE.com

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Reciprocal hemizygosity via sequencing (RH-seq) is a powerful new method to map the genetic basis of a trait difference between species. Pools of hemizygotes are generated by transposon mutagenesis and their fitness is tracked through competitive growth using high-throughout sequencing. Analysis of the resulting data pinpoints genes underlying the trait.

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

A central goal of modern genetics is to understand how and why organisms in the wild differ in phenotype. To date, the field has advanced largely on the ...

genetic-mapping-thermotolerance-differences-between-species

Research

JoVE Journal

Genetics

16.8K Views.  University of California Berkeley. Reciprocal hemizygosity via sequencing (RH-seq) is a powerful new method to map the genetic basis of a trait difference between species. Pools of hemizygotes are generated by transposon mutagenesis and their fitness is tracked through competitive growth using high-throughout sequencing. Analysis of the resulting data pinpoints genes underlying the trait.

Video: Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Genetic Engineering of an Unconventional Yeast for Renewable Biofuel and Biochemical Production

Yarrowia lipolytica is a non-pathogenic, dimorphic and strictly aerobic yeast species. Owing to its distinctive physiological features and metabolic characteristics, this unconventional yeast is not only a good model for the study of the fundamental nature of fungal differentiation but is also a promising microbial platform for biochemical production and various biotechnological applications, which require extensive genetic manipulations. However, genetic manipulations of Y. lipolytica have been limited due to the lack of an efficient and stable genetic transformation system as well as very high rates of non-homologous recombination that can be mainly attributed to the KU70 gene. Here, we report an easy and rapid protocol for the efficient genetic transformation and for gene deletion in Y. lipolytica Po1g. First, a protocol for the efficient transformation of exogenous DNA into Y. lipolytica Po1g was established. Second, to achieve the enhanced double-crossover homologous recombination rate for further deletion of target genes, the KU70 gene was deleted by transforming a disruption cassette carrying 1 kb homology arms. Third, to demonstrate the enhanced gene deletion efficiency after deletion of the KU70 gene, we individually deleted 11 target genes encoding alcohol dehydrogenase and alcohol oxidase using the same procedures on the KU70 knockout platform strain. It was observed that the rate of precise homologous recombination increased substantially from less than 0.5% for deletion of the KU70 gene in Po1g to 33%-71% for the single gene deletion of the 11 target genes in Po1g KU70&#916;. A replicative plasmid carrying the hygromycin B resistance marker and the Cre/LoxP system was constructed, and the selection marker gene in the yeast knockout strains was eventually removed by expression of Cre recombinase to facilitate multiple rounds of targeted genetic manipulations. The resulting single-gene deletion mutants have potential applications in biofuel and biochemical production.

We herein report methods on the molecular genetic manipulation of the Yarrowia lipolytica Po1g strain for improved gene deletion efficiency. The resulting engineered Y. lipolytica strains have potential applications in biofuel and biochemical production.

Yarrowia lipolytica is a non-pathogenic, dimorphic and strictly aerobic yeast species. Owing to its distinctive physiological features and metabolic ...

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

Replication of the genome occurs during S phase of the cell cycle in a highly regulated process that ensures the fidelity of DNA duplication. Each genomic region is replicated at a distinct time during S phase through the simultaneous activation of multiple origins of replication. Time of replication (ToR) correlates with many genomic and epigenetic features and is linked to mutation rates and cancer. Comprehending the full genomic view of the replication program, in health and disease is a major future goal and challenge.
This article describes in detail the &#34;Copy Number Ratio of S/G1 for mapping genomic Time of Replication&#34; method (herein called: CNR-ToR), a simple approach to map the genome wide ToR of mammalian cells. The method is based on the copy number differences between S phase cells and G1 phase cells. The CNR-ToR method is performed in 6 steps: 1. Preparation of cells and staining with propidium iodide (PI); 2. Sorting G1 and S phase cells using fluorescence-activated cell sorting (FACS); 3. DNA purification; 4. Sonication; 5. Library preparation and sequencing; and 6. Bioinformatic analysis. The CNR-ToR method is a fast and easy approach that results in detailed replication maps.

We describe here a relatively fast and simple approach for mapping genome-wide mammalian replication timing, from cell isolation to the basic analysis of the sequencing results. A genomic map of a representative replication program will be provided following the protocol.

Replication of the genome occurs during S phase of the cell cycle in a highly regulated process that ensures the fidelity of DNA duplication. Each genomic ...

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

Genome-wide mapping of protein-DNA interactions is critical for understanding gene regulation, chromatin remodeling, and other chromatin-resident processes. Formaldehyde crosslinking followed by chromatin immunoprecipitation and high-throughput sequencing (X-ChIP-seq) has been used to gain many valuable insights into genome biology. However, X-ChIP-seq has notable limitations linked to crosslinking and sonication. Native ChIP avoids these drawbacks by omitting crosslinking, but often results in poor recovery of chromatin-bound proteins. In addition, all ChIP-based methods are subject to antibody quality considerations. Enzymatic methods for mapping protein-DNA interactions, which involve fusion of a protein of interest to a DNA-modifying enzyme, have also been used to map protein-DNA interactions. We recently combined one such method, chromatin endogenous cleavage (ChEC), with high-throughput sequencing as ChEC-seq. ChEC-seq relies on fusion of a chromatin-associated protein of interest to micrococcal nuclease (MNase) to generate targeted DNA cleavage in the presence of calcium in living cells. ChEC-seq is not based on immunoprecipitation and so circumvents potential concerns with crosslinking, sonication, chromatin solubilization, and antibody quality while providing high resolution mapping with minimal background signal. We envision that ChEC-seq will be a powerful counterpart to ChIP, providing an independent means by which to both validate ChIP-seq findings and discover new insights into genomic regulation.

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

We describe chromatin endogenous cleavage coupled with high-throughput sequencing (ChEC-seq), a chromatin immunoprecipitation (ChIP)-orthogonal method for mapping protein binding sites genome-wide with micrococcal nuclease (MNase) fusion proteins.

Genome-wide mapping of protein-DNA interactions is critical for understanding gene regulation, chromatin remodeling, and other chromatin-resident ...

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method used for the identification of open (accessible) regions of chromatin. These regions represent regulatory DNA elements (e.g., promoters, enhancers, locus control regions, insulators) to which transcription factors bind. Mapping the accessible chromatin landscape is a powerful approach for uncovering active regulatory elements across the genome. This information serves as an unbiased approach for discovering the network of relevant transcription factors and mechanisms of chromatin structure that govern gene expression programs. ATAC-seq is a robust and sensitive alternative to DNase I hypersensitivity analysis coupled with next-generation sequencing (DNase-seq) and formaldehyde-assisted isolation of regulatory elements (FAIRE-seq) for genome-wide analysis of chromatin accessibility and to the sequencing of micrococcal nuclease-sensitive sites (MNase-seq) to determine nucleosome positioning. We present a detailed ATAC-seq protocol optimized for human primary immune cells i.e. CD4+ lymphocytes (T helper 1 (Th1) and Th2 cells). This comprehensive protocol begins with cell harvest, then describes the molecular procedure of chromatin tagmentation, sample preparation for next-generation sequencing, and also includes methods and considerations for the computational analyses used to interpret the results. Moreover, to save time and money, we introduced quality control measures to assess the ATAC-seq library prior to sequencing. Importantly, the principles presented in this protocol allow its adaptation to other human immune and non-immune primary cells and cell lines. These guidelines will also be useful for laboratories which are not proficient with next-generation sequencing methods.

Assay for Transposase-Accessible Chromatin coupled with high-throughput sequencing (ATAC-seq) is a genome-wide method to uncover accessible chromatin. This is a step-by-step ATAC-seq protocol, from molecular to the final computational analysis, optimized for human lymphocytes (Th1/Th2). This protocol can be adopted by researchers without prior experience in next-generation sequencing methods.

Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method used for the identification of open (accessible) regions ...

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

Quiescence is a temporary, reversible state in which cells have ceased cell division, but retain the capacity to proliferate. Multiple studies, including ours, have demonstrated that quiescence is associated with widespread changes in gene expression. Some of these changes occur through changes in the level or activity of proliferation-associated transcription factors, such as E2F and MYC. We have demonstrated that mRNA decay can also contribute to changes in gene expression between proliferating and quiescent cells. In this protocol, we describe the procedure for establishing proliferating and quiescent cultures of human dermal foreskin fibroblasts. We then describe the procedures for inhibiting new transcription in proliferating and quiescent cells with Actinomycin D (ActD). ActD treatment represents a straightforward and reproducible approach to dissociating new transcription from transcript decay. A disadvantage of ActD treatment is that the time course must be limited to a short time frame because ActD affects cell viability. Transcript levels are monitored over time to determine transcript decay rates. This procedure allows for the identification of genes and isoforms that exhibit differential decay in proliferating versus quiescent fibroblasts.

We describe a protocol for generating proliferating and quiescent primary human dermal fibroblasts, monitoring transcript decay rates, and identifying differentially decaying genes.

Quiescence is a temporary, reversible state in which cells have ceased cell division, but retain the capacity to proliferate. Multiple studies, including ...

Generation of Genome-wide Chromatin Conformation Capture Libraries from Tightly Staged Early Drosophila Embryos

Investigating the three-dimensional architecture of chromatin offers invaluable insight into the mechanisms of gene regulation. Here, we describe a protocol for performing the chromatin conformation capture technique in situ Hi-C on staged Drosophila melanogaster embryo populations. The result is a sequencing library that allows the mapping of all chromatin interactions that occur in the nucleus in a single experiment. Embryo sorting is done manually using a fluorescent stereo microscope and a transgenic fly line containing a nuclear marker. Using this technique, embryo populations from each nuclear division cycle, and with defined cell cycle status, can be obtained with very high purity. The protocol may also be adapted to sort older embryos beyond gastrulation. Sorted embryos are used as inputs for in situ Hi-C. All experiments, including sequencing library preparation, can be completed in five days. The protocol has low input requirements and works reliably using 20 blastoderm stage embryos as input material. The end result is a sequencing library for next generation sequencing. After sequencing, the data can be processed into genome-wide chromatin interaction maps that can be analyzed using a wide range of available tools to gain information about topologically associating domain (TAD) structure, chromatin loops, and chromatin compartments during Drosophila development.

Generation of Genome-wide Chromatin Conformation Capture Libraries from Tightly Staged Early Drosophila Embryos

This work describes a protocol for the generation of high resolution in situ Hi-C libraries from tightly staged pre-gastrulation Drosophila melanogaster embryos.

Investigating the three-dimensional architecture of chromatin offers invaluable insight into the mechanisms of gene regulation. Here, we describe a ...

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the spatio-temporal expression of their target genes through physical contact, often bridging considerable (in some cases hundreds of kilobases) genomic distances and bypassing nearby genes. The human genome harbors an estimated one million enhancers, the vast majority of which have unknown gene targets. Assigning distal regulatory regions to their target genes is thus crucial to understand gene expression control. We developed Promoter Capture Hi-C (PCHi-C) to enable the genome-wide detection of distal promoter-interacting regions (PIRs), for all promoters in a single experiment. In PCHi-C, highly complex Hi-C libraries are specifically enriched for promoter sequences through in-solution hybrid selection with thousands of biotinylated RNA baits complementary to the ends of all promoter-containing restriction fragments. The aim is to then pull-down promoter sequences and their frequent interaction partners such as enhancers and other potential regulatory elements. After high-throughput paired-end sequencing, a statistical test is applied to each promoter-ligated restriction fragment to identify significant PIRs at the restriction fragment level. We have used PCHi-C to generate an atlas of long-range promoter interactions in dozens of human and mouse cell types. These promoter interactome maps have contributed to a greater understanding of mammalian gene expression control by assigning putative regulatory regions to their target genes and revealing preferential spatial promoter-promoter interaction networks. This information also has high relevance to understanding human genetic disease and the identification of potential disease genes, by linking non-coding disease-associated sequence variants in or near control sequences to their target genes.

DNA regulatory elements, such as enhancers, control gene expression by physically contacting target gene promoters, often through long-range chromosomal interactions spanning large genomic distances. Promoter Capture Hi-C (PCHi-C) identifies significant interactions between promoters and distal regions, enabling the assignment of potential regulatory sequences to their target genes.

The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the ...

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that control the fidelity of transcription. To fill this gap in scientific knowledge, we recently optimized the circle-sequencing assay to detect transcription errors throughout the transcriptome of Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans. This protocol will provide researchers with a powerful new tool to map the landscape of transcription errors in eukaryotic cells so that the mechanisms that control the fidelity of transcription can be elucidated in unprecedented detail.

This protocol provides researchers with a new tool to monitor the fidelity of transcription in multiple model organisms.

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that ...

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing high-throughput short-read DNA sequencing. The resulting sequence datasets can not only track what genes in a population that are enriched during selection for positive yeast 2-hybrid interactions, but also give detailed information about the relevant subdomains of proteins sufficient for interaction. Here, we describe a full suite of stand-alone software programs that allow non-experts to perform all the bioinformatics and statistical steps to process and analyze DNA sequence fastq files from a batch yeast 2-hybrid assay. The processing steps covered by these software include: 1) mapping and counting sequence reads corresponding to each candidate protein encoded within a yeast 2-hybrid prey library; 2) a statistical analysis program that evaluates the enrichment profiles; and 3) tools to examine the translational frame and position within the coding region of each enriched plasmid that encodes the interacting proteins of interest.

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing ...

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. While this has led to increased identification of likely pathogenic mutations associated with genetic syndromes, it has also dramatically increased the number of incidentally found genetic variants of unknown significance (VUS). Determining the clinical significance of these variants is a major challenge for both scientists and clinicians. An approach to assist in determining the likelihood of pathogenicity is signal-to-noise analysis at the protein sequence level. This protocol describes a method for amino acid-level signal-to-noise analysis that leverages variant frequency at each amino acid position of the protein with known protein topology to identify areas of the primary sequence with elevated likelihood of pathologic variation (relative to population &#34;background&#34; variation). This method can identify amino acid residue location &#34;hotspots&#34; of high pathologic signal, which can be used to refine the diagnostic weight of VUSs such as those identified by next generation genetic testing.

Amino acid-level signal-to-noise analysis determines the prevalence of genetic variation at a given amino acid position normalized to background genetic variation of a given population. This allows for identification of variant &#34;hotspots&#34; within a protein sequence (signal) that rises above the frequency of rare variants found in a population (noise).

Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. ...