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Identification and Validation of Reference Genes for Quantitative Real-Time PCR in Drosophila suzukii (Diptera: Drosophilidae)
research-article
Author(s):
Yifan Zhai
1
,
* ,
Qingcai Lin
2 ,
Xianhong Zhou
1 ,
Xiaoyan Zhang
3 ,
Tingli Liu
1 ,
Yi Yu
1
,
*
Publication date (Electronic):
8 September 2014
Journal:
PLoS ONE
Publisher:
Public Library of Science
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Abstract
To accurately evaluate gene expression levels and obtain more accurate quantitative real-time RT-PCR (qRT-PCR) data, normalization relative to reliable reference gene(s) is required. Drosophila suzukii, is an invasive fruit pest native to East Asia, and recently invaded Europe and North America, the stability of its reference genes have not been previously investigated. In this study, ten candidate reference genes ( RPL18, RPS3, AK, EF-1β, TBP, NADH, HSP22, GAPDH, Actin, α-Tubulin), were evaluated for their suitability as normalization genes under different biotic (developmental stage, tissue and population), and abiotic (photoperiod, temperature) conditions. The three statistical approaches (geNorm, NormFinder and BestKeeper) and one web-based comprehensive tool (RefFinder) were used to normalize analysis of the ten candidate reference genes identified α-Tubulin, TBP and AK as the most stable candidates, while HSP22 and Actin showed the lowest expression stability. We used three most stable genes ( α-Tubulin, TBP and AK) and one unstably expressed gene to analyze the expression of P-glycoprotein in abamectin-resistant and sensitive strains, and the results were similar to reference genes α-Tubulin, TBP and AK, which show good stability, while the result of HSP22 has a certain bias. The three validated reference genes can be widely used for quantification of target gene expression with qRT-PCR technology in D.suzukii.
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Most cited references21
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Expression in Aneuploid Drosophila S2 Cells
Yu Zhang, John H. Malone, Sara Powell … (2010)
Introduction The somatic cells of multicellular animals are almost exclusively diploid, with haploidy restricted to post-meiotic germ cells. Having two copies of every gene has an obvious advantage. Mutations arise de novo within cells of an organism and within organisms in populations, such that deleterious mutation-free haploid genomes are extremely rare. The wild type alleles of genes tend to be dominant to the recessive loss-of-function alleles, providing a degree of redundancy allowing diploid organisms to survive even with a substantial genetic load of deleterious mutations in each haplotype. While the dose of most individual genes is of little consequence to the organism, larger scale genomic imbalance, or aneuploidy, is detrimental [1]–[4]. Chromosomal aneuploidy occurs when whole chromosomes are lost or duplicated and segmental aneuploidy results from deletions, duplications, and unbalanced translocations. In Drosophila, a systematic genome-wide segmental aneuploidy study [5] demonstrated that of all genes (now known to be about 15,000 [6]), only about 50 are haploinsufficient and just one gene is triplo-lethal. However, these same experiments showed that large deletions and duplications result in reduced viability and fertility that depends on the extent of aneuploidy, and not on any particular region or gene [5]. This indicates that the detrimental effect of aneuploidy is a collective function of multiple small effects, not a function of particular genes. Interestingly, while aneuploidy results in inviability at the organism level, aneuploid cells can out-compete diploid cells for growth in vivo or in vitro. Human cancer cells are a good example of proliferating cells characterized by aneuploidy [7]. Most tumors are nearly diploid or tetraploid with extra or lost chromosomes. Even tumors with a normal number of chromosomes contain other rearrangements that result in segmental aneuploidy. It is likely that aneuploidy results in a systems or gene interaction defect. Given that a deleterious effect of aneuploidy is likely to occur at the level of genome balance, understanding the response to aneuploidy requires the exploration of general control mechanisms that operate at the network level. We have turned to widely used Drosophila S2 tissue culture cells as an aneuploid model [8],[9]. These cells are generally tetraploid [9] and studies of gene expression and X chromosome dosage compensation indicate that they are male [10]. As a natural consequence of chromosomal sex determination in Drosophila, females have two X chromosomes and two pairs of autosomes (2X;2A) and males have a single X chromosome (1X;2A) [11]. Therefore, male cells can be thought of as naturally occurring chromosomal aneuploids. The response to altered gene dose probably occurs at multiple levels, but transcription is an early step in the flow of information from the genome and is a likely site for control. For example, X chromosome dosage compensation clearly occurs at the transcriptional level [12] and is exquisitely precise [13]. The Male Specific Lethal (MSL) complex regulates the balanced expression of X chromosomes in wild type 1X;2A male flies. MSL is composed of at least four major proteins (Msl1, Msl2, Msl3, and Mof) and two non-coding RNAs (RoX1 and RoX2) [11]. Mof is an acetyltransferase responsible for acetylating H4K16 [11],[14],[15]. Mof is highly enriched on the male X chromosome as a component of the MSL complex. However, Mof also associates with a more limited repertoire of autosomal genes independently of MSL [16]. H4K16ac is associated with increased transcription in many systems [17]. Therefore, it is widely believed that this acetylation results in increased expression of the X chromosome [11], although an alternative hypothesis suggests that MSL sequesters Mof from the autosomes to drive down autosome expression [18]. Determining which of these mechanisms occurs is complicated by the very nature of sampling experiments when much of the transcriptome is altered. The number of X chromosome transcripts sampled from the transcriptome depends on the relative abundance of the X chromosome and autosome transcripts. The salient feature of both models is balanced X chromosome and autosome expression. While the term dosage compensation is used to describe X chromosome expression, dosage compensation is not restricted to X chromosomes in Drosophila. Autosomes also show significant, but much less precise, dosage compensation at the expression level [13],[19]–[21], suggesting that there is a general dose response genome-wide. Despite the clear role of MSL in X chromosome dosage compensation, the control system rules for MSL function and the contribution of global compensation mechanisms to the specific case of the X chromosome are poorly understood. There are three basic transcript control mechanisms that could modify the effect of gene dose: buffering, feedback, and feed-forward [22]. Here we define buffering as the passive absorption of gene dose perturbations by inherent system properties. For example, if transcription obeys mass-action kinetics and the gene/transcription complex is considered an enzyme [23], then one would not expect a one-to-one relationship between mRNA and gene copy because of the small effect of a change in enzyme concentration at steady-state [24]. In addition to the enzymatic properties of transcription, more than a generation of molecular biologists has elegantly described extensive transcriptional regulation networks controlling key phenotypes [25]. These regulatory motifs are sensitive to changes in gene dose [26]. Feedback is an outstanding error-controlled regulator that detects deviations from the norm and implements corrective action. Feed-forward regulation differs in that it anticipates the possible effect of perturbations on the system rather than correcting the perturbation after the deviation occurs. This could operate if cells detect copy number and correct transcription levels before a quantitative error in transcript abundance is evident. In male embryos, the sex determination hierarchy detects X chromosome number and leads to association of the MSL complex with the X chromosome before zygotic transcription is activated [27], as expected for a feed-forward regulator. However, MSL is selectively bound to transcribed genes [28], which is also consistent with feedback regulation. By examining the response of X chromosome genes to dose in the presence and absence of MSL, we show that X chromosome dosage compensation results from a combination of MSL-dependent feed-forward regulation based on anticipated effects from unbalanced gene dose and a more general and dynamic response to perceived gene dose. The latter could be due to negative feedback, buffering, or both. Results Segmental Aneuploidy in S2 Cells To determine the extent of aneuploidy in S2 cells, we performed next generation sequencing (DNA-Seq) and comparative genome hybridization (CGH). These data confirmed the predicted male genotype of S2 cells, as the average sequence depth of the X chromosome (reads per kb per million reads, RPKM) was 54% of the autosome RPKM (Figures 1 and 2A). 10.1371/journal.pbio.1000320.g001 Figure 1 S2 cell DNA copy number. (A–D) DNA density and copy number profiles of the X chromosome (A, B) and chromosome 2L (C, D), showing copy number of aneuploidy segments along chromosome length. The RPKM DNA-Seq density in nonoverlapping 1 kb windows was plotted against the chromosome coordinates and the final deduced copy number is indicated (color key). The copy number was determined by Bayesian change point analysis (CPA) (A, C) and CGH (B, D). The CGH results are projected onto the DNA-Seq data. The average DNA densities of each aneuploid segment between predicted breakpoints (black lines) are shown. 10.1371/journal.pbio.1000320.g002 Figure 2 Expression at varying copy numbers. (A, B) Boxplots showing the distribution of DNA-Seq read densities (in non-overlapping 1 kb windows) mapped to chromosome arms in S2 cells (A) and the distribution of RNA-Seq expression values at the gene-level (B). Pie charts (A, B) show the distributions of copy numbers on each chromosome arm (for expressed genes only). See Figure 1 for copy number color key. The X chromosome is in red. (C, D) Boxplots showing the distribution of RNA-Seq expression values by copy number (C) and expression per copy (D). Equivalent expression medians for two copies on the X and four copies on the autosomes are indicated (dashed line). For all boxplots, the 25th to 75th percentiles (boxes), medians (lines in boxes), and ranges (whiskers, 1.5 times the interquartile range extended from both ends of the box) are shown. Asterisks indicate significant differences from all other chromosome arms (A, B) or from the 2X or 4A baseline (C). We also found that S2 cells exhibit numerous large regions of segmental aneuploidy (Figure 1, Figure S1, Table S1). Stepwise deviations from expected dose covered ∼42% (∼40.0 Mb) of the autosomes and ∼17% (∼3.8 Mb) of the X chromosome (Figure S1). The vast majority of the aneuploid segments showed an extra or lost copy. There was high congruence between DNA-Seq and CGH methods. For example, we determined that >93% of calls for copy numbers between one and five made by DNA-Seq analysis were confirmed by CGH, even when comparing different lots of cells grown under slightly different conditions (Figure S2, Table S2). These data suggest that S2 cells are highly aneuploid but show a reasonably stable genotype. There was much more variability seen when copy number was greater than five (30% agreement between methods and cultures). This could be due to failure to call short segmental duplications or to repeat expansion/retraction in different cultures. Regardless of cause, we decided to focus our subsequent expression analyses on the high-confidence one to five copy genes (Table S3). Genome-Wide Compensation We observed striking differences in DNA-Seq read density among chromosome arms due to segmental aneuploidy (Figure 2A, p 10−2, KS test), indicating that dosage compensation occurs genome-wide, not just on the X chromosome. To examine the precision of dosage compensation, we determined the relationship between expression and copy number. Compensation was not perfect, as expression increased with copy number (Figure 2C, p 0.99). This indicates that gene expression is a saturating function of gene dose regardless of chromosome location or the presence of MSL. Discussion Our data indicate that the MSL complex and general compensation mechanisms independently contribute to male X chromosome dosage compensation. The MSL complex recognizes active X chromosome genes [28]–[31]. We have shown that MSL then acts as a simple unidirectional multiplier of expression regardless of the actual gene dose and gene expression level. In contrast, buffering and feed-back are dose sensitive and absorb the expression perturbations caused by unbalanced dose. We suggest that all these mechanisms are critical for proper X chromosome dosage compensation. Some rough accounting illustrates the composite nature of X chromosome dosage compensation. In the Drosophila genus, dosage compensation results in a 2.0- to 2.2-fold increase in X chromosome expression in males relative to autosomes [13],[32]. Similarly, in S2 cells we observed a 2.08-fold increase in X chromosome expression. The fixed-fold effect of MSL resulted in at least a 1.35-fold increase in X-chromosome expression. Dose-responsive compensation also acted to increase X chromosome expression and was independent of MSL function. We can estimate the contribution of dose-responsive compensation from work performed on whole flies and on S2 cells. Autosomal dosage compensation increases per copy expression by 1.4- to 1.6-fold in diploid flies with a single copy of tens of genes [13],[19]. In agreement with those reported values, we can project that a 2-fold change in scaled DNA dose in S2 cells results in about a 1.5-fold increase in scaled gene expression. Thus, at face value, the layered effect of dose-responsive compensation and feed-forward dosage compensation may explain all of the final increase in S2 cell X chromosome expression (1.50-fold×1.35-fold = 2.03-fold). While most work on dosage compensation focuses on the X chromosome [2],[11], other organisms also show dosage compensation on autosomes [33]. For example, mammalian trisomies show only about a 1.3-fold increase in gene expression as a result of a 1.5-fold change in gene dose [34],[35]. Compensation is likely to be a universal property of biological systems that enables cells to avoid deleterious effects of genetic load and other perturbations. Materials and Methods Cell Strains and Media Drosophila S2 cells [9] (a.k.a. SL2) were obtained from Drosophila RNAi Screening Center (DRSC, Harvard Medical School, Boston, MA) and were grown at 25°C in Schneider's Drosophila Medium (Invitrogen, Carlsbad, CA) supplemented with 10% Fetal Bovine serum (SAFC Biosciences, Lenexa, KS) and Penicillin-Streptomycin (Invitrogen, Carlsbad, CA). These cells were used for all experiments, except CGH, where S2-DRSC cells were obtained from the Drosophila Genomics Resource Center (#181, Bloomington, IN). Sequencing We extracted S2 cell genomic DNA using a genomic DNA kit (Qiagen, Valencia, CA). Approximately 2 µg of purified genomic DNA was randomly fragmented to less than 1,000 bp by 30 min sonication at 4°C with cycles of 30 s pulses with 30 s intervals using the Bioruptor UCD 200 and a refrigerated circulation bath RTE-7 (Diagenode, Sparta, NJ). Sonicated chromatin (see ChIP protocol) was purified by phenol/chloroform extraction. We extracted S2 cell total RNA with Trizol (Invitrogen, Carlsbad, CA) and isolated mRNA using Oligotex poly(A) (Qiagen, Valencia, CA). The number of cells used for each extraction was counted using a haemocytometer. The quality of mRNA was examined by RNA 6000 Nano chip on a Bioanalyzer 2100 (Agilent, Santa Clara, CA) according to the manufacture's protocol. One hundred ng of the extracted mRNA was then fragmented in fragmentation buffer (Ambion, Austin, TX) at 70°C for exactly 5 min. The first strand cDNA was then synthesized by reverse transcriptase using the cleaved mRNA fragments as template and high concentration (3 µg) random hexamer Primers (Invitrogen, Carlsbad, CA). After the first strand was synthesized, second strand cDNA synthesis was performed using 50U DNA polymerase I and 2U RNaseH (Invitrogen, Carlsbad, CA) at 16°C for 2.5 h. Deep sequencing of both DNA and short cDNA fragments were performed [36],[37]. Libraries were prepared according to instructions for genomic DNA sample preparation kit (Illumina, San Diego, CA). The library concentration was measured on a Nanodrop spectrophotometer (NanoDrop products, Wilmington, DE), and 4 pM of adaptor-ligated DNA was hybridized to the flow cell. DNA clusters were generated using the Illumina cluster station, followed by 36 cycles of sequencing on the Illumina Genome Analyzer, in accordance with the manufacturer's protocols. Two technical replicate libraries were constructed for each DNA-Seq sample. Two libraries were prepared from two biological replicates of each RNA material (RNAi or mock treated). RNAi dsRNA for RNAi treatment [38] was produced by in vitro transcription of a PCR generated DNA template from Drosophila genomic DNA containing the T7 promoter sequence on both ends. Target sequences were scanned to exclude any complete 19 mer homology to other genes [39]. The dsRNAs were generated using the MEGAscript T7 kit (Ambion, Austin, TX) and purified using RNAeasy kit (Qiagen, Valencia, CA). Two different primer sets were used for each target gene, and the one with better RNAi efficiency was used for downstream experiments. The selected primer sequences for generation of msl2 dsRNA template by PCR were as follows: forward, 5′-taatacgactcactatagggTTGCTCCGACTTCAAGACCT-3′, and reverse, 5′-taatacgactcactatagggGCATCACGTAGGAGACAGCA-3′ and the selected primer sequences for generation of mof dsRNA template were as follows: forward, 5′-taatacgactcactatagggGACGGTCATCACAACAGGTG-3′, and reverse, 5′-taatacgactcactatagggTGCGGTCGCTGTAGTCATAG-3′. For RNAi treatment, S2 cells were resuspended in serum free media at 2×106 cells/ml. Twenty µg dsRNA was added to 1 ml of cell suspension and incubated for 45 min at room temperature. Cells with the same serum free media treatment but without added dsRNA were used as mock treated controls. After the incubation, 3 ml complete medium was added and the cells were cultured for another 4 d. Cells were collected and split into three aliquots for mRNA extraction, chromatin immunoprecipitation, and western analysis. ChIP For ChIP [40], 5–10×106 S2 cells were fixed with 1% formaldehyde in tissue culture media for 10 min at room temperature. Glycine was added to a final concentration of 0.125 M to stop cross-linking. After 5 min of additional incubation and two washes with ice-cold PBS, cells were collected and resuspended in cell lysis buffer (5 mM PH 8.0 PIPES buffer, 85 mM KCl, 0.5% Nonidet P40, and protease inhibitors cocktail from Roche, Basel, Switzerland) for 10 min and then resuspended in nuclei lysis buffer (50 mM PH 8.1 Tris.HCl, 10 mM EDTA, 1% SDS and protease inhibitors) for 20 min at 4°C. The nuclear extract was sheared to 200–1,000 bp by sonication on ice for 8 min (pulsed 8 times for 30 s with 30 s intervals using a Misonix Sonicator 3000; Misonix, Inc. Farmingdale, NY). The chromatin solution was then clarified by centrifugation at 14,000 rpm for 10 min at 4°C. Five ul anti-H4AcK16 (Millipore, Billerica, MA) was incubated with the chromatin for 2 h and then was bound to protein A agarose beads at 4°C overnight. The beads were washed three times with 0.1% SDS, 1% Trition, 2 mM EDTA, 20 mM PH 8.0 Tris, and 150 mM NaCl; three times with 0.1% SDS, 1% Trition, 2 mM EDTA, 20 mM PH 8.0 Tris, and 500 mM NaCl; and twice with 10 mM PH 8.1 Tris, 1 mM EDTA, 0.25 M LiCl, 1% NP40, and 1% sodium deoxycholate. The immunoprecipitated DNA was eluted from the beads in 0.1 M NaHCO3 and 1% SDS and incubated at 65°C overnight to reverse cross-linking. DNA was purified by phenol-chloroform extraction and ethanol precipitation. The precipitated DNA for Chromatin immunoprecipitation was amplified using a Ligation-mediated PCR (LM-PCR) protocol from FlyChip [41]. ChIP was performed on triplicate biological samples. Microarrays Six hundred ng of amplified DNA (ChIP enriched DNA or input DNA) were labeled using 6ug Cy3- or Cy5-labeled random nonamers (Trilink Biosciences, San Diego, CA) with 50U Klenow (New England Biolabs, Ipswich, MA) and 2 mM dNTPs. The labeled DNA was purified and hybridized to FlyGEM microarrays [42]. Arrays were scanned on an Axon 4000B scanner (Molecular Devices Corporation, Sunnyvale, CA) and signal was extracted with GenePix v.5.1 image acquisition software (Molecular Devices Corporation). Two hundred ng aliquots of the same extracted mRNA used for RNA-Seq were labeled as described [42] and were hybridized to NimbleGen custom 12 plex microarrays at 42°C using a MAUI hybridization station (BioMicro Systems, Salt Lake City, UT) according to manufacturer instructions (NimbleGen Systems, Madison, WI). Arrays were scanned on an Axon 4200AL scanner (Molecular Devices Corporation, Sunnyvale, CA) and data were captured using NimbleScan 2.1 (NimbleGen Systems, Madison, WI). Western Analysis Cell lysates were prepared from cells 4 d after dsRNA or mock treatment by boiling for 5 min in NuPAGE LDS sample buffer (Invitrogen, Carlsbad, CA). Samples were run by SDS-PAGE using a 4%–12% Bis-Tris gel (Invitrogen, Carlsbad, CA) and transferred to PVDF membrane. Blots were incubated with anti-MSL antibody (1∶500), anti-MOF antibody (1∶3,000, gifts of M. Kuroda), or anti-α tubulin antibody (1∶10,000, Sigma, St. Louis, MO) and then with HRP-secondary antibodies in PBS buffer with 0.1% Tween 20. Protein signals were detected by Pierce SuperSignal West Dura extended Duration Substrate (Thermo Fisher Scientific, Rockford, IL). Images were captured using a Fuji LAS-3000 Imager and quantified using the Image Gauge software (Fuji Film, Tokyo, Japan). Data Processing Both DNA-Seq and RNA-Seq sequence reads were compiled using a manufacturer-provided computational pipeline (Version 0.3) including the Firecrest and Bustard applications [36]. Sequence reads were then aligned with the Drosophila melanogaster assembly (BDGP Release 5, dm3) [6],[43] using Eland. Only uniquely mapped reads with less than two mismatches were used. For DNA-Seq data, we counted the number of reads in the non-overlapped 1 kb region along each chromosome using all sequenced reads from two technical DNA-Seq libraries and calculated the read density by the number of unique mapped reads per kb per million mapped reads (RPKM) [37]. The breakpoint positions of aneuploid segments were identified using the Bayesian analysis of change point (bcp) package from R [44]. Because some reads mapped to multiple positions in the genome and thus inappropriately lower the deduced copy number in regions with low sequence complexity, we removed all the 1 kb windows with RPKM lower than 2 (RPKM value of one copy = 2.29) prior to change point analysis. Breakpoints with posterior possibility >0.95 were used. Copy number was assigned to segments based on the fold between average segments RPKM value between breakpoints (2.29±1.15 RPKM = 1 copy, 4.58±1.15 RPKM = 2 copy, etc.). Genes spanning two segments were not used in gene expression analysis. For RNA-Seq data, we counted the number of unique mapped reads within all unique exons of Drosophila Flybase [45] Release 5.12 annotation (Oct. 2008) and calculated the total number of reads of all unique exons per kb of total length of unique exons per million mapped reads (RPKM) for each annotated gene. The RPKM calculation was done for individual RNA-Seq libraries separately, and then RPKM values were averaged for biological replicates (r 2 = 0.98 between replicates). Non-expressed genes are not useful for ratiometric analysis and these were therefore excluded. We used RPKM values for intergenic regions to determine expression thresholds. For intergenic regions, the RPKM values were calculated for total number of reads between adjacent gene model pairs. Only 5% of intergenic regions in S2 cells have a RPKM value greater than or equal to 4. Therefore, we called genes with RPKM values no less than 4 in S2 cells as expressed with an estimated type I error rate of 5%. All microarray data (except CGH) and statistical tests were processed and analyzed in R/Bioconductor [46]. For the ChIP-chip experiments, we used quantile normalization based on the input channel. The distributions of raw and normalized intensities were checked to make sure that normalization was appropriate (i.e., that the skew was maintained). We used the average ChIP/input ratio from biological replicates (r 2 = 0.40–0.54 between replicates). The ChIP/input ratios in RNAi and mock treated cells were used for K-means clustering analysis with 3 nodes using Euclidean similarity metric and genes on X chromosome and autosomes were clustered separately using Cluster3.0 and then visualized using Tree-View [47]. For expression profiling, we normalized using loess within each 12-plex and quantile between 12-plexes. Average probeset log2 intensities were calculated in both channels for each gene. Correlations between array intensities and RPKM values were estimated by Spearman's rank correlation coefficient. The comparisons for the distributions of DNA densities or expression values among different chromosomes and different copy numbers were performed using two sample Kolmogorov-Smirnov tests (KS tests). Normalization is inherently problematic when a large fraction of the genome changes expression, as in the RNAi experiments. Given that 20% of the genome is encoded on the X chromosome (X) and 80% is encoded on autosomes (A), and that one samples transcripts from a total mRNA pool to generate an expression profile, and that X chromosome expression is reduced by half and autosome expression does not change, then autosomal transcripts must be over-sampled in the experiment. Conversely, if the autosome expression is doubled, then X chromosome transcripts must be under-sampled. While it is imprudent to formally state the precise contribution of X chromosome expression changes and autosomal expression changes due to MSL-mediated dosage compensation, we can determine which makes the larger contribution based on the RPKM, total mRNA, and cell count measurements. Using this information, we calculated the log-likelihood value for two hypotheses: Here hypothesis H 0 states that the expression of autosomes (A) remains the same and the expression of the X chromosome (X) decreases by half after RNAi treatment. Hypothesis H 1 states that the expression of autosomes (A) is increased by 2-fold after the RNAi treatment and the expression of X chromosome (X) remains the same. The expected sum of expression in the RNAi treated cells is 90% of wild type for H 0 and 180% for H 1. E is the measured mRNA per cell. In the duplicate RNA-Seq experiments, we obtained mRNA yields of 0.16 pg and 0.17 pg/cell from mock treated, 0.15 pg and 0.19 pg/cell from Msl2 knockdown, and 0.14 pg and 0.20 pg/cell from Mof knockdown S2 cells. The log-likelihood of H 0 – the log-likelihood of H 1 = 26.4 suggests that X chromosome expression change contributes more than autosomal expression change to the observed measurements of expression in wide type cells relative to RNAi treated cells. Comparative Genomic Hybridization (CGH) DNA was isolated from Drosophila S2-DRSC cells obtained from the Drosophila Genomics Resource Center (#181, Bloomington, IN) and from w1118 0–2 h embryos as described previously [48]. The isolated cell line and embryonic DNA were labeled with either Cy5 or Cy3 conjugated dUTP and subsequently hybridized to a custom Agilent genomic tiling array (GEO; GPL7787). Changes in copy number along each of the Drosophila chromosome arms were detected by a dynamic programming algorithm which divided each arm into the optimal number of copy number segments [49]. Accession Numbers All Seq and array data sets are available at GEO under accession number GSE16344. The CGH data set is available at modENCODE submission ID 596. Supporting Information Figure S1 Copy number determination by Bayesian Change Point Analysis of DNA-Seq read density. (1.12 MB PDF) Click here for additional data file. Figure S2 DNA-Seq densities of each copy number defined by DNA-Seq copy number calls or CGH copy number calls. (0.07 MB PDF) Click here for additional data file. Figure S3 RNA-Seq and array expression profiling. (2.01 MB PDF) Click here for additional data file. Table S1 Copy number segments based on DNA-Seq. (0.04 MB XLS) Click here for additional data file. Table S2 Copy number validation by DNA-Seq and CGH. (0.09 MB DOC) Click here for additional data file. Table S3 The number of genes in each copy number category. (0.03 MB DOC) Click here for additional data file.
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The Chromosomal High-Affinity Binding Sites for the Drosophila Dosage Compensation Complex
Introduction Genes residing on the single X chromosome in male Drosophila flies are transcribed at elevated rates to match the expression levels of the two X chromosomes in female cells. Transcriptional tuning in male cells depends on the activity of a ribonucleoprotein complex, the dosage compensation complex (DCC, also referred to as MSL [male-specific lethal] complex, reviewed in [1],[2]). Formation of DCC is male-specific due to the expression of the key subunit MSL2, which in turn drives the expression of the non-coding RNA components of the DCC, the roX (RNA on the X) RNAs [3],[4]. The complex associates almost exclusively with the X chromosome, which explains the selective activation of X chromosomal genes. This is at least in part due to the acetylation of lysine 16 of histone H4 (H4K16) by the histone acetyltransferase (HAT) MOF, an integral subunit of the DCC [5]. This modification may directly lead to unfolding of the chromatin fiber [6] or indirectly counteract factors that promote the formation of repressive chromatin [7],[8] rendering chromatin more permissive to the progress of transcription. The phenomenon of dosage compensation allows the study of general principles of transcriptional fine-tuning and chromosome-wide regulation. A key question is how the DCC is recruited specifically to the X chromosome. High-resolution mapping demonstrated that the complex targets transcriptionally active regions on the X chromosome with a preference for coding sequences [9],[10]. The DCC distribution pattern cannot be easily explained by a single targeting principle, but presumably results from the successive application of two or more distinct principles. Early genetic experiments led to a concept that assumes the existence of a relatively small number of X chromosome-specific primary recruitment or chromosomal ‘entry’ sites (CES) for the DCC, from which the complex would ‘spread’ to the bulk of chromosomal binding sites that differ qualitatively from the entry sites [11],[12]. Entry sites could, for example, be defined by a particular DNA sequence element, whereas features of active chromatin combined with proximity to entry sites would be a hallmark of secondary sites. Subsequent studies disputed whether DCC binding sites should be sorted into categories defined by different recruitment principles, or whether all targeting could be explained by a single principle (e.g. DNA sequence) that was applied to define sites of higher or lower affinity [13],[14],[15],[16]. Independent of whether primary recruitment sites differ from the bulk of DCC binding sites in quality or by a quantitative feature, they attract the DCC under stringent conditions. For example, DCC is recruited to high-affinity sites (HAS) even if they are removed from the X chromosomal context and inserted on an autosome, or at low levels of DCC (genetically achieved through expression of low amounts of MSL2) [16],[17], or if the integral DCC subunits MSL3, MLE, MOF or the roX RNAs are absent [18],[19],[20]. Under the latter circumstances binding sites are demarcated by binding of a sub-complex consisting of only MSL1 and MSL2 [19]. Evidently, distribution of DCC to sites of supposedly lower affinity depends on MOF, MSL3, MLE and the roX RNAs. More recently, Kuroda and colleagues obtained additional support for the concept that primary and secondary DCC binding sites are defined by different principles by showing that the binding to active chromatin in the vicinity of primary targeting sites is not X-specific [21]. Insertion of a roX gene in an autosome leads to extended ‘spreading’ of the DCC over the neighboring chromatin (both roX genes contain a HAS [11],[22]). Under these circumstances, the DCC associated with transcribed sequences on autosomes like it normally does on the X chromosome. Recruitment of DCC was suggested to involve binding to methylated histone H3 at lysine 36 (H3K36me3), a modification that is placed by histone methyltransferases associated with elongating RNA polymerase II (pol II) and hence marks sites of active transcription [21]. X chromosome-specific targeting may, therefore, be encoded by primary targeting sites. So far, just a few DNA elements that robustly fulfill the criteria for a primary targeting site have been characterized at the DNA sequence level. These include sites within the roX genes [22],[23], the Smr and Tao-1 genes [13] as well as a site that maps to cytological position 18D [15]. Due to this limited number, a defining feature with predictive value could not be extracted, although the presence of multiple distinct DNA sequence elements has been correlated with HAS [22],[24]. Strikingly, low complexity sequence elements such as GA- and CA-based dinucleotide repeats as well as runs of adenines have repeatedly been noted in these analyses [10],[13],[24]. Dissection of HAS DNA has yielded sub-fragments that retain limited binding activity. We therefore suggested that primary targeting is based on the local clustering of distinct sequence motifs [13],[24]. Progress on HAS definition requires moving the analysis from the anecdotal to the systematic level. We therefore mapped all DCC binding sites with highest affinities on a chromosome-wide scale by combining chromatin immunoprecipitation (ChIP) with probing of high-resolution DNA tiling arrays (ChIP-on-chip) under conditions where sites of higher affinity are preferentially visualized. This strategy not only allowed the generation of a sufficiently large training set for sequence analysis, but at the same time provided a means to directly compare the high affinity binding pattern with several chromatin features that have already been mapped along the Drosophila male X chromosome. Results We followed two complementary strategies for filtering the DCC binding sites with highest affinity from the chromosome-wide binding profile. First, we attempted to reproduce in male tissue culture cells the conditions that lead to selective visualization of HAS on polytene chromosomes in mutant larvae, where MSL1 and MSL2 interact selectively with HAS in the absence of MSL3, MLE or MOF [13],[18],[19]. Towards this goal we reduced the levels of these factors in the male Drosophila cell line SL2 by RNA interference (RNAi) and monitored the residual interaction of MSL1 or MSL2 (genetic studies have established the mutual interdependence of these two subunits for their interactions with the bulk of X chromosomal sites [19]). The second strategy followed the idea that HAS should, on average, show a higher occupancy by DCC and hence should be selectively obtained by ChIP if the extent of formaldehyde crosslinking was reduced. Lower levels of crosslinking should also reveal sites of more intimate contact of MSL proteins with DNA. Reassuringly, both strategies led to a similar alteration of the MSL binding pattern with enhanced peaks along all previously known HAS. The combined data should therefore help to define an inventory of sites with similar properties. Coding Sequences Have the Least Affinity for the DCC We lowered the levels of MSL3, MLE and MOF in SL2 cells by RNAi and mapped the residual binding pattern of the DCC core components MSL1 and MSL2 by ChIP-on-chip. All knock-down experiments were controlled for non-specific effects by a parallel RNAi treatment with an irrelevant dsRNA (which corresponds to glutathione-S-transferase (GST) sequences: ‘GST RNAi’). After 7 days of treatment with double-stranded (ds) RNA we achieved approx. 90% depletion of the target proteins as compared to the RNAi GST control (Figures 1A & B). Removal of MLE also led to reduction of MSL3 levels. Furthermore, RNAi-mediated depletion of MLE, MSL3 and MOF resulted in a substantial reduction of MSL1 to as little as 20%, indicating a global destabilization of the complex (Figure 1C). A similar drop in protein levels was observed for MSL2 (not shown). These circumstances should further facilitate selecting binding sites of only the highest affinity. 10.1371/journal.pgen.1000302.g001 Figure 1 Confirmation of RNAi target depletion by western blotting. Effect of (A) MOF RNAi on MOF and (B) MLE and MSL3 RNAi on corresponding protein levels. Relative amounts of target protein after interference normalized to α-tubulin are indicated. C) Effect of RNAi on MSL1 protein levels. Relative amounts of MSL1 are indicated. In flies a genetic knockout of MLE or MSL3 leads to the most pronounced reduction of MSL1-MSL2 binding [13]. We therefore first investigated the residual MSL1 profile after RNAi against MLE or MSL3 (Figure S1). The chromosomal interaction profile showed surprisingly mild effects: only 6 and 9% of all significant MSL1 binding events were lost upon MSL3 or MLE depletion, respectively. On all MSL1 target probes we observed a moderate reduction of MSL1 signals (Figure S1C). Conceivably, the remaining DCC subunits after incomplete knockdown may suffice to sustain MSL1 binding. However, in light of results from the analysis of mutant fly strains we consider more likely that the ChIP-on-chip methodology underestimates homogeneous inter-array differences and therefore might obscure a global reduction of MSL1 binding under knockdown conditions. This would be due to disproportional procedures such as array hybridization and scanning as well as signal normalization across arrays. Visual inspection of the binding pattern, however, allowed for the identification of loci where MSL1 association was substantially reduced (such as the small gene cluster in the right half of Figure S1A). This indicates, that MSL3 and MLE RNAi cause a local redistribution of MSL1, which should contribute to the identification of high affinity regions that are supposed to be more resistant to these perturbations. In the case of MSL3, examination of the loss of MSL1 binding within distinct functional regions revealed a significantly stronger reduction in coding sequences as compared to other binding regions (p-value 0.5 within 10 kb (X) or of 0.5 at a maximum of 2 kb (3R)], the latter most likely reflecting rather restricted or singular binding events blurred by the ChIP resolution of about 500 bases. Furthermore, MSL1 binding to autosomal sites not only remains upon reduction of MOF, MSL3 or MLE through RNAi, but is even increased under those conditions (Figure S3). This might reflect a re-distribution of MSL sub-complexes from the X to autosomes after elimination of the spreading component. Chromosomal Organization of Dosage Compensation The 130 HAS defined by our analysis are distributed all along the X chromosome with a preferred distance between 60–300 kb. If the HAS were the primary organizers of larger dosage compensation domains, we would expect a relationship between the robustness of transcriptional compensation of genes and their distance from the nearest sites. Figure 8 shows that this is actually the case. Dosage compensation reflected by the drop of transcript upon ablation of MSL2 by RNAi decreases with growing distance (Figure 8A). On the other hand, the further away a given gene is from a HAS, the more dependent is its compensation on the spreading factors MOF and MSL3 (Figures 8B & C). This finding demonstrates that the HAS we have identified play a role as organizers of compensated domains. 10.1371/journal.pgen.1000302.g008 Figure 8 Compensation of X-linked genes is dependent on distance from high-affinity sites (HAS). Based on microarray expression profiling studies the relation of mean fold changes (log2) in gene expression after RNAi and the genes' distance from the closest HAS is depicted. A distance of 0 indicates that the HAS is directly within the gene. Effects of MSL2 (A), MOF (B) and MSL3 (C) knockdown are displayed as boxplots on gene groups of varying distance. For clarity, extreme outliers have been omitted from the panels. Genes located more than 3 kb away from the next HAS respond significantly less to MSL2 RNAi (p-value 0.00087; two-sided t-test). On the contrary, upon MOF and MSL3 RNAi these genes are stronger affected (p-value 3.115e-05 and 5.804e-08, respectively). Discussion Combining differential crosslinking and RNAi interference against the DCC subunits previously shown to be required for the ‘spreading’ of the complex from high affinity or ‘entry’ sites we identified 131 high-affinity sites (HAS) of the Drosophila dosage compensation complex in male SL2 cells. This set of sites contains all previously identified HAS (or chromosomal entry sites, CES) and a representative selection colocalizes with interbands on polytene chromosomes that had been described as harboring primary binding sites for the DCC in previous genetic analyses. The sites we now identified thus have similar properties to the ones identified by genetic means. Our study not only provides a much large number of such sites, but also resolves their positions and widths much more precisely than enabled by the polytene chromosome analyses. Most importantly, our study suggests that the HAS have a function in dosage compensation since we observe a positive correlation between the proximity of genes to a HAS and the extent of dosage compensation. Conversely, the further away genes reside from the nearest HAS the more they depend on the spreading factors such as MOF or MSL3 for enhancement of transcription. The 130 X-chromosomal HAS are distributed all along the chromosome with a predominant spacing between 60 and 300 kb. The realm within which loci profit from the presence of a high affinity ‘DCC attraction center’ may be of the same order of magnitude. However, we generated the inventory of HAS by applying fairly stringent thresholding criteria. Less stringent selection criteria will undoubtedly reveal a large number of sites with degenerate features and lower affinities that may serve as ‘relay stations’ for DCC spreading and may contribute cumulatively to concentration of the DCC on the X chromosome[27]. Finally, the linear display of DCC–chromosome interactions in a browser obviously does not reflect the three-dimensional path and packaging of the chromosomal fiber, which might facilitate transfer of a chromatin-bound complex between distant loci. Under normal circumstances the DCC binds with high preference to transcribed and, indeed, coding sequences [9],[10]. Our observation that a transcribed region upstream of the Nej gene harbors a strong site in our set of binding sites but is not occupied in polytene chromosomes may, therefore, be due to differences in the transcription status between salivary glands and SL2 cells. Selection for sites of higher affinity leads to preferential loss of DCC from coding sequences, and under low-crosslinking conditions the majority of DCC binds at non-coding sequences in UTRs, introns, and also outside of the transcribed sequences in presumed regulatory and intergenic regions. Apparently, coding sequences have a lower affinity than non-coding sequences. At least part of the attraction of the DCC to transcribed sequences is due to the histone H3K36me3 mark, which is co-transcriptionally placed by Set2 and may provide a docking site for MSL3 [21]. However, this modification marks all transcribed sequences on autosomes as well and cannot be responsible for primary targeting. If, as suggested by this and previous work [10],[13],[24],[28], DNA sequence motifs contribute to DCC targeting, the observed preference for HAS outside of coding regions makes sense: assuming that binding affinity increases as sites conform with an idealized ‘consensus’ sequence, evolution of HAS with better defined sequences will be limited at coding regions where the main selective pressure is on preserving protein coding. If coding regions contain sequence elements that bind DCC they may, therefore, be of lower affinity and hence be preferentially lost as the stringency of the selection increases. Sequence analysis of the HAS did not lead to the identification of a single motif that could explain the HAS interaction pattern. Rather, we found low complexity sequences, in particular GA and CA dinucleotide repeats, generally enriched in HAS, but in no instance present in more than 50% of the sites. The results of the sequence analysis fluctuate considerably depending on the selected training set, the analysis parameters and algorithms used. The only motif that was found consistently within the set of HAS that is also enriched on the X chromosome is an almost perfect 11mer of GA. We previously identified similar repeats employing very different strategies [10],[13]. Blocks of GA are also important for targeting the DCC to a nucleosome-free region within the roX2 gene [22]. Recently, Kuroda and colleagues published a similar study including high resolution mapping of HAS of the Drosophila DCC [29]. Even though they used Drosophila embryos and different experimental approaches (e.g. genetic knockouts instead of RNAi and Solexa sequencing in addition to tiling array analysis) the results of the two studies match surprisingly well. In fact, 90 of our 130 X-chromosomal HAS perfectly overlap with the chromosomal entry sites (CES) from the Kuroda lab (the differences in sites may well be explained by the different transcriptional status of the cells/embryos employed in the two studies). The GA-based sequence motif that we found enriched in the HAS perfectly covers the consensus MSL response element of the Kuroda lab and they also observe a comparable histone depletion among their HAS. Using a reporter gene assay a role for the GA-rich sequence element in transcription activation was documented [29]. This not only confirms the suitability of our experimental approach but also reveals that a large fraction of HAS overlap in different specimens. How GA repeat motifs contribute to DCC loading is not known, but several scenarios may be considered. So far, a direct interaction of DCC subunits with specific DNA elements cannot be excluded. Further, DCC targeting may rely on interaction with an accessory protein with appropriate sequence preference, such as Pipsqueak or the GAGA factor (GAF) encoded by the Trithorax-like (Trl) gene. These two GAG-binding proteins colocalize at numerous sites on polytene chromosomes [30]. Hypomorph trl mutants show a male-specific lethality if the levels of MSL1 and MSL2 are reduced [31]. However, GAF only colocalizes with MSL2 at one out of 33 HAS and mutant larvae with strong Trl alleles show no obvious alteration of the DCC binding pattern on polytene X chromosomes. However, they display an increased number of autosomal binding sites, which may indicate a certain perturbation of targeting [31]. GA-rich elements may synergize with other DNA sequences (and hence other interacting factors) to form HAS, as previously suggested [13],[24]. Local clustering of two unrelated DNA sequence motifs, neither of which is particularly enriched on the X chromosome, appears to be crucial for targeting the DCC in C. elegans [32]. The affinity of a given DNA sequence for an interacting factor is strongly lowered by its nucleosomal organization [27]. Chromatin serves as a general thresholding system to present only those binding sites that reside in an appropriate non-nucleosomal context or benefit from nucleosome remodeling [33]. Interestingly, we find that the HAS, independent of whether they are located in regulatory regions, introns or outside of transcribed sequences, tend to be depleted of nucleosomes. Nucleosome depletion alone is not a stringent determinant of DCC association since many sites of low nucleosome density do not contain HAS or are not bound by the complex. Conversely, not all HAS are entirely nucleosome-free. Nevertheless, an improved definition of HAS may require considering the degree of nucleosome occupancy of sites in addition to the actual sequence itself. Nucleosome disruption may be brought about by ATP-dependent nucleosome remodeling or by competition of DCC binding with nucleosome assembly at the replication fork [34]. In the latter scenario the absence of nucleosomes would be a consequence of DCC binding rather than a requirement for interaction. Nucleosomes are also disrupted by the progression of the elongating RNA polymerase, a fact that may explain the recent observation that DCC binding to a sequence element within the MOF gene benefited from transcription [28]. Dinucleotide repeats and nucleosome depletion are also characteristic of autosomal MSL binding sites, however, these sites differ from HAS by two interesting features. First, we observed an altered stoichiometry of MSL proteins at autosomal sites, which often appear to lack MSL2. At these sites the colocalization of MSL1, MSL2 and MOF is the exception rather than the rule, suggesting that the known interdependence of MSL1 and MSL2 for chromosome association [19] is not absolute, but context-dependent. Second, binding of MSL proteins to autosomal sites appears unusually confined and does not spread onto the adjacent active chromatin as is commonly observed for X-chromosomal HAS. Lack of spreading is also found in the presence of MSL2. Because the distribution of MSL proteins from initial targeting sites is strongly facilitated by transcription of roX RNA from the same chromosome [11],[21],[35], we speculate that autosomal sites may be bound by MSL proteins in the absence of roX RNAs. Our data are consistent with a multi-step model of X chromosomal targeting by the DCC, which involves assembly of the complex with nascent roX RNA within the X chromosomal territory, followed by its diffusion to and concentration by the set of HAS, which we have identified in this study. Distribution to all target genes may then be brought about by large numbers of low affinity sites and the transcription-associated H3K36 methyl mark. Materials and Methods Drosophila Cell Culture and RNA Interference Cultivation of the male Drosophila cell line SL2 and RNA interference of target genes were carried out as described previously [36]. In brief, 1×106 SL2 cells were incubated with 10 µg dsRNA for 1 hour in serum-free medium. After addition of serum-containing medium, cells were incubated for 7 days at 26°C before chromatin preparation. Preparation of whole cells extracts and western blot confirmation of target gene knockdown has been described previously [36]. Depletion efficiency was quantified using a Li-Cor Odyssey system using α-tubulin as a reference. Sequences of primers used for dsRNA production are listed in Table S2. Chromatin IP SL2 cells were crosslinked in growth medium using 1% formaldehyde for 60 minutes in icewater. Alternatively we used 0.1% formaldehyde for 10 minutes at RT (low formaldehyde crosslinking). Fixation was quenched by addition of glycine to a final concentration of 125 mM. After washing, cells were resuspended in RIPA buffer and sonicated using a Bioruptor (Diagenode, Belgium) 8 times 30 seconds using the ‘high’ setting. Fragment size of the obtained chromatin was checked to be between 300 bp and 700 bp. Chromatin was precleared using a protein A/protein G-sepharose mixture for 1 hr at 4°C. 200 µl chromatin was incubated with appropriate amounts of antibodies in a total volume of 500 µl RIPA buffer at 4°C overnight. After washing and crosslink reversal, immunprecipitated nucleic acids were purified on GFX columns (GE Healthcare). Input chromatin serving as reference sample was treated accordingly. Overall, we performed immunoprecipitations for MSL1 (4 biological replicates) and MSL2 (2 replicates) on chromatin from untreated SL2 cells. In addition, we precipitated MSL1-containg chromatin after GST, MSL3, or MLE RNAi (2 replicates each). After low formaldehyde crosslinking, we performed ChIP for MSL2 from untreated cultures (2 replicates) and MSL1 IP after GST or MOF RNAi (3 replicates each). The rabbit polyclonal MSL1 and MSL2 antibodies used in this study were described elsewhere [10],[24]. Tiling Array Analysis Input and IP DNA were amplified using the WGA kit (Sigma) according to an online protocol (http://www.epigenome-noe.net/researchtools/protocol.php?protid30). Labeling and hybridization to NimbleGen arrays was carried out at ImaGenes (Berlin, Germany). We used a custom array layout (approx. 1 probe/100 bases) comprising the euchromatic part of the entire X chromosome, 5 MB of 2L, 2R and 3L, respectively, as well as 10 MB of 3R. Data analysis was performed using R/Bioconductor (www.R-project.org; www.bioconductor.org). Raw signals of corresponding experimental replicates were normalized using the ‘vsn’ package [37]. Enrichment statistics (IP versus input signals) were computed using the ‘sam’ algorithm within Bioconductor [38]. Fdr values of the sam statistic were determined using ‘locfdr’ [39]. Region summarization was performed using the HMM algorithm of TileMap [40]. Probes were considered to be bound significantly if the posterior probability of the HMM was greater than 0.5. Statistical tests and presentations were performed using R defaults if not indicated otherwise. Details about high-level computations are available upon request. Visualization was carried out by loading the mean enrichment ratios as GFF files into GBrowse (www.gmod.org). All data correspond to Drosophila genome version dm2 and annotation version gadfly 4.3. Raw data was deposited at the NCBI gene expression omnibus, GEO (data series GSE12292). Wild type profiles and locations of high-affinity sites are available for browsing at http://genome1.bio.med.uni-muenchen.de. Additional Data Sources The histone H3 profile and regions of histone depletion in SL2 cells were calculated from the GEO data series GSE8557 [21]. Gene expression changes upon RNAi of MSL2 in SL2 cells were derived from [41]. MOF and MSL3 knockout data were downloaded from ArrayExpress, accession E-MEXP-1505 [42]. Immuno-FISH on Drosophila Polytene Chromosomes FISH probes spanning the selected high-affinity sites were PCR amplified from genomic DNA. Primer sequences for the individual probes are listed in the supplement (Table S2). Immuno-FISH was performed exactly as described online (http://www.epigenome-noe.net/researchtools/protocol.php?protid4). Supporting Information Figure S1 MSL3 and MLE RNAi reduce MSL1 binding to coding sequences. A) Genome browser snapshot with gene spans and gene models. MSL1 binding profiles after GST, MSL3 and MLE RNAi are provided. Depicted is the log2 of the mean enrichment ratio (IP/Input) of 2 replicate experiments. B) Barplot showing the relative distribution of probes significantly bound by MSL1 after GST, MLE and MSL3 RNAi with respect to functional genomic context. Proximal probes are defined as those located within 500 bases up- or downstream of genes. C) Boxplot of changes in MSL1 enrichment after MLE and MSL3 RNAi on MSL1 target probes. Colour grouping of boxes corresponds to functional context. The left box of the duplicates corresponds to MLE RNAi , the right one to MSL3 RNAi. (1.78 MB TIF) Click here for additional data file. Figure S2 MSL1 binding is resistant to RNAi at high-affinity sites: Boxplots of probe-level MSL1 enrichment changes in MSL1 binding regions after RNAi divided into HAS and non-HAS probes for different RNAi experiments. P-values of two-sided t-tests are provided. (0.40 MB TIF) Click here for additional data file. Figure S3 Autosomal MSL1 sites are resistant to MOF RNAi. A) Absolute changes in the number of autosomal probes that are significantly enriched in MSL1 and (B) the corresponding relative changes. C) Differences in MSL1 signal on MSL1 target probes after MOF RNAi grouped by functional context. (0.30 MB TIF) Click here for additional data file. Table S1 List of all high-affinity sites identified by our approach. (0.03 MB XLS) Click here for additional data file. Table S2 Sequences of primers for generation of dsRNA and primers for FISH probe productions. (0.02 MB XLS) Click here for additional data file. Dataset S1 Exemplary MEME analysis output of the top 30 high-affinity sites. (0.20 MB DOC) Click here for additional data file. Dataset S2 Exemplary MEME analysis output of the top 20 autosomal MSL1 binding sites. (0.20 MB DOC) Click here for additional data file.
- Record: found
- Abstract: found
- Article: found
Quantitative evaluation and selection of reference genes in mouse oocytes and embryos cultured in vivo and in vitro
Solomon Mamo, Arpad Baji Gal, Szilard Bodo … (2007)
Background Real-time PCR is an efficient tool to measure transcripts and provide valuable quantitative information on gene expression of preimplantation stage embryos. Finding valid reference genes for normalization is essential to interpret the real-time PCR results accurately, and understand the biological dynamics during early development. The use of reference genes also known as housekeeping genes is the most widely applied approach. However, the different genes are not systematically compared, and as a result there is no uniformity between studies in selecting the reference gene. The goals of this study were to compare a wide selection of the most commonly used housekeeping genes in mouse oocytes and preimplantation stage embryos produced under different culture conditions, and select the best stable genes for normalization of gene expression data. Results Quantitative real time PCR method was used to evaluate 12 commonly used housekeeping genes (Actb, Gapdh, H2afz, Hprt, Ppia, Ubc, Eef1e1, Tubb4, Hist2h2aa1, Tbp, Bmp7, Polr2a) in multiple individual embryos representing six different developmental stages. The results were analysed, and stable genes were selected using the geNorm software. The expression pattern was almost similar despite differences in the culture system; however, the transcript levels were affected by culture conditions. The genes have showed various stabilities, and have been ranked accordingly. Conclusion Compared to earlier studies with similar objectives, we used a unique approach in analysing larger number of genes, comparing embryo samples derived in vivo or in vitro, analysing the expression in the early and late maternal to zygote transition periods separately, and using multiple individual embryos. Based on detailed quantification, pattern analyses and using the geNorm application, we found Ppia, H2afz and Hprt1 genes to be the most stable across the different stages and culture conditions, while Actb, the classical housekeeping gene, showed the least stability. We recommend the use of the geometric averages of those three genes for normalization in mouse preimplantation-stage gene expression studies.
Author and article information
Contributors
Youjun Zhang: Role: Editor
Journal
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS ONE
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
Title:
PLoS ONE
Publisher:
Public Library of Science
(San Francisco, USA
)
ISSN
(Electronic):
1932-6203
Publication date Collection: 2014
Publication date
(Electronic):
8
September
2014
Volume: 9
Issue: 9
Electronic Location Identifier: e106800
Affiliations
Author notes
* E-mail:
robertyuyi@
123456163.com
(YY);
zyifan@
123456saas.ac.cn
(YZ)
Article
Publisher ID: PONE-D-14-16000DOI: 10.1371/journal.pone.0106800
PMC ID: 4157791
PubMed ID: 25198611
SO-VID: ec70c320-3654-49be-a9dc-e632417acc8a
Copyright statement:
Copyright @
2014
License:
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
History
Date
received
: 10
April
2014
Date
accepted
: 2
August
2014
Page count
Pages: 9
Funding
Shandong Provincial Modern Agricultural Industry Technology System Innovation Team
Foundation, China (SDAIT-03-022-08) and Ministry of Agriculture Agricultural Research
Exceptional Talents and Innovation Team Foundation, China. The funders had no role
in study design, data collection and analysis, decision to publish, or preparation
of the manuscript.
Categories
Subject:
Research Article
Subject:
Biology and Life Sciences
Subject:
Agriculture
Subject:
Pest Control
Subject:
Pests
Subject:
Molecular Biology
Subject:
Molecular Biology Techniques
Subject:
Molecular Complexes
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