Sex-specific Doublesex^Mexpression in subsets of <it>Drosophila </it>somatic gonad cells

<p>Abstract</p> <p>Background</p> <p>In <it>Drosophila melanogaster</it>, a pre-mRNA splicing hierarchy controls sexual identity and ultimately leads to sex-specific Doublesex (DSX) transcription factor isoforms. The male-specific DSX^Mrepresses genes involved in female development and activates genes involved in male development. Spatial and temporal control of <it>dsx </it>during embryogenesis is not well documented.</p> <p>Results</p> <p>Here we show that DSX^Mis specifically expressed in subsets of male somatic gonad cells during embryogenesis. Following testis formation, germ cells remain in contact with DSX^M-expressing cells, including hub cells and premeiotic somatic cyst cells that surround germ cells during spermatogenesis in larval and adult testes.</p> <p>Conclusion</p> <p>We show that <it>dsx </it>is transcriptionally regulated in addition to being regulated at the pre-mRNA splicing level by the sex determination hierarchy. The <it>dsx </it>locus is spatially controlled by somatic gonad identity. The continuous expression of DSX^Min cells contacting the germline suggests an ongoing short-range influence of the somatic sex determination pathway on germ cell development.</p

A Non-Parametric Peak Calling Algorithm for DamID-Seq

<div><p>Protein—DNA interactions play a significant role in gene regulation and expression. In order to identify transcription factor binding sites (TFBS) of double sex (DSX)—an important transcription factor in sex determination, we applied the DNA adenine methylation identification (DamID) technology to the fat body tissue of <i>Drosophila</i>, followed by deep sequencing (DamID-Seq). One feature of DamID-Seq data is that induced adenine methylation signals are not assured to be symmetrically distributed at TFBS, which renders the existing peak calling algorithms for ChIP-Seq, including SPP and MACS, inappropriate for DamID-Seq data. This challenged us to develop a new algorithm for peak calling. A challenge in peaking calling based on sequence data is estimating the averaged behavior of background signals. We applied a bootstrap resampling method to short sequence reads in the control (Dam only). After data quality check and mapping reads to a reference genome, the peaking calling procedure compromises the following steps: 1) reads resampling; 2) reads scaling (normalization) and computing signal-to-noise fold changes; 3) filtering; 4) Calling peaks based on a statistically significant threshold. This is a non-parametric method for peak calling (NPPC). We also used irreproducible discovery rate (IDR) analysis, as well as ChIP-Seq data to compare the peaks called by the NPPC. We identified approximately 6,000 peaks for DSX, which point to 1,225 genes related to the fat body tissue difference between female and male <i>Drosophila</i>. Statistical evidence from IDR analysis indicated that these peaks are reproducible across biological replicates. In addition, these peaks are comparable to those identified by use of ChIP-Seq on S2 cells, in terms of peak number, location, and peaks width.</p></div

Relationships between local irreproducible discovery rate (idr) and log2 fold changes.

<p>Local idr is the probability that two peaks are irreproducible. We use two sets of peaks to illustrate the intrinsic association of local idr and log2 fold changes in DsxF: the set of 8,500 overlapping peaks (≥ 50% bp) between the two biological replicates (A through C), and the set of 6,000 reproducible peaks by IDR analysis (D through F). A and D: Plots of the log2 fold changes between the two replicates. B and E: Plots of the log2 fold changes against the local idr (in -log2 scale) in replicate 1. C and F: Similar plots in replicate 2. Comparisons of the two sets of peaks indicate that IDR analysis is essentially to find the low end cutoff of the log2 fold changes.</p

Distribution of peaks in gene features.

<p>A: On the basis of the <i>Drosophila melanogaster</i> gene annotation (Flybase.r5.38), we aligned the approximately 6,000 peaks of DsxF against the gene features. The majority of the peaks fall into less than one Kb promoters and intron regions, accounting for 61% together; followed by distal intergenic regions (9.7%) and 5′ UTR regions (5.1%). B: In order to identify the sequence motif of DSX binding sites, we performed a <i>de novo</i> search using the MEME algorithm (<a href="http://meme.nbcr.net/meme/" target="_blank">http://meme.nbcr.net/meme/</a>), based on the middle 200-bp DNA sequences of the reproducible peaks.</p

Number of peaks validated by the IDR analysis.

<p>A&B: Using the NPPC algorithm, we identified 6,157 and 5,836 between-replicate shared peaks for DsxF and DsxM, respectively. C&D: IDR analysis between biological replicates in DsxF and DsxM, respectively. According to Li et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117415#pone.0117415.ref008" target="_blank">8</a>], a correspondence curve describes the function between a parameter of top t% ranked peaks and corresponding rank intersection between the two replicates. The slope (y-axis) is the first derivative of the function against the parameter. The slope change from 0 to larger than 0 represents the decay point of inconsistency. E: The relationship between ranked peaks and the overall IDR. Corresponding to the top 6,000 peaks, the IDR is 5%.</p

Distribution of uniquely mapped reads on chromosome X of <i>Drosophila melanogaster</i>.

<p>A snapshot of the University of California, Santa Cruz (UCSC) genome browser shows the distribution of the mapped reads after scaling in reads number per million mapped reads. A and B: The mapped reads in the Dam-DsxM/+ and Dam only (control) genotypes, respectively. C: Reads distribution of Dam-DsxM/+ after the control signal subtraction. D: Distribution of the restriction enzyme <i>Dpnl</i>. Below this the annotated genes are displayed.</p