Genome-Wide Nucleosome Positioning Is Orchestrated by Genomic Regions Associated with DNase I Hypersensitivity in Rice

<div><p>Nucleosome positioning dictates the DNA accessibility for regulatory proteins, and thus is critical for gene expression and regulation. It has been well documented that only a subset of nucleosomes are reproducibly positioned in eukaryotic genomes. The most prominent example of phased nucleosomes is the context of genes, where phased nucleosomes flank the transcriptional starts sites (TSSs). It is unclear, however, what factors determine nucleosome positioning in regions that are not close to genes. We mapped both nucleosome positioning and DNase I hypersensitive site (DHS) datasets across the rice genome. We discovered that DHSs located in a variety of contexts, both genic and intergenic, were flanked by strongly phased nucleosome arrays. Phased nucleosomes were also found to flank DHSs in the human genome. Our results suggest the barrier model may represent a general feature of nucleosome organization in eukaryote genomes. Specifically, regions bound with regulatory proteins, including intergenic regions, can serve as barriers that organize phased nucleosome arrays on both sides. Our results also suggest that rice DHSs often span a single, phased nucleosome, similar to the H2A.Z-containing nucleosomes observed in DHSs in the human genome.</p></div

Patterns of nucleosome positioning around DHSs in the rice genome.

<p>The nucleosome positioning profiles were shown around the DHSs located in (A) proximal promoters (within 200 bp upstream of a TSS); (B) distal promoters (200–1000 bp upstream of a TSS); (C) within genes; (D) downstream regions of genes (within 200 bp downstream of gene transcription); (E) intergenic regions and (F) 10,000 randomly selected genomic regions. Y-axes show normalized reads (read number in per bp genome in per million reads) within 1 kb upstream and downstream around the DHSs. Ellipses indicate the nucleosomes within (grey) and outside (black) of DHSs. Arrows in (A–D) indicate the direction of gene transcription. Single-end MNase-seq reads were used in mapping nucleosome positioning.</p

Boxplots of estimated lengths of linkers (A) and spacing (B) between the phased nucleosomes mapped close to DHSs.

<p>"***","**","*" indicated <i>p</i><0.001, <i>p</i><0.01, <i>p</i><0.05, respectively, for the comparison of linker length/spacing between intergenic region and either regions within genes (“gene”) or in proximal promoters (“200 bp”).</p

Phased nucleosome arrays flanked TSSs of rice genes.

<p>(A) Nucleosome positioning profile associated with active genes. Phased nucleosome arrays are detectable after the TSSs. (B) Nucleosome positioning profile associated with non-expressed genes. Phased nucleosome arrays are detected on either side of the TSSs. (C) Distribution of DHS length for five different DHS categories. Note: the length of DHSs associated with proximal promoters (black line) are more variable than the lengths of other DHSs. (D) Heatmap of nucleosome positioning associated with active genes. Left panel: All expressed genes were sorted by the length of DHSs located in proximal promoters. The 5′ ends of the MNase-seq reads were mapped within 1 kb upstream and 1 kb downstream of the TSS of each gene to show the boundaries of nucleosomes core and linker. The red line on the left heatmap indicates the boundaries of DHSs. With the same order of the genes as in the left panel, the 5′ ends of DNase-seq reads (middle panel) and the fragments per kilobase of exon per million fragments mapped (FPKM) value log10 transformation (right panel) were mapped to show the DNase I sensitivity and the expression level of each gene, respectively.</p

Patterns of nucleosome positioning around DHSs in the human genome.

<p>DHSs (data from CD4+ T cell line) were also divided into five different categories based on their genomic locations: <b>(A)</b> proximal promoters (within 200 bp upstream of a TSS); <b>(B)</b> within genes; and <b>(C)</b> intergenic regions. Y-axes show normalized MNase-seq reads (read number in per bp genome in per million reads). Zero on the x-axes indicates the most sensitive site of the aligned DHSs. Ellipses indicate phased nucleosomes with H2A.Z. Arrows in (A, B) indicate the direction of gene transcription.</p

Nucleosome positioning profiles associated with DHSs with different lengths in proximal promoters.

<p>(A) DHSs in 320–480 bp. (B) DHSs in 200–320 bp. (C) DHSs in 140–200 bp. (D) DHSs in 80–140 bp. (E) DHSs in 20–80 bp. Y-axes show normalized reads of DNase-seq and MNase-seq. Zero on the X-axis indicates the boundary of DHSs toward short arm of the chromosomes. Black ellipses indicate the inferred nucleosomes. Grey ellipses indicate -1 nucleosomes within DHSs. Black vertical lines in (d, e) indicate the left and right boundaries of the DHSs inferred by DNase-seq reads.</p

The E-MS Algorithm: Model Selection With Incomplete Data

<div><p>We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models is finite. Some special cases of the procedure are considered, including E-MS with the generalized information criteria (GIC), and E-MS with the adaptive fence (AF; Jiang et al.). We prove numerical convergence of the E-MS algorithm as well as consistency in model selection of the limiting model of the E-MS convergence, for E-MS with GIC and E-MS with AF. We study the impact on model selection of different missing data mechanisms. Furthermore, we carry out extensive simulation studies on the finite-sample performance of the E-MS with comparisons to other procedures. The methodology is also illustrated on a real data analysis involving QTL mapping for an agricultural study on barley grains. Supplementary materials for this article are available online.</p></div

Classified Mixed Model Prediction

<p>Many practical problems are related to prediction, where the main interest is at subject (e.g., personalized medicine) or (small) sub-population (e.g., small community) level. In such cases, it is possible to make substantial gains in prediction accuracy by identifying a class that a new subject belongs to. This way, the new subject is potentially associated with a random effect corresponding to the same class in the training data, so that method of mixed model prediction can be used to make the best prediction. We propose a new method, called classified mixed model prediction (CMMP), to achieve this goal. We develop CMMP for both prediction of mixed effects and prediction of future observations, and consider different scenarios where there may or may not be a “match” of the new subject among the training-data subjects. Theoretical and empirical studies are carried out to study the properties of CMMP, including prediction intervals based on CMMP, and its comparison with existing methods. In particular, we show that, even if the actual match does not exist between the class of the new observations and those of the training data, CMMP still helps in improving prediction accuracy. Two real-data examples are considered. Supplementary materials for this article are available online.</p

A double agroinfiltration procedure to test candidate genes associated with potato late blight resistance mediated by the <i>RB</i> gene.

<p>All the pictures were taken at 10 days post infiltration and bars represent 2 cm. (A) Infiltration with <i>Agrobacterium</i> carrying pGR106-IpiO1 and HR response was observed around the infiltrated site. (B) Infiltration with <i>Agrobacterium</i> containing pHellsgate-8 silencing construct. (C) Double agroinfiltration with <i>Agrobacterium</i> carrying <i>Sgt1-RNAi</i> construct followed with pGR106-IpiO1. No HR was observed around the infiltrated site. (D) Double agroinfiltration with <i>Agrobacterium</i> carrying <i>Rar1-RNAi</i> construct followed with pGR106-IpiO1. HR response was observed around the infiltrated site.</p

RT-PCR analysis of transient silencing of the potato <i>Rar1</i> gene in two independent potato leaves (A and B).

<p>Leaf samples around the infiltrated spots were collected at days 1, 2, 5 and 6 dpi. Lane 1: 100 bp DNA ladder marker; Lane 2: leaf sample from un-infiltrated control; Lane 3: leaf from infiltrated site 1 dpi; Lane 4: leaf from infiltrated site 2 dpi; Lane 5: leaf from infiltrated site 5 dpi; Lane 6: leaf from infiltrated site 6 dpi. <i>Actin</i> was amplified as a control for the amount of template. The amplified <i>Rar1</i> and <i>Actin</i> transcripts are 339 bp and 360 bp, respectively.</p