The Origin Recognition Complex Interacts with a Subset of Metabolic Genes Tightly Linked to Origins of Replication

The origin recognition complex (ORC) marks chromosomal sites as replication origins and is essential for replication initiation. In yeast, ORC also binds to DNA elements called silencers, where its primary function is to recruit silent information regulator (SIR) proteins to establish transcriptional silencing. Indeed, silencers function poorly as chromosomal origins. Several genetic, molecular, and biochemical studies of HMR-E have led to a model proposing that when ORC becomes limiting in the cell (such as in the orc2-1 mutant) only sites that bind ORC tightly (such as HMR-E) remain fully occupied by ORC, while lower affinity sites, including many origins, lose ORC occupancy. Since HMR-E possessed a unique non-replication function, we reasoned that other tight sites might reveal novel functions for ORC on chromosomes. Therefore, we comprehensively determined ORC “affinity” genome-wide by performing an ORC ChIP–on–chip in ORC2 and orc2-1 strains. Here we describe a novel group of orc2-1–resistant ORC–interacting chromosomal sites (ORF–ORC sites) that did not function as replication origins or silencers. Instead, ORF–ORC sites were comprised of protein-coding regions of highly transcribed metabolic genes. In contrast to the ORC–silencer paradigm, transcriptional activation promoted ORC association with these genes. Remarkably, ORF–ORC genes were enriched in proximity to origins of replication and, in several instances, were transcriptionally regulated by these origins. Taken together, these results suggest a surprising connection among ORC, replication origins, and cellular metabolism

Effects of Heat Treatment on the Microstructure and Properties of a Cast Nickel-Based High-Cr Superalloy

The effect of solution treatment and intermediate heat treatment on the microstructure and properties of a new cast nickel-based high-Cr superalloy was investigated in this paper. The results indicate that the tensile strength and elongation at 900 °C increase when the solution temperature increases from 1160 °C to 1180 °C and then decrease when the solution temperature changes from 1180 °C to 1200 °C and 1220 °C. The stress rupture test results of the high-Cr superalloy under conditions of 900 °C/275 MPa shows that the rupture time, elongation, and reduction of area initially increased and then decreased with the increase in solution treatment temperatures. The results of stress rupture tests for the alloy after intermediate heat treatment followed by furnace-cooling, air-cooling, and water-cooling show that the morphology and distribution of γ’ phase have a great influence on the tensile test results at 900 °C of the alloy but no obvious influence on the test at 900 °C/275 MPa. The microstructure analysis of the superalloy after heat treatment shows that: when the solution treatment temperatures are at 1200 °C and 1220 °C, the incipient melting appears in the interdendritic region, which can severely deteriorate mechanical properties; the morphology of γ′ phase changes gradually from cube to spherical; and a large number of fine γ’ phase precipitates in the γ channel are found with increasing cooling rate after intermediate heat treatment

Increasing the Accuracy of Mapping Urban Forest Carbon Density by Combining Spatial Modeling and Spectral Unmixing Analysis

Accurately mapping urban vegetation carbon density is challenging because of complex landscapes and mixed pixels. In this study, a novel methodology was proposed that combines a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), to map the forest carbon density of Shenzhen City of China, using Landsat 8 imagery and sample plot data collected in 2014. The independent variables that contributed to statistically significantly improving the fit of a model to data and reducing the sum of squared errors were first selected from a total of 284 spectral variables derived from the image bands. The vegetation fraction from LSUA was then added as an independent variable. The results obtained using cross-validation showed that: (1) Compared to the methods without the vegetation information, adding the vegetation fraction increased the accuracy of mapping carbon density by 1%–9.3%; (2) As the observed values increased, the LSR and kNN residuals showed overestimates and underestimates for the smaller and larger observations, respectively, while LMSR improved the systematical over and underestimations; (3) LSR resulted in illogically negative and unreasonably large estimates, while KNN produced the greatest values of root mean square error (RMSE). The results indicate that combining the spatial modeling method LMSR and the spectral unmixing analysis LUSA, coupled with Landsat imagery, is most promising for increasing the accuracy of urban forest carbon density maps. In addition, this method has considerable potential for accurate, rapid and nondestructive prediction of urban and peri-urban forest carbon stocks with an acceptable level of error and low cost

Primers and PCR reaction conditions used in this study.

<p>Primers and PCR reaction conditions used in this study.</p

Phylogenetic tree of 29 Amomi Fructus landraces constructed by the maximum parsimony method based on 35 single nucleotide polymorphisms (SNPs) from 4 loci (ITS, <i>LSU</i> D1–D3, <i>rbcL</i>, and <i>matK</i>).

<p>Each circle in the tree represents a unique SNP genotype (ST). Accession codes for each ST are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114940#pone-0114940-g002" target="_blank">Fig. 2</a>. Numbers on the tree branches are bootstrap values from 1,000 replicates. Only bootstrap values >60% are included.</p

Sequencing success, sequence quality, and barcode recovery of six regions.

<p>Sequencing success, sequence quality, and barcode recovery of six regions.</p

Minimum spanning tree analysis of 29 Amomi Fructus landraces based on the data set of 35 SNPs from four loci (ITS, <i>LSU</i> D1–D3, <i>rbcL</i>, and <i>matK</i>).

<p>Each circle in the tree represents a different SNP genotype (ST). The circle size is proportional to the number of landraces belonging to a ST. Numbers between circles represent the number of SNP differences. Two or more STs differing at two or fewer SNPs were regarded as a group (indicated by the gray shadow) and are connected with solid lines; those that differ by more than 2 SNPs are connected with dashed lines.</p

SNP Typing for Germplasm Identification of <i>Amomum villosum</i> Lour. Based on DNA Barcoding Markers

<div><p><i>Amomum villosum</i> Lour., produced from Yangchun, Guangdong Province, China, is a <i>Daodi</i> medicinal material of Amomi Fructus in traditional Chinese medicine. This herb germplasm should be accurately identified and collected to ensure its quality and safety in medication. In the present study, single nucleotide polymorphism typing method was evaluated on the basis of DNA barcoding markers to identify the germplasm of Amomi Fructus. Genomic DNA was extracted from the leaves of 29 landraces representing three <i>Amomum</i> species (<i>A</i>. <i>villosum</i> Lour., <i>A. xanthioides</i> Wall. ex Baker and <i>A. longiligulare</i> T. L. Wu) by using the CTAB method. Six barcoding markers (ITS, ITS2, <i>LSU</i> D1–D3, <i>matK, rbcL</i> and <i>trnH-psbA</i>) were PCR amplified and sequenced; SNP typing and phylogenetic analysis were performed to differentiate the landraces. Results showed that high-quality bidirectional sequences were acquired for five candidate regions (ITS, ITS2, <i>LSU</i> D1–D3, <i>matK</i>, and <i>rbcL</i>) except <i>trnH-psbA</i>. Three ribosomal regions, namely, ITS, ITS2, and <i>LSU</i> D1–D3, contained more SNP genotypes (STs) than the plastid genes <i>rbcL</i> and <i>matK</i>. In the 29 specimens, 19 STs were detected from the combination of four regions (ITS, <i>LSU</i> D1–D3, <i>rbcL</i>, and <i>matK</i>). Phylogenetic analysis results further revealed two clades. Minimum-spanning tree demonstrated the existence of two main groups: group I was consisting of 9 STs (ST1–8 and ST11) of <i>A. villosum</i> Lour., and group II was composed of 3 STs (ST16–18) of <i>A. longiligulare</i> T.L. Wu. Our results suggested that ITS and <i>LSU</i> D1–D3 should be incorporated with the core barcodes <i>rbcL</i> and <i>matK</i>. The four combined regions could be used as a multiregional DNA barcode to precisely differentiate the Amomi Fructus landraces in different producing areas.</p></div