Molecular identification and mapping of a novel stripe rust resistance gene in wheat resistance line CH5389

<div><p>ABSTRACT: Stripe rust, caused by Puccinia striiformis is one of the most destructive diseases of wheat worldwide. CH5389 is a wheat-Thinopyrum intermedium derived line conferring stripe rust resistance. Genetic analyses of seedlings of F2 populations and F2:3 families developed by crossing CH5389 and susceptible common wheat revealed that stripe rust resistance in CH5389 was controlled by a single dominant gene that was designated YrCH5389. Eight SSR and EST-PCR polymorphic markers on chromosome 3AL were identified in F2 population of CH5389/Taichung29. The YrCH5389 was flanked by EST marker BE405348 and SSR marker Xwmc388 on chromosome 3AL with genetic distances of 2.2 and 4.6 cM, respectively. Comparative genomic analysis demonstrated that the orthologous genomic region of YrCH5389 covered 990 kb in rice, 640 kb in Brachypodium, and 890 kb in sorghum. Based on the locations of the markers, the resistance gene was located to chromosome deletion bin 3AL-0.85-1.00. Because there are no officially named stripe rust resistance genes on the 3AL chromosome, the YrCH5389 should be designated as a new resistance gene. These linkage markers could be useful for marker-assisted selection in wheat resistance breeding.</p></div

<i>In Vivo</i> Pyro-SIP Assessing Active Gut Microbiota of the Cotton Leafworm, <i>Spodoptera littoralis</i>

<div><p>The gut microbiota is of crucial importance for the host with considerable metabolic activity. Although great efforts have been made toward characterizing microbial diversity, measuring components' metabolic activity surprisingly hasn't kept pace. Here we combined pyrosequencing of amplified 16S rRNA genes with <i>in vivo</i> stable isotope probing (Pyro-SIP) to unmask metabolically active bacteria in the gut of cotton leafworm (<i>Spodoptera littoralis</i>), a polyphagous insect herbivore that consumes large amounts of plant material in a short time, liberating abundant glucose in the alimentary canal as a most important carbon and energy source for both host and active gut bacteria. With ¹³C glucose as the trophic link, Pyro-SIP revealed that a relatively simple but distinctive gut microbiota co-developed with the host, both metabolic activity and composition shifting throughout larval stages. <i>Pantoea</i>, <i>Citrobacter</i> and <i>Clostridium</i> were particularly active in early-instar, likely the core functional populations linked to nutritional upgrading. <i>Enterococcus</i> was the single predominant genus in the community, and it was essentially stable and metabolically active in the larval lifespan. Based on that <i>Enterococci</i> formed biofilm-like layers on the gut epithelium and that the isolated strains showed antimicrobial properties, <i>Enterococcus</i> may be able to establish a colonization resistance effect in the gut against potentially harmful microbes from outside. Not only does this establish the first in-depth inventory of the gut microbiota of a model organism from the mostly phytophagous Lepidoptera, but this pilot study shows that Pyro-SIP can rapidly gain insight into the gut microbiota's metabolic activity with high resolution and high precision.</p></div

Sugar composition in the gut content of cotton leafworm.

<p>(<b>A</b>) Sugars in the gut content of larvae fed on cotton or (<b>B</b>) artificial diet by GC-MS characterization after aldononitrile acetate derivatization. The larval alimentary canal is divided into three regions: foregut, midgut and hindgut, shown in diagram (<b>C</b>). (<b>D</b>) Quantification of dominant glucose reveals a significant decrease in average content along the gut. However, cotton-feeding larvae exhibit higher amount of glucose in all gut regions. <b>*</b> and <b>^a</b> indicate significant difference: P (<b>*</b>¹) = 0.0020, P (<b>*</b>²) = 0.0366, P (^a) = 0.0017. Error bars indicate standard errors. 1, Ribose; 2, Arabinose; 3, Mannose; 4, Glucose; 5, Galactose.</p

Frequency of 16S rRNA sequences in the microbiota obtained from the native-glucose control (bacterial relative abundance) and [¹³C]-glucose treatment (bacterial metabolic activity), represented as a heatmap.

<p>Left panel displays dynamic changes of taxa in early-instar larvae and right panel for late-instar. Warm colors indicate higher and cold colors lower abundance, calculated according to the formula in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085948#pone-0085948-g002" target="_blank">Fig. 2A</a> (also see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085948#pone-0085948-g005" target="_blank">Fig. 5</a> for the percentage of taxa).</p

Bacterial diversity and relative abundance in the gut microbiota of late-instar larvae.

<p>(<b>A</b>) Rarefaction curves of 16S rDNA sequences were obtained from representative SIP fractions of early-instar and late-instar larvae. Abbreviations: E, representative fractions from early-instar larvae fed on ¹³C-glucose; L, fractions from late-instar larvae fed on ¹³C-glucose. (<b>B</b>) Relative abundance of bacterial taxa in different SIP fractions, represented in a relative area graph as revealed by pyrosequencing. Abbreviations are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085948#pone-0085948-g003" target="_blank">Fig. 3</a>.</p

Phylogenetic analysis of (a) Firmicutes and (b) Proteobacteria identified from the gut microbiota of cotton leafworm.

<p>(<b>A</b>) Maximum Likelihood tree was derived from partial 16S rDNA sequence data for members of Firmicutes. (<b>B</b>) Neighbor-Joining tree was derived from partial 16S rDNA sequence data for members of Proteobacteria. Representative pyrosequences from this work and near full-length 16S rDNA sequences retrieved from previous clone-library-based studies are indicated by black circles (•) and blue circles, respectively. Labeled taxa are marked with triangles (▴). Reference sequences are downloaded from GenBank (accession numbers are in parentheses.). <i>Methanosarcina barkeri</i> (AF028692) is used as an outgroup. Family-level clusters are indicated by different colors. Bootstrap values (in percent) are based on 1000 replications. Bar represents 2% sequence divergence. Right section denotes percentage of representative bacterial 16S rRNA sequences in the total dataset of each sample. Abbreviations: +¹²C, native-glucose amendment; +¹³C, ¹³C-glucose amendment; L, light fractions; H, heavy fractions.</p

Bacterial diversity and relative abundance in the gut microbiota of early-instar larvae.

<p>(<b>A</b>) Rarefaction curves of 16S rDNA sequences were obtained from representative SIP fractions of the control ([¹²C]) and labeling treatment ([¹³C]). (<b>B</b>) Relative abundance of bacterial taxa in different SIP fractions, represented in a relative area graph as revealed by pyrosequencing. Abbreviations: [¹²C] Light, light fractions (fractions 9–11, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085948#pone-0085948-g002" target="_blank">Fig. 2A</a>) of native-glucose amendment; [¹²C] Middle, middle fraction (fraction 7) of that; [¹²C] Heavy, heavy fractions (fractions 4–5) of that; [¹³C] Light, light fractions of ¹³C-glucose amendment; [¹³C] Middle, middle fraction of that; [¹³C] Heavy, heavy fractions of that.</p

Freestanding Triboelectric Nanogenerator Enables Noncontact Motion-Tracking and Positioning

Recent development of interactive motion-tracking and positioning technologies is attracting increasing interests in many areas, such as wearable electronics, intelligent electronics, and the internet of things. For example, the so-called somatosensory technology can afford users strong empathy of immersion and realism due to their consistent interaction with the game. Here, we report a noncontact self-powered positioning and motion-tracking system based on a freestanding triboelectric nanogenerator (TENG). The TENG was fabricated by a nanoengineered surface in the contact-separation mode with the use of a free moving human body (hands or feet) as the trigger. The poly­(tetrafluoroethylene) (PTFE) arrays based interactive interface can give an output of 222 V from casual human motions. Different from previous works, this device also responses to a small action at certain heights of 0.01–0.11 m from the device with a sensitivity of about 315 V·m^–1, so that the mechanical sensing is possible. Such a distinctive noncontact sensing feature promotes a wide range of potential applications in smart interaction systems