DNA Barcoding for Efficient Species- and Pathovar-Level Identification of the Quarantine Plant Pathogen <i>Xanthomonas</i>

<div><p>Genus <i>Xanthomonas</i> comprises many economically important plant pathogens that affect a wide range of hosts. Indeed, fourteen <i>Xanthomonas</i> species/pathovars have been regarded as official quarantine bacteria for imports in China. To date, however, a rapid and accurate method capable of identifying all of the quarantine species/pathovars has yet to be developed. In this study, we therefore evaluated the capacity of DNA barcoding as a digital identification method for discriminating quarantine species/pathovars of <i>Xanthomonas</i>. For these analyses, 327 isolates, representing 45 <i>Xanthomonas</i> species/pathovars, as well as five additional species/pathovars from GenBank (50 species/pathovars total), were utilized to test the efficacy of four DNA barcode candidate genes (16S rRNA gene, <i>cpn60</i>, <i>gyrB</i>, and <i>avrBs2</i>). Of these candidate genes, <i>cpn60</i> displayed the highest rate of PCR amplification and sequencing success. The tree-building (Neighbor-joining), ‘best close match’, and barcode gap methods were subsequently employed to assess the species- and pathovar-level resolution of each gene. Notably, all isolates of each quarantine species/pathovars formed a monophyletic group in the neighbor-joining tree constructed using the <i>cpn60</i> sequences. Moreover, <i>cpn60</i> also demonstrated the most satisfactory results in both barcoding gap analysis and the ‘best close match’ test. Thus, compared with the other markers tested, <i>cpn60</i> proved to be a powerful DNA barcode, providing a reliable and effective means for the species- and pathovar-level identification of the quarantine plant pathogen <i>Xanthomonas</i>.</p></div

Neighbor-joining tree based on <i>gyrB</i>.

<p>Bootstrap values (>50%) are shown above the branches. Identical sequences are represented only once. Numbers following taxon names indicate the number of isolates. Red dots indicate quarantine <i>Xanthomonas</i> species/pathovars. Species/pathovars that were successfully identified are shown on the right.</p

Histograms of the frequencies (y-axes) of pairwise intra-species/pathovar (dark gray bars) and inter-species/pathovar (light gray bars) divergences based on the uncorrected K2P distance (x-axes) for each candidate gene.

<p>Histograms of the frequencies (y-axes) of pairwise intra-species/pathovar (dark gray bars) and inter-species/pathovar (light gray bars) divergences based on the uncorrected K2P distance (x-axes) for each candidate gene.</p

Neighbor-joining tree based on <i>avrBs2</i>.

Bootstrap values (>50%) are shown above the branches. Identical sequences are represented only once. Numbers following taxon names indicate the number of isolates. Red dots indicate quarantine Xanthomonas species/pathovars. Species/pathovars that were successfully identified are shown on the right.</p

Identification success based on “best close match” method.

<p>Identification success based on “best close match” method.</p

Neighbor-joining tree based on <i>cpn60</i>.

<p>Bootstrap values (>50%) are shown above the branches. Identical sequences are represented only once. Numbers following taxon names indicate the number of isolates. Red dots indicate quarantine <i>Xanthomonas</i> species/pathovars. Species/pathovars that were successfully identified are shown on the right.</p

Primers and PCR conditions used for DNA sequence amplifications in this study.

<p>Primers and PCR conditions used for DNA sequence amplifications in this study.</p

Neighbor-joining tree based on 16S rRNA gene.

<p>Bootstrap values (>50%) are shown above the branches. Identical sequences are represented only once. Numbers following taxon names indicate the number of isolates. Red dots indicate quarantine <i>Xanthomonas</i> species/pathovars. Species/pathovars that were successfully identified are shown on the right.</p

Sample sizes, success rates of amplification and sequencing, and sequence characteristics of the four DNA regions in the <i>Xanthomonas</i> species assessed in this study.

<p>Sample sizes, success rates of amplification and sequencing, and sequence characteristics of the four DNA regions in the <i>Xanthomonas</i> species assessed in this study.</p

Visualizing and Quantifying Wettability Alteration by Silica Nanofluids

An aqueous suspension of silica nanoparticles or nanofluid can alter the wettability of surfaces, specifically by making them hydrophilic and oil-repellent under water. Wettability alteration by nanofluids has important technological applications, including for enhanced oil recovery and heat transfer processes. A common way to characterize the wettability alteration is by measuring the contact angles of an oil droplet with and without nanoparticles. While easy to perform, contact angle measurements do not fully capture the wettability changes to the surface. Here, we employed several complementary techniques, such as cryo-scanning electron microscopy, confocal fluorescence and reflection interference contrast microscopy, and droplet probe atomic force microscopy (AFM), to visualize and quantify the wettability alterations by fumed silica nanoparticles. We found that nanoparticles adsorbed onto glass surfaces to form a porous layer with hierarchical micro- and nanostructures. The porous layer can trap a thin water film, which reduces contact between the oil droplet and the solid substrate. As a result, even a small addition of nanoparticles (0.1 wt %) lowers the adhesion force for a 20 μm sized oil droplet by more than 400 times from 210 ± 10 to 0.5 ± 0.3 nN as measured by using droplet probe AFM. Finally, we show that silica nanofluids can improve oil recovery rates by 8% in a micromodel with glass channels that resemble a physical rock network