Analysis of the Transcriptome of Blowfly <i>Chrysomya megacephala</i> (Fabricius) Larvae in Responses to Different Edible Oils

<div><p>Background</p><p><i>Chrysomya megacephala</i> (Fabricius), a prevalent necrophagous blowfly that is easily mass reared, is noted for being a mechanical vector of pathogenic microorganisms, a pollinator of numerous crops, and a resource insect in forensic investigation in the postmortem interval. In the present study, in order to comprehensively understand the physiological and biochemical functions of <i>C. megacephala</i>, we performed RNA-sequencing and digital gene expression (DGE) profiling using Solexa/Illumina sequencing technology.</p><p>Methodology/Principal Findings</p><p>A total of 39,098,662 clean reads were assembled into 27,588 unigenes with a mean length of 768 nt. All unigenes were searched against the Nt database, Nr database, Swiss-Prot, Cluster of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genome (KEGG) with the BLASTn or BLASTx algorithm (E-value<0.00001) for annotations. In total, 7,081 unigenes and 14,099 unigenes were functionally classified into 25 COG categories and 240 KEGG pathways, respectively. Furthermore, 20,216 unigenes were grouped into 48 sub-categories belonging to 3 main Gene Ontology (GO) categories (ontologies). Using the transcriptome data as references, we analyzed the differential gene expressions between a soybean oil-fed group (SOF) and a lard oil-fed group (LOF), compared to the negative control group (NC), using the DGE approach. We finally obtained 1,566 differentially expressed genes in SOF/NC, and 1,099 genes in LOF/NC. For further analysis, GO and KEGG functional enrichment were performed on all differentially expressed genes, and a group of differentially expressed candidate genes related to lipometabolism were identified.</p><p>Conclusions/Significance</p><p>This study provides a global survey of <i>C. megacephal</i>a and provides the basis for further research on the functional genomics of this insect.</p></div

Histogram presentation of COG function classification of Unigenes.

<p>7,081 unigenes were classified functionally into 25 COG categories.</p

qRT-PCR validation of DGE results.

<p>The left y-axis indicates the relative expression level obtained by qRT-PCR (2^−ΔΔCt), which were presented as fold changes in gene expression normalized to the <i>actin</i> gene in each group, and the right y-axis indicates the TPM (transcripts per million mapped reads) obtained by DGE.</p

Changes in gene expression profiling among the different treatments.

<p>Up-(red) and down-regulated (green) unigenes were quantified and presented by histogram, comparisons of differentially expressed genes in SOF/NC and LOF/NC presented by Venn chart.</p

The KEGG pathways related to lipid metabolism in the third instar larvae of <i>C. megacephala</i>.

<p>The KEGG pathways related to lipid metabolism in the third instar larvae of <i>C. megacephala</i>.</p

Summary for annotation of unigenes (E-value<0.00001).

<p>Summary for annotation of unigenes (E-value<0.00001).</p

Histogram presentation of GO classification of Unigenes.

<p>20,216 unigenes were grouped into 48 sub-categories, which were divided into three categories: biological processes, cellular components, and molecular functions.</p

Summary for the Illumina sequencing and <i>de novo</i> assembly.

<p>N50 = median length of all non-redundant consensus sequences.</p

Robust and Antibacterial Polymer/Mechanically Exfoliated Graphene Nanocomposite Fibers for Biomedical Applications

With the increasing demand for composites of multifunctional and integrated performance, graphene-based nanocomposites have been attracting increasing attention in biomedical applications because of their outstanding physicochemical properties and biocompatibility. High product yields and dispersion of graphene in the preparation process of graphene-based nanocomposites have long been a challenge. Further, the mechanical properties and biosafety of final nanocomposites are very important for real usage in biomedical applications. Here, we presented a novel high-throughput method of graphene on mechanical exfoliation in a natural honey medium, and a yield of ∼91% of graphene nanoflakes can be easily achieved with 97.76% of single-layer graphenes. The mechanically exfoliated graphene (MEG) can be well-dispersed in the poly­(vinyl alcohol) (PVA) matrix. The PVA/MEG nanocomposite fibers are obtained by gel spinning and stretched 20 times. As a candidate for monofilament sutures, the PVA/MEG nanocomposite fibers with 0.3 wt % of MEG have an ultrahigh ultimate tensile strength of 2.1 GPa, which is far higher than that of the neat PVA fiber (0.75 GPa). In addition, the PVA/MEG nanocomposite fibers also have antibacterial property, low cytotoxicity, and other properties. On the basis of the above-mentioned properties, the effects of a common surgical suture and PVA/MEG nanocomposite fibers on wound healing are evaluated. As a result, the wounds treated with PVA/MEG nanocomposite fibers with 0.3 wt % of MEG show the best healing after 5 days of surgery. It is possible that this novel surgical suture will be available in the market relying on the gentle, inexpensive method of obtaining nonoxidized graphene and the simple process of obtaining nanocomposite fibers

Cluster analysis of the DGGE profiles of the predominant fecal bacteria of 15 patients in follow-up samples.

<p>Clustering was performed using Dice’s coefficient and UPGMA. <b>(a)</b> Cluster analysis of the DGGE profiles from the different groups. The metric scale denotes the degree of similarity. <b>(b)</b> MDS analysis of the cluster shown in (a). The plot is an optimized 3D representation of the similarity matrix obtained from BioNumerics software, and the x-, y-, and z-axes separately represent three different dimensions: Dim 1, Dim 2, and Dim 3. The Euclidean distance between two points reflects similarity. <b>(c)</b> PCA of fecal microbiota based on the DGGE fingerprinting shown in (a). The plot is reoriented to maximize variation among lanes along the first three principal components (the contributions 11.5, 20.0 and 26.7, respectively) obtained from BioNumerics software.</p