Great Genetic Differentiation among Populations of Meconopsis integrifolia and Its Implication for Plant Speciation in the Qinghai-Tibetan Plateau

The complex tectonic events and climatic oscillations in the Qinghai-Tibetan Plateau (QTP), the largest and highest plateau in the world, are thought to have had great effects on the evolutionary history of the native plants. Of great interest is to investigate plant population genetic divergence in the QTP and its correlation with the geologic and climatic changes. We conducted a range-wide phylogeographical analysis of M. integrifolia based on the chloroplast DNA (cpDNA) trnL-trnF and trnfM-trnS regions, and defined 26 haplotypes that were phylogenetically divided into six clades dated to the late Tertiary. The six clades correspond, respectively, to highly differentiated population groups that do not overlap in geographic distribution, implying that the mountain ranges acting as corridors or barriers greatly affected the evolutionary history of the QTP plants. The older clade of M. integrifolia only occurs in the southwest of the species' range, whereas the distributions of younger clades extend northeastward in the eastern QTP, suggesting that climatic divergence resulting from the uplift of the QTP triggered the initial divergence of M. integrifolia native to the plateau. Also, the nrDNA ITS region was used to clarify the unexpected phylogenetic relationships of cpDNA haplotypes between M. integrifolia and M. betonicifolia. The topological incongruence between the two phylogenies suggests an ancestral hybridization between the two species. Our study indicates that geographic isolation and hybridization are two important mechanisms responsible for the population differentiation and speciation of Meconopsis, a species-rich genus with complex polyploids

Visualization of plasmid delivery to keratinocytes in mouse and human epidermis

The accessibility of skin makes it an ideal target organ for nucleic acid-based therapeutics; however, effective patient-friendly delivery remains a major obstacle to clinical utility. A variety of limited and inefficient methods of delivering nucleic acids to keratinocytes have been demonstrated; further advances will require well-characterized reagents, rapid noninvasive assays of delivery, and well-developed skin model systems. Using intravital fluorescence and bioluminescence imaging and a standard set of reporter plasmids we demonstrate transfection of cells in mouse and human xenograft skin using intradermal injection and two microneedle array delivery systems. Reporter gene expression could be detected in individual keratinocytes, in real-time, in both mouse skin as well as human skin xenografts. These studies revealed that non-invasive intravital imaging can be used as a guide for developing gene delivery tools, establishing a benchmark for comparative testing of nucleic acid skin delivery technologies

Phylogenetic Reconstruction and DNA Barcoding for Closely Related Pine Moth Species (Dendrolimus) in China with Multiple Gene Markers

Unlike distinct species, closely related species offer a great challenge for phylogeny reconstruction and species identification with DNA barcoding due to their often overlapping genetic variation. We tested a sibling species group of pine moth pests in China with a standard cytochrome c oxidase subunit I (COI) gene and two alternative internal transcribed spacer (ITS) genes (ITS1 and ITS2). Five different phylogenetic/DNA barcoding analysis methods (Maximum likelihood (ML)/Neighbor-joining (NJ), “best close match” (BCM), Minimum distance (MD), and BP-based method (BP)), representing commonly used methodology (tree-based and non-tree based) in the field, were applied to both single-gene and multiple-gene analyses. Our results demonstrated clear reciprocal species monophyly for three relatively distant related species, Dendrolimus superans, D. houi, D. kikuchii, as recovered by both single and multiple genes while the phylogenetic relationship of three closely related species, D. punctatus, D. tabulaeformis, D. spectabilis, could not be resolved with the traditional tree-building methods. Additionally, we find the standard COI barcode outperforms two nuclear ITS genes, whatever the methods used. On average, the COI barcode achieved a success rate of 94.10–97.40%, while ITS1 and ITS2 obtained a success rate of 64.70–81.60%, indicating ITS genes are less suitable for species identification in this case. We propose the use of an overall success rate of species identification that takes both sequencing success and assignation success into account, since species identification success rates with multiple-gene barcoding system were generally overestimated, especially by tree-based methods, where only successfully sequenced DNA sequences were used to construct a phylogenetic tree. Non-tree based methods, such as MD, BCM, and BP approaches, presented advantages over tree-based methods by reporting the overall success rates with statistical significance. In addition, our results indicate that the most closely related species D. punctatus, D. tabulaeformis, and D. spectabilis, may be still in the process of incomplete lineage sorting, with occasional hybridizations occurring among them

A New Method for Species Identification via Protein-Coding and Non-Coding DNA Barcodes by Combining Machine Learning with Bioinformatic Methods

Species identification via DNA barcodes is contributing greatly to current bioinventory efforts. The initial, and widely accepted, proposal was to use the protein-coding cytochrome c oxidase subunit I (COI) region as the standard barcode for animals, but recently non-coding internal transcribed spacer (ITS) genes have been proposed as candidate barcodes for both animals and plants. However, achieving a robust alignment for non-coding regions can be problematic. Here we propose two new methods (DV-RBF and FJ-RBF) to address this issue for species assignment by both coding and non-coding sequences that take advantage of the power of machine learning and bioinformatics. We demonstrate the value of the new methods with four empirical datasets, two representing typical protein-coding COI barcode datasets (neotropical bats and marine fish) and two representing non-coding ITS barcodes (rust fungi and brown algae). Using two random sub-sampling approaches, we demonstrate that the new methods significantly outperformed existing Neighbor-joining (NJ) and Maximum likelihood (ML) methods for both coding and non-coding barcodes when there was complete species coverage in the reference dataset. The new methods also out-performed NJ and ML methods for non-coding sequences in circumstances of potentially incomplete species coverage, although then the NJ and ML methods performed slightly better than the new methods for protein-coding barcodes. A 100% success rate of species identification was achieved with the two new methods for 4,122 bat queries and 5,134 fish queries using COI barcodes, with 95% confidence intervals (CI) of 99.75–100%. The new methods also obtained a 96.29% success rate (95%CI: 91.62–98.40%) for 484 rust fungi queries and a 98.50% success rate (95%CI: 96.60–99.37%) for 1094 brown algae queries, both using ITS barcodes