Sequencing and characterisation of complete mitogenome DNA for Rasbora sarawakensis (Cypriniformes: Cyprinidae: Rasbora) with phylogenetic consideration

The Blueline Rasbora (Rasbora sarawakensis) is a small ray-finned fish categorized under the genus Rasbora in the Cyprinidae family. In this study, the complete mitogenome sequence of R. sarawakensis was sequenced using four primers targeting overlapping regions. The mitogenome is 16,709 bp in size, accommodating 22 transfer RNA genes, 13 protein-coding genes, two ribosomal RNA genes and a putative control region. Identical gene organisation was detected between this species and other genus counterparts. The heavy strand houses 28 genes while the light strand stores the other nine genes. Most protein-coding genes employ ATG as start codon, excluding COI gene, which utilizes GTG instead. The central conserved sequence blocks (CSB-F, CSB-E and CSB-D), variable sequence blocks (CSB-3, CSB-2 and CSB-1) as well as the terminal associated sequence (TAS) are conserved in the control region. The maximum likelihood phylogenetic tree revealed the divergence of R. sarawakensis from the basal region of the Rasbora clade, where its evolutionary relationships with R. maculatus and R. pauciperforata are poorly resolved as indicated by the low bootstrap values. This work acts as steppingstone towards further molecular evolution and population genetics studies of Rasbora genus in future.     

基质刚性影响声孔效应介导的单细胞基因转染

实验研究了基质刚性对单细胞质粒DNA转染效果的影响。实验采用高声压短脉冲(0.45MPa,10μs)条件的超声对培养在不同硬度凝胶基质(软的凝胶基质：0.2kPa,硬的凝胶基质：40kPa)上的力学敏感细胞NIH 3T3进行质粒DNA转染实验。实验结果表明,培养在硬的凝胶基质上的细胞,质粒DNA转染效率明显高于培养在软的凝胶基质上的细胞。进一步对质粒DNA进行荧光示踪可知培养在不同刚性基质上的细胞导入质粒DNA的方式不同。当细胞被培养在硬的凝胶基质上时,通过声致穿孔产生的小孔进入细胞内的质粒DNA更多,而培养在软的凝胶基质上的细胞,更多的质粒DNA可以通过非声致穿孔作用,例如内吞方式导入细胞。 细胞骨架蛋白分布规律表明,硬的凝胶基质上培养的细胞内有更多的F肌动蛋白微丝,可以更好地支撑起细胞的铺展形态,相对不容易发生内吞作用。而软的凝胶基质上培养的细胞内F肌动蛋白则更多以球形状态存在,细胞形貌骗向圆形,此时更容易发生胞吞作用。     

A Conformational Restriction Strategy for the Control of CRISPR/Cas Gene Editing with Photoactivatable Guide RNAs**

The CRISPR/Cas system is one of the most powerful tools for gene editing. However, approaches for precise control of genome editing and regulatory events are still desirable. Here, we report the spatiotemporal and efficient control of CRISPR/Cas9- and Cas12a-mediated editing with conformationally restricted guide RNAs (gRNAs). This approach relied on only two or three pre-installed photo-labile substituents followed by an intramolecular cyclization, representing a robust synthetic method in comparison to the heavily modified linear gRNAs that often require extensive screening and time-consuming optimization. This tactic could direct the precise cleavage of the genes encoding green fluorescent protein (GFP) and the vascular endothelial growth factor A (VEGFA) protein within a predefined cutting region without notable editing leakage in live cells. We also achieved light-mediated myostatin (MSTN) gene editing in embryos, wherein a new bow-knot-type gRNA was constructed with excellent OFF/ON switch efficiency. Overall, our work provides a significant new strategy in CRISPR/Cas editing with modified circular gRNAs to precisely manipulate where and when genes are edited.     

Mesoporous Silica Nanoparticles Decorated with Carbosilane Dendrons as New Non‐viral Oligonucleotide Delivery Carriers

A novel nanosystem based on mesoporous silica nanoparticles covered with carbosilane dendrons grafted on the external surface of the nanoparticles is reported. This system is able to transport single‐stranded oligonucleotide into cells, avoiding an electrostatic repulsion between the cell membrane and the negatively charged nucleic acids thanks to the cationic charge provided by the dendron coating under physiological conditions. Moreover, the presence of the highly ordered pore network inside the silica matrix would make possible to allocate other therapeutic agents within the mesopores with the aim of achieving a double delivery. First, carbosilane dendrons of second and third generation possessing ammonium or tertiary amine groups as peripheral functional groups were prepared. Hence, different strategies were tested in order to obtain their suitable grafting on the outer surface of the nanoparticles. As nucleic acid model, a single‐stranded DNA oligonucleotide tagged with a fluorescent Cy3 moiety was used to evaluate the DNA adsorption capacity. The hybrid material functionalised with the third generation of a neutral dendron showed excellent DNA binding properties. Finally, the cytotoxicity as well as the capability to deliver DNA into cells, was tested in vitro by using a human osteoblast‐like cell line, achieving good levels of internalisation of the vector DNA/carbosilane dendron‐functionalised material without affecting the cellular viability.     

Detection of p53 Gene by Using Genomagnetic Assay Combined with Carbon Nanotube Modified Disposable Sensor Technology

Electrochemical monitoring of DNA hybridization related to p53 gene sequence was investigated using genomagnetic assay combined with single walled carbon nanotube (SWCNT) modified pencil graphite electrodes (PGEs). The hybridization was performed either at magnetic beads (MB) surface or in solution. The enhanced guanine signal was obtained using SWCNT‐PGEs compared to one obtained by unmodified PGEs. The selectivity of genomagnetic assay was tested under optimum conditions. The DLs were calculated as 0.88 µM and 0.11 µM for hybridization performed at MB surface and solution, respectively. This selective, practical and cost effective genomagnetic assay combined with SWCNT‐PGEs is reported herein for the first time.     

Guanidinoamidized linear polyethyleneimine for gene delivery

Guanidine was introduced to low molecular weight linear polyethyleneimine(LPEI) via amide groups, to explore the effect of both guanidine degree and pendant chain length on its transfection behavior. The resulting guanidinoamidized LPEIs(GLPEIs) could dramatically reduce LPEI's toxicity, enhance its DNA-packaging capability, cellular uptake and therefore transfection efficiency. These polyplexes were taken up very efficiently via caveolae-mediated endocytosis and their transfection efficiencies in ovarian cancer cells were significantly improved compared to native LPEI10 k polyplexes. Among these GLPEIs, LPEI-C3-G100 showed higher DNA affinity even than LPEI25 k and the highest transfection efficiency, probably due to the optimization of polymer chain flexibility. Of notice, LPEI-C3-G100 polyplexes could more effectively accumulate into cytoplasm than LPEI25 k, although the transfection efficiency of LPEI-C3-G100 polyplexes was not superior to that of LPEI25 k polyplexes, which would be probably attributed to the more efficient release of LPEI25 k polyplexes than LPEI-C3-G100 polyplexes in the cytoplasm.     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.