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.     

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.     

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.     

A hybrid nanoassembly for ultrasound-inducible cytosolic siRNA delivery and cancer sono-gene therapy

Cancer gene therapy by small-interfering RNAs (siRNAs) holds great promise but is impeded by a low cytoplasmic delivery efficiency. The past two decades have witnessed many efforts that are dedicated to discover biomaterials in order to increase cellular uptake efficiency of siRNAs. However, less attention has been paid to the lysosomal trapping dilemma that greatly restricts gene silencing outcomes. Herein, to address this challenge, we developed a sono-controllable strategy for ultrasound-promoted cytosolic siRNA delivery. A hybrid nanoassembly (HNA) was prepared via electrostatic self-assembly of a siRNA and a nona-arginine modified with protoporphyrin IX that is a sonosensitizer. After cellular uptake and exposure to sono-irradiation, HNA generated singlet oxygen to facilitate the lysosomal escape of siRNA to knock down anti-apoptotic Bcl-2 in the cytoplasm. We showed that the colocalization ratios between siRNA and the lysosome decreased from 91 % to 33 % post sono-irradiation; meanwhile, the gene silencing efficacy increased from 46 % to 68 % at 300 nM of HNA. Furthermore, sonodynamic therapy was achieved by the sonosensitizer under ultrasound irradiation, which combined gene therapy to eradicate cancer cells, resulting in a cell death rate of 82 %. This study thus presents a novel ultrasonic approach for effective cytoplasmic delivery of siRNAs and combinational sono-gene therapy of cancer.     

In situ vaccination and gene-mediated PD-L1 blockade for enhanced tumor immunotherapy

Despite of the promising achievements of immune checkpoints blockade therapy (ICB) in the clinic, which was often limited by low objective responses and severe side effects. Herein, we explored a synergistic strategy to combine in situ vaccination and gene-mediated anti-PD therapy, which was generated by unmethylated cytosine-phosphate-guanine (CpG) and pshPD-L1 gene co-delivery. PEI worked as the delivery carrier to co-deliver the CpG and pshPD-L1 genes, the formed PDC (PEI/DNA/CpG) nanoparticles were further shielded by aldehyde modified polyethylene glycol (OHC-PEG-CHO) via pH responsive Schiff base reaction for OHC-PEG-CHO-PEI/DNA/CpG nanoparticles (P(PDC) NPs) preparation. All steps could be finished within 30 min. Such simple nanoparticles achieved the synergistic antitumor efficacy in B16F10 tumor-bearing mice, and the amplified T cell responses, together with enhanced NK cells infiltration were observed after the combined treatments. In addition, the pH responsive delivery system reduced the side effects triggered by anti-PD therapy. The facile and effective combination strategy we presented here might provide a novel treatment for tumor inhibition.     

Circumvent PEGylation dilemma by implementing matrix metalloproteinase-responsive chemistry for promoted tumor gene therapy

Surface modification by poly(ethylene glycol) (PEGylation) has been acknowledged as a powerful strategy in minimizing non-specific reactions for biomedical devices. Once applied into manufacture of drug/gene delivery systems, PEGylation has demonstrated to significantly improve their biocompatibility and stealthiness in physiological environment. Nonetheless, reluctant cell membrane affinities thus cellular uptake efficiencies owing to PEGylation brought up further issues that are imperative to be resolved. Pertain to this PEGylation dilemma, we attempted to introduce peptide (GPLGVRG) linkage between block copolymer of PEG-poly{N'-[N-(2-aminoethyl)-2-aminoethyl]aspartamide} PAsp(DET), wherein the cationic PAsp(DET) could self-assemble with pDNA into nanoscaled complex core. Noteworthy was the peptide linkage whose amino acids sequence could be specifically recognized and degraded by matrix metalloproteinases (MMPs) (overexpressed in extracellular milieu of tumors). Therefore, our subsequent studies validated facile detachment of PEGylation from the aforementioned polyplex micelles upon treatment of MMPs, which elicited improved cytomembrane affinities and cellular uptake efficiencies. In addition, promoted escape from endosome entrapment was also confirmed through direct endosome membrane destabilization by PAsp(DET), which was further elucidated to be attributable to dePEGylation as well as elevated charged density of PAsp(DET) in acidic endosomes. These benefits from dePEGylation eventually contributed to promoted gene expression at the affected cells and potent tumor growth suppression based on anti-angiogenic approach. Therefore, our developed strategy has provided a facile approach in overcoming the dilemma of PEGylation, which could be informative in design of drug/gene delivery systems.