Zhifeng Shao

Redox-responsive micelles self-assembled from dynamic covalent block copolymers for intracellular drug delivery.

Redox-responsive micelles self-assembled from dynamic covalent block copolymers with double disulfide linkage in the backbone have been developed successfully. The amphiphilic block copolymers PEG-PLA associated with complementary H-bonding sequences can self-assemble into spherical micelles in aqueous media with sizes from 34 nm to 107 nm with different molar mass of PEG and PLA. Moreover, in vitro drug release analyses indicate that reductive environment can result in triggered drug release profiles. The glutathione (GSH) mediated intracellular drug delivery was investigated against HeLa human cervical carcinoma cell line. Flow cytometry and fluorescence microscopy measurements demonstrated that the micelles exhibited faster drug release in glutathione monoester (GSH-OEt) pretreated HeLa cells than that in the nonpretreated cells. Cytotoxicity assay of DOX-loaded micelles indicated the higher cellular proliferation inhibition against 10 mM of GSH-OEt pretreated HeLa cells than that of the nonpretreated ones. These reduction-responsive, biodegradable and biocompatibility micelles could provide a favorable platform to construct excellent drug delivery systems for cancer therapy.

Chitosan oligosaccharide copolymer micelles with double disulphide linkage in the backbone associated by H-bonding duplexes for targeted intracellular drug delivery

A folic acid (FA) conjugated chitosan oligosaccharide (CSO) polylactic acid (PLA) copolymer FA-CSO-PLA with double disulphide linkage in the backbone directed by H-bonding association duplex was synthesized, and its self-assembled micelles were evaluated as smart targeted drug delivery carriers. Both of the intermediates and the terminal copolymers were characterized by 1H-NMR and gel permeation chromatography (GPC). The critical micelle concentration (CMC) value is 0.045 mg mL−1 which suggests the micelles are highly stable in dilute solution. TEM and DLS further confirmed the successful formation of micelles with an average size of 61 and 100 nm, PDI of 0.209 and 0.230 for blank and DOX loaded micelles, respectively. The micelles were destructed under a reductive environment, leading to encapsulated drug release. Moreover, fluorescence microscopy demonstrated that the micelles exhibited both a passive and active targeting ability in HeLA cells due to an EPR effect and folate-mediated endocytosis. These results suggested the micelles would provide a favourable platform for constructing excellent drug delivery systems for cancer therapy.

Identification of serum biomarkers for gastric cancer diagnosis using a human proteome microarray

We aimed to globally discover serum biomarkers for diagnosis of gastric cancer (GC). GC serum autoantibodies were discovered and validated using serum samples from independent patient cohorts encompassing 1,401 participants divided into three groups, i.e. healthy, GC patients, and GC-related disease group. To discover biomarkers for GC, the human proteome microarray was first applied to screen specific autoantibodies in a total of 87 serum samples from GC patients and healthy controls. Potential biomarkers were identified via a statistical analysis protocol. Targeted protein microarrays with only the potential biomarkers were constructed and used to validate the candidate biomarkers using 914 samples. To provide further validation, the abundance of autoantibodies specific to the biomarker candidates was analyzed using enzyme-linked immunosorbent assays. Receiver operating characteristic curves were generated to evaluate the diagnostic accuracy of the serum biomarkers. Finally, the efficacy of prognosis efficacy of the final four biomarkers was evaluated by analyzing the clinical records. The final panel of biomarkers consisting of COPS2, CTSF, NT5E, and TERF1 provides high diagnostic power, with 95% sensitivity and 92% specificity to differentiate GC patients from healthy individuals. Prognosis analysis showed that the panel could also serve as independent predictors of the overall GC patient survival. The panel of four serum biomarkers (COPS2, CTSF, NT5E, and TERF1) could serve as a noninvasive diagnostic index for GC, and the combination of them could potentially be used as a predictor of the overall GC survival rate.

Reductive triblock copolymer micelles with a dynamic covalent linkage deliver antimiR-21 for gastric cancer therapy

A reductive tri-block copolymer PEG-SS-PLA-SS-PEI with a double disulphide linkage in the backbone directed by H-bonding association was synthesized and self-assembled into cationic polymeric nanomicellar particles for in vivo antimiRNA delivery with an average diameter of 68 nm and a zeta potential of approximately 39 mV. The chemical structure of the copolymer was well characterized by 1H NMR and GPC. The cationic polymeric nanomicellar particles could be unpacked in an intracellular reductive environment (GSH) leading to the release of encapsulated antimiRNA. MTT assays in vitro showed no significant cytotoxicity of SGC7901 gastric cancer cells incubated with PEG-SS-PLA-SS-PEI micelles. The in vitro study indicated that the micelle-based antimiR-21 delivery system could effectively facilitate cellular uptake and greatly down-regulate the expression level of miR-21 in SGC7901 cell lines, which was comparable to Lipofectamine™ 2000. The down regulation of miR-21 remarkably induced apoptosis, suppressed the tumor cell migration and invasion, and increased the expression of target genes such as phosphatase and tensin homolog deleted on chromosome ten (PTEN) and Programmed Cell Death Protein 4 (PDCD4). More importantly, the in vivo systemic administration of the micelles/antimiR-21 complex in a gastric cancer model significantly inhibited tumor growth and increased the expression of target genes. The nanoparticle based on the PEG-SS-PLA-SS-PEI copolymer would be a safe and efficient carrier for delivery of therapeutic antimiRNA, which shows a prospective therapy method in gastric cancer.

Optimization of chemical fungicide combinations targeting the maize fungal pathogen, Bipolaris maydis: a systematic quantitative approach.

To control the southern corn leaf blight, a severe disease of maize around the world, a combination of fungicides is often more potent than using individual fungicides. However, the number of possible combinations increases exponentially with the increase of the number of fungicides combined and their concentrations. It is thus extremely challenging to identify effective fungicide combinations by trial and error from all possible combinations. In this paper, a systematic approach based on a support vector machine, a machine learning algorithm, is proposed to searching for the optimal combinations using only a limited number of measurements. The constructed model also incorporates information related to the inhibition rate (IR) and the cost of each composing fungicide into the optimization process. With this method, we show that only around 130 measurements on a coarse grid of concentrations out of thousands of possible experiments are sufficient to reconstruct the response model and to obtain the optimal fungicide combinations. Experimental results demonstrate that the optimized combinations can achieve an IR greater than 90%, while the required concentrations and the cost of individual fungicides are dramatically reduced. We anticipate that this method should be equally effective in the search for optimal combinations of multiple compounds in other diseases.

Spatially defined microsatellite analysis reveals extensive genetic mosaicism and clonal complexity in intestinal metaplastic glands.

Intestinal metaplasia (IM) has been recognized as the first irreversible precancerous stage of intestinal‐type gastric cancer at which genetic instabilities, such as microsatellite (MS) instability and loss of heterozygosity, can already be detected. However, the extent and clonal relationship of these genetic lesions in the precancerous tissues are not fully appreciated. In this work, we have used well established MS markers to analyze the relatedness of spatially separated individual metaplastic glands as well as subsegments within single glands from the same patients. We found that individual IM glands frequently show different marker lengths even for closely apposed IM glands, suggesting that these tissues have already gained the ability to independently evolve their genome regardless of whether or not they share a common origin. Furthermore, within individual IM glands, there is also significant intra‐gland diversity in the MS markers. Since most of these cells are not dividing and only have a limited lifespan, this result indicates that in each IM gland, a single dominant clone is rare and new clones are constantly created by either progenitor cells or stem cells. This greatly enhanced ability to create de novo genetic alterations may underlie the importance of this stage in the eventual progression toward cancer. Given the widely observed phenotype switch in the early stages of many solid tumors, whether this associated genetic stability is also an intrinsic property of metaplastic transformation should be extensively characterized to further our understanding of cancer initiation.

Design and Synthesis of New Acid Cleavable Linkers for DNA Sequencing by Synthesis

A new kind of acid sensitive tetrahydrofuranyl (THF) linker was synthesized and then reacted with 5-(6)-carboxytetramethylrhodaminesuccinimidyl ester (5(6)-TAMRA, SE), followed by di(N-succinimidyl) carbonate (DSC) and modified 2′-deoxyuridine triphosphate (dUTP); the final product, as a reversible terminator for DNA sequencing by synthesis (DNA SBS), was given obtained and confirmed by 1H-NMR, 31P-NMR, and HRMS with purity of up to 99%. The synthesized dye-labeled terminator incorporated into DNA strand successfully, and the fluorophore was cleaved completely under acidic conditions. The preliminary results encourage us to explore more acid-sensitive linkers for DNA SBS to increase the cleavage efficiency under weakly acidic conditions.

Enhancing the effectiveness of fungicides by optimizing their combinations

In controlling biological diseases, it is often more potent to use a combination of agents than using individual ones. However, the number of possible combinations increases exponentially with the number of agents and their concentrations. It is prohibitive to search for effective agent combinations by trial and error as biological systems are complex and their responses to agents are often a slow process. This motivates to build a suitable model to describe the biological systems and help reduce the number of experiments. In this paper, we consider the use of fungicides to inhibit Bipolaris maydis and construct models that describe the responses to fungicide combinations. Three data-driven modeling methods, the polynomial regression, the artificial neural network and the support vector regression, are compared based on the experimental data of the inhibition rates of the southern corn leaf blight with different fungicide combinations. The analysis of the results demonstrates that the support vector regression is best suited to the construction of the response model in terms of achieving better prediction with fewer experiments.