Xianting Ding

Electrochemical detection of lung cancer specific microRNAs using 3D DNA origami nanostructures

Recent reports have indicated that aberrant expression of microRNAs is highly correlated with occurrence of lung cancer. Therefore, highly sensitive detection of lung cancer specific microRNAs provides an attractive approach in lung cancer early diagnostics. Herein, we designed 3D DNA origami structure that enables electrochemical detection of lung cancer related microRNAs. The 3D DNA origami structure is constituted of a ferrocene-tagged DNA of stem-loop structure combined with a thiolated tetrahedron DNA nanostructure at the bottom. The top portion hybridized with the lung cancer correlated microRNA, while the bottom portion was self-assembled on gold disk electrode surface, which was modified with gold nanoparticles (Au NPs) and blocked with mercaptoethanol (MCH). The preparation process and the performance of the proposed electrochemical genosensor were characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), cyclic voltammetry (CV) and differential pulse voltammetry (DPV). Under the optimal conditions, the developed genosensor had a detection limit of 10 pM and a good linearity with microRNA concentration ranging from 100 pM to 1 µM, which showed a great potential in highly sensitive clinical cancer diagnosis application.

Optimization of drug combinations using Feedback System Control

We describe a protocol for the discovery of synergistic drug combinations for the treatment of disease. Synergistic drug combinations lead to the use of drugs at lower doses, which reduces side effects and can potentially lead to reduced drug resistance, while being clinically more effective than the individual drugs. To cope with the extremely large search space for these combinations, we developed an efficient combinatorial drug screening method called the Feedback System Control (FSC) technique. Starting with a broad selection of drugs, the method follows an iterative approach of experimental testing in a relevant bioassay and analysis of the results by FSC. First, the protocol uses a cell viability assay to generate broad dose-response curves to assess the efficacy of individual compounds. These curves are then used to guide the dosage input of each drug to be tested in combination. Data from applied drug combinations are input into the differential evolution (DE) algorithm, which predicts new combinations to be tested in vitro. This process identifies optimal drug-dose combinations, while saving orders of magnitude in experimental effort. The complete optimization process is estimated to take ∼4 weeks. FSC does not require insight into the disease mechanism, and it has therefore been applied to find combination therapies for many different pathologies, including cancer and infectious diseases, and it has also been used in organ transplantation.

A streamlined search technology for identification of synergistic drug combinations

A major key to improvement of cancer therapy is the combination of drugs. Mixing drugs that already exist on the market may offer an attractive alternative. Here we report on a new model-based streamlined feedback system control (s-FSC) method, based on a design of experiment approach, for rapidly finding optimal drug mixtures with minimal experimental effort. We tested combinations in an in vitro assay for the viability of a renal cell adenocarcinoma (RCC) cell line, 786-O. An iterative cycle of in vitro testing and s-FSC analysis was repeated a few times until an optimal low dose combination was reached. Starting with ten drugs that target parallel pathways known to play a role in the development and progression of RCC, we identified the best overall drug combination, being a mixture of four drugs (axitinib, erlotinib, dasatinib and AZD4547) at low doses, inhibiting 90% of cell viability. The removal of AZD4547 from the optimized drug combination resulted in 80% of cell viability inhibition, while still maintaining the synergistic interaction. These optimized drug combinations were significantly more potent than monotherapies of all individual drugs (p < 0.001, CI < 0.3).

An electrochemical biosensor based on DNA tetrahedron/graphene composite film for highly sensitive detection of NADH.

Dihydronicotinamide adenine dinucleotide (NADH) is a major biomarker correlated with lethal diseases such as cancers and bacterial infection. Herein, we report a graphene-DNA tetrahedron-gold nanoparticle modified gold disk electrode for highly sensitive NADH detection. By assembling the DNA tetrahedron/graphene composite film on the gold disk electrode surface which prior harnessed electrochemical deposition of gold nanoparticles to enhance the effective surface area, the oxidation potential of NADH was substantially decreased to 0.28V (vs. Ag/AgCl) and surface fouling effects were successfully eliminated. Furthermore, the lower detection limit of NADH by the presented platform was reduced down to 1fM, with an upper limit of 10pM. Both the regeneration and selectivity of composite film-modified electrode are investigated and proved to be robust. The novel sensor developed here could serve as a highly sensitive probe for NADH detection, which would further benefit the field of NADH related disease diagnostics.

Optimizing Combinations of Flavonoids Deriving from Astragali Radix in Activating the Regulatory Element of Erythropoietin by a Feedback System Control Scheme

Identifying potent drug combination from a herbal mixture is usually quite challenging, due to a large number of possible trials. Using an engineering approach of the feedback system control (FSC) scheme, we identified the potential best combinations of four flavonoids, including formononetin, ononin, calycosin, and calycosin-7-O-β-D-glucoside deriving from Astragali Radix (AR; Huangqi), which provided the best biological action at minimal doses. Out of more than one thousand possible combinations, only tens of trials were required to optimize the flavonoid combinations that stimulated a maximal transcriptional activity of hypoxia response element (HRE), a critical regulator for erythropoietin (EPO) transcription, in cultured human embryonic kidney fibroblast (HEK293T). By using FSC scheme, 90% of the work and time can be saved, and the optimized flavonoid combinations increased the HRE mediated transcriptional activity by ~3-fold as compared with individual flavonoid, while the amount of flavonoids was reduced by ~10-fold. Our study suggests that the optimized combination of flavonoids may have strong effect in activating the regulatory element of erythropoietin at very low dosage, which may be used as new source of natural hematopoietic agent. The present work also indicates that the FSC scheme is able to serve as an efficient and model-free approach to optimize the drug combination of different ingredients within a herbal decoction.

Protective effects of madecassoside against Doxorubicin induced nephrotoxicity in vivo and in vitro

Madecassoside (MA), a triterpenoid saponin isolated from C. asitica, exerts various pharmacological activity including antioxidative and antinflammatory. Doxorubicin (DOX), a common chemotherapeutic drug, has been reported to induce numerous toxic side effects including renal-toxicity. We hypothesized that MA administration may decrease renal-toxicity caused by DOX. In this study, we investigated this hypothesis by introducing MA and DOX into the culture of Human Proximal Tubule Cells HK-2 and mice model. Our in vivo study demonstrated that MA (12 mg/kg), treatment for two weeks attenuated DOX-induced renal injury via protecting renal function, recovering antioxidant enzyme activity, inhibiting Bax, p-ERK1/2, NF-κB p65, iNOS expression and increasing Bcl-2 expression. Similar findings were obtained in our in vitro studies with treatment of DOX and/or MA. Further studies with application of iNOS inhibitor and ERK1/2 kinase inhibitor indicated that the inhibitory effects of MA on DOX-induced apoptosis and inflammation might be mediated by the suppression of the activation of cleaved caspase-3, ERK1/2 pathways, NF-κB p65 and NO production. These results suggest that MA is a promising protective agent for DOX-induced renal toxicity and can be a potential candidate to protect against renal toxicity in DOX-treated cancer patients.

Output-driven feedback system control platform optimizes combinatorial therapy of tuberculosis using a macrophage cell culture model

Significance Improved regimens for treatment of tuberculosis are needed to shorten the duration of treatment and combat the emergence of drug resistance. Selection of optimized regimens requires assessment of numerous combinations of existing drugs at multiple dose levels. This requirement presents a challenge because of the exponentially large number of combinations—NM for N doses of M drugs. We show here using a high-throughput macrophage model of Mycobacterium tuberculosis infection that a feedback system control technique can determine optimal drug treatment regimens by testing a relatively small number of drug–dose combinations. In an independent assay measuring intramacrophage killing of M. tuberculosis, the optimized regimens are superior to the current standard regimen. Tuberculosis (TB) remains a major global public health problem, and improved treatments are needed to shorten duration of therapy, decrease disease burden, improve compliance, and combat emergence of drug resistance. Ideally, the most effective regimen would be identified by a systematic and comprehensive combinatorial search of large numbers of TB drugs. However, optimization of regimens by standard methods is challenging, especially as the number of drugs increases, because of the extremely large number of drug–dose combinations requiring testing. Herein, we used an optimization platform, feedback system control (FSC) methodology, to identify improved drug–dose combinations for TB treatment using a fluorescence-based human macrophage cell culture model of TB, in which macrophages are infected with isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible green fluorescent protein (GFP)-expressing Mycobacterium tuberculosis (Mtb). On the basis of only a single screening test and three iterations, we identified highly efficacious three- and four-drug combinations. To verify the efficacy of these combinations, we further evaluated them using a methodologically independent assay for intramacrophage killing of Mtb; the optimized combinations showed greater efficacy than the current standard TB drug regimen. Surprisingly, all top three- and four-drug optimized regimens included the third-line drug clofazimine, and none included the first-line drugs isoniazid and rifampin, which had insignificant or antagonistic impacts on efficacy. Because top regimens also did not include a fluoroquinolone or aminoglycoside, they are potentially of use for treating many cases of multidrug- and extensively drug-resistant TB. Our study shows the power of an FSC platform to identify promising previously unidentified drug–dose combinations for treatment of TB.

A Review on Microfluidic Paper-Based Analytical Devices for Glucose Detection

Glucose, as an essential substance directly involved in metabolic processes, is closely related to the occurrence of various diseases such as glucose metabolism disorders and islet cell carcinoma. Therefore, it is crucial to develop sensitive, accurate, rapid, and cost effective methods for frequent and convenient detections of glucose. Microfluidic Paper-based Analytical Devices (μPADs) not only satisfying the above requirements but also occupying the advantages of portability and minimal sample consumption, have exhibited great potential in the field of glucose detection. This article reviews and summarizes the most recent improvements in glucose detection in two aspects of colorimetric and electrochemical μPADs. The progressive techniques for fabricating channels on μPADs are also emphasized in this article. With the growth of diabetes and other glucose indication diseases in the underdeveloped and developing countries, low-cost and reliably commercial μPADs for glucose detection will be in unprecedentedly demand.

Discovery of a low order drug-cell response surface for applications in personalized medicine

The cell is a complex system involving numerous components, which may often interact in a non-linear dynamic manner. Diseases at the cellular level are thus likely to involve multiple cellular constituents and pathways. As some drugs, or drug combinations, may act synergistically on these multiple pathways, they might be more effective than the respective single target agents. Optimizing a drug mixture for a given disease in a particular patient is particularly challenging due to both the difficulty in the selection of the drug mixture components to start out with, and the all-important doses of these drugs to be applied. For n concentrations of m drugs, in principle, n(m) combinations will have to be tested. As this may lead to a costly and time-consuming investigation for each individual patient, we have developed a Feedback System Control (FSC) technique which can rapidly select the optimal drug-dose combination from the often millions of possible combinations. By testing this FSC technique in a number of experimental systems representing different disease states, we found that the response of cells to multiple drugs is well described by a low order, rather smooth, drug-mixture-input/drug-effect-output multidimensional surface. The main consequences of this are that optimal drug combinations can be found in a surprisingly small number of tests, and that translation from in vitro to in vivo is simplified. This points to the possibility of personalized optimal drug mixtures in the near future. This unexpectedly simple input-output relationship may also lead to a simple solution for handling the issue of human diversity in cancer therapeutics.

Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time

The current drug regimens for treating tuberculosis are lengthy and onerous, and hence complicated by poor adherence leading to drug resistance and disease relapse. Previously, using an output-driven optimization platform and an in vitro macrophage model of Mycobacterium tuberculosis infection, we identified several experimental drug regimens among billions of possible drug-dose combinations that outperform the current standard regimen. Here we use this platform to optimize the in vivo drug doses of two of these regimens in a mouse model of pulmonary tuberculosis. The experimental regimens kill M. tuberculosis much more rapidly than the standard regimen and reduce treatment time to relapse-free cure by 75%. Thus, these regimens have the potential to provide a markedly shorter course of treatment for tuberculosis in humans. As these regimens omit isoniazid, rifampicin, fluoroquinolones and injectable aminoglycosides, they would be suitable for treating many cases of multidrug and extensively drug-resistant tuberculosis.