Current Clinical and Pharmaceutical Applications of Microarrays: From Disease Biomarkers Discovery to Automated Diagnostics

Interactions of Catalytic Enzymes with n-Type Polymers for High-Performance Metabolite Sensors

The tight regulation of the glucose concentration in the body is crucial for balanced physiological function. We developed an electrochemical transistor comprising an n-type conjugated polymer film in contact with a catalytic enzyme for sensitive and selective glucose detection in bodily fluids. Despite the promise of these sensors, the property of the polymer that led to such high performance has remained unknown, with charge transport being the only characteristic under focus. Here, we studied the impact of the polymer chemical structure on film surface properties and enzyme adsorption behavior using a combination of physiochemical characterization methods and correlated our findings with the resulting sensor performance. We developed five n-type polymers bearing the same backbone with side chains differing in polarity and charge. We found that the nature of the side chains modulated the film surface properties, dictating the extent of interactions between the enzyme and the polymer film. Quartz crystal microbalance with dissipation monitoring studies showed that hydrophobic surfaces retained more enzymes in a densely packed arrangement, while hydrophilic surfaces captured fewer enzymes in a flattened conformation. X-ray photoelectron spectroscopy analysis of the surfaces revealed strong interactions of the enzyme with the glycolated side chains of the polymers, which improved for linear side chains compared to those for branched ones. We probed the alterations in the enzyme structure upon adsorption using circular dichroism, which suggested protein denaturation on hydrophobic surfaces. Our study concludes that a negatively charged, smooth, and hydrophilic film surface provides the best environment for enzyme adsorption with desired mass and conformation, maximizing the sensor performance. This knowledge will guide synthetic work aiming to establish close interactions between proteins and electronic materials, which is crucial for developing high-performance enzymatic metabolite biosensors and biocatalytic charge-conversion devices.

MicroRNAs as clinical tools for diagnosis, prognosis, and therapy in prostate cancer

Prostate cancer (PCa) is one of the most commonly diagnosed cancers among men worldwide. Despite the presence of accumulated clinical strategies for PCa management, limited prognostic/sensitive biomarkers are available to follow up on disease occurrence and progression. MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression through post-transcriptional regulation of their complementary target messenger RNA (mRNA). MiRNAs modulate fundamental biological processes and play crucial roles in the pathology of various diseases, including PCa. Multiple evidence proved an aberrant miRNA expression profile in PCa, which is actively involved in the carcinogenic process. The robust and pleiotropic impact of miRNAs on PCa suggests them as potential candidates to help more understand the molecular landscape of the disease, which is likely to provide tools for early diagnosis and prognosis as well as additional therapeutic strategies to manage prostate tumors. Here, we emphasize the most consistently reported dysregulated miRNAs and highlight the contribution of their altered downstream targets with PCa hallmarks. Also, we report the potential effectiveness of using miRNAs as diagnostic/prognostic biomarkers in PCa and the high-throughput profiling technologies that are being used in their detection. Another key aspect to be discussed in this review is the promising implication of miRNAs molecules as therapeutic tools and targets for fighting PCa.

Formation of charge-nanopatterned templates with flexible geometry via layer by layer deposition of polyelectrolytes for directed self-assembly of gold nanoparticles

Nanostructure formation via self-assembly processes offers a fast and cost-effective approach to generate surface patterns on large lateral scale. In particular, if the high precision of lithographic techniques is not required, a situation typical of many biotechnological and biomedical applications, it may be considered as the method of choice as it does not require any sophisticated instrumentation. However, in many cases the variety and complexity of the surface structures accessible with a single self-assembly based technique is limited. Here, we report on a new approach which combines two different self-assembly strategies, colloidal lithography and layer-by-layer deposition of polyelectrolytes, in order to significantly expand the spectrum of accessible patterns. In particular, flat and donut-like charge-patterned templates have been generated, which facilitate subsequent deposition of gold nanoparticles in dot, grid, ring, out-of-ring and circular patch structures. Potential applications are e.g. in the fields of biofunctional interfaces with well-defined lateral dimensions, optical devices with tuned properties, and controlled three-dimensional material growth.

[A prototype of oligonucleotide microarray for detection of pathogens relating to arena- and Filoviridae families]

A prototype of oligonucleotide microarray for detection of Lassa, Junin, Machupo, Guanarito viruses (Arenaviridae family), Ebola and Marburg viruses (Filoviridae family) was presented. An original approach founded on virus proteins (nucleocapsid protein for Junin, Guanarito, Machupo viruses and RNA-dependent RNA-polymerase for Lassa, Ebola and Marburg viruses) amino acid sequences analysis with subsequent transform of revealed unique peptides into due sets of oligonucleotides was used to design probes for hybridization and primers.

A unified computational model for revealing and predicting subtle subtypes of cancers

BACKGROUND

Gene expression profiling technologies have gradually become a community standard tool for clinical applications. For example, gene expression data has been analyzed to reveal novel disease subtypes (class discovery) and assign particular samples to well-defined classes (class prediction). In the past decade, many effective methods have been proposed for individual applications. However, there is still a pressing need for a unified framework that can reveal the complicated relationships between samples.

RESULTS

We propose a novel convex optimization model to perform class discovery and class prediction in a unified framework. An efficient algorithm is designed and software named OTCC (Optimization Tool for Clustering and Classification) is developed. Comparison in a simulated dataset shows that our method outperforms the existing methods. We then applied OTCC to acute leukemia and breast cancer datasets. The results demonstrate that our method not only can reveal the subtle structures underlying those cancer gene expression data but also can accurately predict the class labels of unknown cancer samples. Therefore, our method holds the promise to identify novel cancer subtypes and improve diagnosis.

CONCLUSIONS

We propose a unified computational framework for class discovery and class prediction to facilitate the discovery and prediction of subtle subtypes of cancers. Our method can be generally applied to multiple types of measurements, e.g., gene expression profiling, proteomic measuring, and recent next-generation sequencing, since it only requires the similarities among samples as input.