Drug Screening with Genetically Encoded Fluorescent Sensors: Today and Tomorrow

Development of nanobiosensor for therapeutic drug monitoring in personalized cancer treatment approach

Docetaxel is one of the most effective and safe chemotherapy drugs according to the World Health Organization, but its clinical use has been discontinued due to its various side effects. To reduce these side effects, the amount of docetaxel drug should be kept at the most effective level, it should be monitored in body fluids. Due to the limitations of traditional analytical methods used for this purpose, such as expensive and low sensitivity, labor-intensive and time-consuming complex preliminary preparation, efficient methods are required for the determination of the docetaxel level in the body. The increasing demand for the development of personalized therapy has recently spurred significant research into biosensors for the detection of drugs and other chemical compounds. In this study, an electrochemical-based portable nanobiosensor system was developed for the rapid, low-cost, and sensitive determination of docetaxel. In this context, mg-p(HEMA)-IMEO nanoparticles to be used as nanobiosensor bioactive layer was synthesized, characterized, and docetaxel determination conditions were optimized. According to the results obtained, the developed nanobiosensor system can detect docetaxel with a sensitivity of 2.22 mg/mL in a wide calibration range of 0.25-10 mg/mL, in only 15 min, in mixed media such as commercially available artificial blood serum and urine. determined. We concluded that the developed nanobiosensor system can be successfully used in routine drug monitoring as a low-cost biomedical device capable of direct, rapid, and specific drug determination within the scope of personalized treatment, providing point-of-care testing.

Tet-Regulated Expression and Optical Clearing for In Vivo Visualization of Genetically Encoded Chimeric dCas9/Fluorescent Protein Probes

The catalytically inactive mutant of Cas9 (dCas9) endonuclease has multiple biomedical applications, with the most useful being the activation/repression of transcription. dCas9 family members are also emerging as potential experimental tools for gene mapping at the level of individual live cells and intact tissue. We performed initial testing on a set of tools for Cas9-mediated visualization of nuclear compartments. We investigated doxycycline (Dox)-inducible (Tet-On) intracellular distribution of constructs encoding dCas9 orthologs from St. thermophilus (St) and N. meningitides (Nm) fused with EGFP and mCherry fluorescent proteins (FP) in human A549 cells. We also studied time-dependent expression of these chimeric fluorescent constructs (dCas9-FP) after Tet-On induction in live cells and compared it with the time course of dCas9-FP expression in experimental dCas9-FP-expressing tumor xenografts using a combination of fluorescence imaging and in vivo contrast-assisted magnetic resonance imaging for assessing the extent of tumor perfusion. In vivo Dox-induction of mCherry-chimera expression occurred in tumor xenografts as early as 24 h post-induction and was visualized by using optical clearing (OC) of the skin. OC via topical application of gadobutrol enabled high-contrast imaging of FP expression in tumor xenografts due to a 1.1-1.2-fold increase in FI in both the red and green channels.

Seeing Neurodegeneration in a New Light Using Genetically Encoded Fluorescent Biosensors and iPSCs

Neurodegenerative diseases present a progressive loss of neuronal structure and function, leading to cell death and irrecoverable brain atrophy. Most have disease-modifying therapies, in part because the mechanisms of neurodegeneration are yet to be defined, preventing the development of targeted therapies. To overcome this, there is a need for tools that enable a quantitative assessment of how cellular mechanisms and diverse environmental conditions contribute to disease. One such tool is genetically encodable fluorescent biosensors (GEFBs), engineered constructs encoding proteins with novel functions capable of sensing spatiotemporal changes in specific pathways, enzyme functions, or metabolite levels. GEFB technology therefore presents a plethora of unique sensing capabilities that, when coupled with induced pluripotent stem cells (iPSCs), present a powerful tool for exploring disease mechanisms and identifying novel therapeutics. In this review, we discuss different GEFBs relevant to neurodegenerative disease and how they can be used with iPSCs to illuminate unresolved questions about causes and risks for neurodegenerative disease.

Smart genetically-encoded biosensors for the chemical monitoring of living systems

Genetically-encoded biosensors provide the all-optical and non-invasive visualization of dynamic biochemical events within living systems, which has allowed the discovery of profound new insights. Twenty-five years of biosensor development has steadily improved their performance and has provided us with an ever increasing biosensor repertoire. In this feature article, we present recent advances made in biosensor development and provide a perspective on the future direction of the field.

Bioluminescent and Fluorescent Proteins: Molecular Mechanisms and Modern Applications

Light emission by living organisms in the visible spectrum range is called bioluminescence [...].

Unbiased prediction of off-target sites in genome-edited rice using SITE-Seq analysis on a web-based platform

Genome-editing using the CRISPR-Cas9 system has the potential to substantially accelerate crop breeding. Since off-target editing is one of problems, a reliable method for comprehensively detecting off-target sites is needed. A number of in silico methods based on homology to on-target sequence have been developed, however the prediction without false negative is still under discussion. In this study, we performed a SITE-Seq analysis to predict potential off-target sites. SITE-Seq analysis is a comprehensive method that can detect double-strand breaks in vitro. Furthermore, we developed a systematic method using SITE-Seq in combination with web-based Galaxy system (Galaxy for Cut Site Detection), which can perform reproducible analyses without command line operations. We conducted a SITE-Seq analysis of a rice genome targeted by OsFH15 gRNA-Cas9 as a model, and found 41 candidate off-target sites in the annotated regions. Detailed amplicon-sequencing revealed mutations at one off-target site in actual genome-edited rice. Since this off-target site has an uncommon protospacer adjacent motif, it is difficult to predict using in silico methods alone. Therefore, we propose a novel off-target assessment scheme for genome-edited crops that combines the prediction of off-target candidates by SITE-Seq and in silico programs and the validation of off-target sites by amplicon-sequencing.

Functional two-dimensional MXenes as cancer theranostic agents

Recently, MXenes, as a kind of two-dimensional (2D) layered materials with exceptional performance, have become the research hotspots owing to their unique structural, electronic, and chemical properties. They have potential applications in electrochemical storage, photocatalysis, and biosensors. Furthermore, they have certain characteristics such as large surface area, favorable biocompatibility, and ideal mechanical properties, which can expand their applications in biomedical fields, especially in cancer therapy. To date, several researchers have explored the applications of MXenes in tumor elimination, which exhibited other fantastic properties of those 2D MXenes, such as efficient in vivo photothermal ablation, low phototoxicity, high biocompatibility, etc. In this review, the structures, properties, modifications, and preparation methods are introduced respectively. More importantly, the multifunctional platforms for cancer therapy based on MXenes nanosheets (NSs) are reviewed in detail, including single-modality and combined-modality cancer therapy. Finally, the prospects and challenges of MXenes are prospected and discussed. STATEMENT OF SIGNIFICANCE: In this review, the structures, properties, modifications, and preparation methods of MXenes nanomaterials are introduced, respectively. In addition, the preparation conditions and morphological characterizations of some common MXenes for therapeutic platforms are also summarized. More importantly, the practical applications of MXenes-based nanosheets are reviewed in detail, including drug delivery, biosensing, bioimaging, and multifunctional tumor therapy platforms. Finally, the future prospects and challenges of MXenes are prospected and discussed.

Neuronal Activity Reporters as Drug Screening Platforms

Understanding how neuronal activity changes and detecting such changes in both normal and disease conditions is of fundamental importance to the field of neuroscience. Neuronal activity plays important roles in the formation and function of both synapses and circuits, and dysregulation of these processes has been linked to a number of debilitating diseases such as autism, schizophrenia, and epilepsy. Despite advances in our understanding of synapse biology and in how it is altered in disease, the development of therapeutics for these diseases has not advanced apace. Many neuronal activity assays have been developed over the years using a variety of platforms and approaches, but major limitations persist. Current assays, such as fluorescence indicators are not designed to monitor neuronal activity over a long time, they are typically low-throughput or lack sensitivity. These are major barriers to the development of new therapies, as drug screening needs to be both high-throughput to screen through libraries of compounds, and longitudinal to detect any effects that may emerge after continued application of the drug. This review will cover existing assays for measuring neuronal activity and highlight a live-cell assay recently developed. This assay can be performed with easily accessible lab equipment, is both scalable and longitudinal, and can be combined with most other established methods.

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

MOTIVATION

Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations.

RESULTS

In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug-Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation.

AVAILABILITY AND IMPLEMENTATION

Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master.

Synthetic gene circuits as tools for drug discovery

Within mammalian systems, there exists enormous opportunity to use synthetic gene circuits to enhance phenotype-based drug discovery, to map the molecular origins of disease, and to validate therapeutics in complex cellular systems. While drug discovery has relied on marker staining and high-content imaging in cell-based assays, synthetic gene circuits expand the potential for precision and speed. Here we present a vision of how circuits can improve the speed and accuracy of drug discovery by enhancing the efficiency of hit triage, capturing disease-relevant dynamics in cell-based assays, and simplifying validation and readouts from organoids and microphysiological systems (MPS). By tracking events and cellular states across multiple length and time scales, circuits will transform how we decipher the causal link between molecular events and phenotypes to improve the selectivity and sensitivity of cell-based assays.