ICLAMP: a novel technique to explore adenosine deamination via inosine chemical labeling and affinity molecular purification.

Recent developments in sequencing and bioinformatics have advanced our understanding of adenosine-to-inosine (A-to-I) RNA editing. Surprisingly, recent analyses have revealed the capability of adenosine deaminase acting on RNA (ADAR) to edit DNA:RNA hybrid strands. However, edited inosines in DNA remain largely unexplored. A precise biochemical method could help uncover these potentially rare DNA editing sites. We explore maleimide as a scaffold for inosine labeling. With fluorophore-conjugated maleimide, we were able to label inosine in RNA or DNA. Moreover, with biotin-conjugated maleimide, we purified RNA and DNA containing inosine. Our novel technique of inosine chemical labeling and affinity molecular purification offers substantial advantages and provides a versatile platform for further discovery of A-to-I editing sites in RNA and DNA.

Multimodal analyses of a non-human primate model harboring mutant amyloid precursor protein transgenes driven by the human EF1α promoter.

Alzheimer's disease (AD) is the leading cause of dementia which afflicts tens of millions of people worldwide. Despite many scientific progresses to dissect the AD's molecular basis from studies on various mouse models, it has been suffered from evolutionary species differences. Here, we report generation of a non-human primate (NHP), common marmoset model ubiquitously expressing Amyloid-beta precursor protein (APP) transgenes with the Swedish (KM670/671NL) and Indiana (V717F) mutations. The transgene integration of generated two transgenic marmosets (TG1&TG2) was thoroughly investigated by genomic PCR, whole-genome sequencing, and fluorescence in situ hybridization. By reprogramming, we confirmed the validity of transgene expression in induced neurons in vitro. Moreover, we discovered structural changes in specific brain regions of transgenic marmosets by magnetic resonance imaging analysis, including in the entorhinal cortex and hippocampus. In immunohistochemistry, we detected increased Aß plaque-like structures in TG1 brain at 7 years old, although evident neuronal loss or glial inflammation was not observed. Thus, this study summarizes our attempt to establish an NHP AD model. Although the transgenesis approach alone seemed not sufficient to fully recapitulate AD in NHPs, it may be beneficial for drug development and further disease modeling by combination with other genetically engineered models and disease-inducing approaches.

A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations.

Although understanding their chemical composition is vital for accurately predicting the bioactivity of multicomponent drugs, nutraceuticals, and foods, no analytical approach exists to easily predict the bioactivity of multicomponent systems from complex behaviors of multiple coexisting factors. We herein represent a metabolic profiling (MP) strategy for evaluating bioactivity in systems containing various small molecules. Composition profiles of diverse bioactive herbal samples from 21 green tea extract (GTE) panels were obtained by a high-throughput, non-targeted analytical procedure. This employed the matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) technique, using 1,5-diaminonaphthalene (1,5-DAN) as the optical matrix for detecting GTE-derived components. Multivariate statistical analyses revealed differences among the GTEs in their antioxidant activity, oxygen radical absorbance capacity (ORAC). A reliable bioactivity-prediction model was constructed to predict the ORAC of diverse GTEs from their compositional balance. This chemometric procedure allowed the evaluation of GTE bioactivity by multicomponent rather than single-component information. The bioactivity could be easily evaluated by calculating the summed abundance of a few selected components that contributed most to constructing the prediction model. 1,5-DAN-MALDI-MS-MP, using diverse bioactive sample panels, represents a promising strategy for screening bioactivity-predictive multicomponent factors and selecting effective bioactivity-predictive chemical combinations for crude multicomponent systems.