METHODS: Three-week-old weanling NRs were fed either a high-carbohydrate diet (%En from carbohydrate/fat/protein = 70:10:20, 16.7 kJ/g; n = 8) or the same high-carbohydrate diet supplemented with PFJ (415 ml of 13,000-ppm gallic acid equivalent (GAE) for a final concentration of 5.4 g GAE per kg diet or 2.7 g per 2000 kcal; n = 8). Livers were obtained from these NRs for microarray gene expression analysis using Illumina MouseRef-8 Version 2 Expression BeadChips. Microarray data were analysed along with the physiological parameters of diabetes.
RESULTS: Compared to the control group, 71 genes were up-regulated while 108 were down-regulated in the group supplemented with PFJ. Among hepatic genes up-regulated were apolipoproteins related to high-density lipoproteins (HDL) and genes involved in hepatic detoxification, while those down-regulated were related to insulin signalling and fibrosis.
CONCLUSION: The results obtained suggest that the anti-diabetic effects of PFJ may be due to mechanisms other than an increase in insulin secretion.
OBJECTIVES: To develop a novel in vitro skin glycation model as a screening tool for topical formulations with antiglycation properties and to further characterize, at the molecular level, the glycation stress-driven skin ageing mechanism.
METHODS: The glycation model was developed using human reconstituted full-thickness skin; the presence of N(ε) -(carboxymethyl) lysine (CML) was used as evidence of the degree of glycation. Topical application of emulsion containing a well-known antiglycation compound (aminoguanidine) was used to verify the sensitivity and robustness of the model. Cytokine immunoassay, quantitative real-time polymerase chain reaction and histological analysis were further implemented to characterize the molecular mechanisms of skin ageing in the skin glycation model.
RESULTS: Transcriptomic and cytokine profiling analyses in the skin glycation model demonstrated multiple biological changes, including extracellular matrix catabolism, skin barrier function impairment, oxidative stress and subsequently the inflammatory response. Darkness and yellowness of skin tone observed in the in vitro skin glycation model correlated well with the degree of glycation stress.
CONCLUSIONS: The newly developed skin glycation model in this study has provided a new technological dimension in screening antiglycation properties of topical pharmaceutical or cosmeceutical formulations. This study concomitantly provides insights into skin ageing mechanisms driven by glycation stress, which could be useful in formulating skin antiageing therapy in future studies.
RESULTS: We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC50) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC50 of 0.8-1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control.
CONCLUSIONS: DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.
RESULTS: We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).
CONCLUSIONS: Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.