scholarly journals In silico identification of anti-aging pharmaceutics from community knowledge

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 676-676
Author(s):  
Samuel Beck ◽  
Jun-Yeong Lee ◽  
Jarod Rollins

Abstract In this era of Big Data, the volume of biological data is growing exponentially. Systematic profiling and analysis of these data will provide a new insight into biology and human health. Among diverse types of biological data, gene expression data closely mirror both the static phenotypes and the dynamic changes in biological systems. Drug-to-drug or drug-to-disease comparison of gene expression signature allows repurposing/repositioning of existing pharmaceutics to treat additional diseases that, in turn, provides a rapid and cost-effective approach for drug discovery. Thanks to technological advances, gene expression profiling by mRNA-seq became a routine tool to address all aspects of the problem in modern biological research. Here, we present how drug repositioning using published mRNA-seq data can provide unbiased and applicable pharmaco-chemical intervention strategies to human diseases and aging. In specifics, we profiled over a half-million gene expression profiling data generated from various contexts, and using this, we screened conditions that can suppress age-associated gene expression changes. As a result, our analysis identified various previously validated aging intervention strategies as positive hits. Furthermore, our analysis also predicted a novel group of chemicals that has not been studied from an aging context, and this indeed significantly extended the life span in model animals. Taken together, our data demonstrate that our community knowledge-guided in silico drug-discovery pipeline provides a useful and effective tool to identify the novel aging intervention strategy.

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22041-e22041
Author(s):  
C. Lih ◽  
Y. Li ◽  
L. Trinh ◽  
S. Chien ◽  
X. Wu ◽  
...  

e22041 Background: Microarrays have been used to monitor global genes expression and have aided the identification of novel biomarkers for patients stratification and drug response prediction . To date there has been limited application of microarray- based gene expression analysis to formalin fixed paraffin embedded tissues (FFPET). FFPE tissues are the most commonly available clinical samples with documented clinical information for retrospective clinical analysis. However, FFPET RNA has proven to be an obstacle for microarray analysis because of low yield and compromised RNA integrity. Methods: Using a novel RNA amplification method, Single Primer Isothermal Amplification (SPIA, NuGEN Inc, San Carlos, CA), we amplified FFPET RNA, hybridized amplified, and labeled cDNA onto Affymetrix HG U133plus2 GeneChips. Results: We found that SPIA amplification successfully overcomes the problems of poor quality of FFPET RNA, and produced informative biological data. Comparing the gene expression data from 5 different types of FFPET archival cancer samples (breast, lung, ovarian, colon, and melanoma), we demonstrated that gene expression signatures clearly distinguish the tissue of origin. Further, from an analysis of 91 FFPET samples comprised of ER+, HER2+, triple negative breast cancer patients, and normal breast tissue, we have identified a 103 gene signature that distinguishes the intrinsic sub-types of breast cancer. Finally, the accuracy of gene expression measured by microarray was verified by real time PCR quantitation of the ERBB2 gene, resulting in a significant correlation (R = 0.88). Conclusions: We have demonstrated the feasibility of global gene expression profiling using RNA extracted from FFPET and have shown that a gene expression signature can stratify patient samples into different subtypes of disease. This study paves the way to identify novel molecular biomarkers for disease stratification and therapy response from archival FFPET samples, leading to the goals of personalized medicine. No significant financial relationships to disclose.


2020 ◽  
Author(s):  
Omer An ◽  
Yangyang Song ◽  
Xinyu Ke ◽  
Jimmy Bok-Yan So ◽  
Raghav Sundar ◽  
...  

AbstractBackground & AimsGastric cancer (GC) cases are often diagnosed at an advanced stage with poor prognosis. Platinum-based chemotherapy has been internationally accepted as first-line therapy for inoperable or metastatic GC. To achieve greater benefits, it is critical to select patients who are eligible for the treatment. Albeit gene expression profiling has been widely used as a genomic classifier to identify molecular subtypes of GC and stratify patients for different chemotherapy regimens, the prediction accuracy remains to be improved. More recently, adenosine-to-inosine (A-to-I) RNA editing has emerged as a new player contributing to GC development and progression, offering potential clinical utility for diagnosis and treatment.MethodsWe conducted a transcriptome-wide RNA editing analysis of a cohort of 104 patients with advanced GC and identified an RNA editing (GCRE) signature to guide GC chemotherapy, using a systematic computational approach followed by both in vitro validations and in silico validations in TCGA.ResultsWe found that RNA editing events alone stand as a prognostic and predictive biomarker in advanced GC. We developed a GCRE score based on the GCRE signature consisting of 50 editing sites associated with 29 genes and achieved a high accuracy (84%) of predicting patient response to chemotherapy. Of note, patients demonstrating higher editing levels of this panel of sites present a better overall response. Consistently, GC cell lines with higher editing levels showed higher chemosensitivity. Applying the GCRE score on TCGA dataset confirmed that responders had significantly higher levels of editing in advanced GC.ConclusionsOverall, the GCRE signature reliably stratifies patients with advanced GC and predicts response from chemotherapy.SignificanceDespite the increasing documentation of RNA editing and its functional regulation, the translational potential of RNA editome in cancer remains largely under-investigated. This study reports for the first time an RNA editing signature in advanced GC, to reliably stratify patients with advanced disease to predict response from chemotherapy independently of gene expression profiling and other genomic and epigenetic changes. For this purpose, a bioinformatics approach was used to develop a GCRE score based on a panel of 50 editing sites from 29 unique genes (GCRE signature), followed by an experimental evaluation of their clinical utility as predictive biomarker in GC cell lines and in silico validation in using RNA sequencing (RNA-Seq) datasets from TCGA. The applied methodology provides a robust means of an RNA editing signature to be investigated in patients with advanced GC. Overall, this study provides insights into the translation of RNA editing process into predictive clinical applications to direct chemotherapy against GC.


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