China
Africa
Explore chapters and articles related to this topic
The Precision Medicine Approach in Oncology
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
Although microarrays are widely used to monitor drug responses at the genetic level, they have limitations based on their dependence on predesigned oligonucleotide probes with detection based on hybridization. A new technique that combines Cap Analysis of Gene Expression (CAGE) with third-generation, single-molecule sequencing has been developed to overcome the limitations of microarray-based technologies. CAGE, a method developed by Riken and co-workers, maps the start sites of transcription and the associated promoters, as well as quantifying the transcriptome. This technique can analyze cellular responses to drugs more precisely, as it allows the monitoring and quantification of the activity of individual gene promoters.
Disease Prediction and Drug Development
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
As the amount of sequenced genomes have increased, it has become important to tag them to identify in the newly sequenced genomes and determine the transcription start-sites on a genome-wide scale. CAGE is a mRNA-based tagging scheme that studies and catalogs a short subsequence of mRNA at 5ʹ end. It also helps in establishing the transcription for different genes in the regulatory network. The typical length of CAP tags is around 20–30 nucleotides.
Omics Technology: Novel Approach for Screening of Plant-Based Traditional Medicines
Published in Megh R. Goyal, Hafiz Ansar Rasul Suleria, Ademola Olabode Ayeleso, T. Jesse Joel, Sujogya Kumar Panda, The Therapeutic Properties of Medicinal Plants, 2019
Rojita Mishra, Satpal Singh Bisht, Mahendra Rana
In 1995, Serial Analysis of Gene Expression (SAGE) was developed, which is based on the sequencing. The basic working principle is an analysis of concatenated random transcripts by Sanger‘s method. Briefly, in SAGE analysis, cDNA is produced from mRNA then is digested into 11bp tag fragments with the help of restriction endonucleases. The cDNA tags are concatenated head to tail into about 500 bp long strands and sequenced using Sanger sequencing. The sequences are deconvoluted into original 11bp tags. Two approaches are there: (1) If a reference genome is available then tags can be aligned to identify the corresponding genes; (2) In other approaches where a reference genome is not available, and tags are used as diagnostic markers if the tag is expressed differentially in the disease state. The Cap Analysis of Gene Expression (CAGE) is a slight variation of SAGE, where sequence tags are from the 5‘-end of the mRNA transcript. If the reference genome is available, then transcriptional starting site of the gene is identified in other words, promoter analysis, and cloning of full-length cDNAs become possible [4].
Colorectal cancer screening and diagnosis: omics-based technologies for development of a non-invasive blood-based method
Published in Expert Review of Anticancer Therapy, 2021
María Gallardo-Gómez, Loretta De Chiara, Paula Álvarez-Chaver, Joaquin Cubiella
Approximately 98% of the genes from our genome are non-coding genes (ncRNA), which have regulatory functions and have been grouped according to their length. MicroRNAs (miRNA) are the most studied small ncRNAs (18–25 nt) that act post-transcriptionally, typically targeting the 3´ untranslated region of mRNAs. The techniques used for their study have been mentioned in the Genomics and transcriptomics section. On the other hand, long ncRNAs (lncRNA), bearing more than 200 nt, have various functions related to gene regulation, including transcriptional activation or repression, acting as a scaffold for chromatin remodeling complexes, enhancer RNA, and regulation of RNA splicing [61]. RNA-seq is used for novel lncRNA discovery. CAGE (Cap analysis of gene expression) is another high-throughput NGS-based method that allows mapping and quantification of expression of 5´-capped RNAs [62]. Different approaches have also been used to analyze lncRNA interactions with DNA, protein, and RNA [63].
Generation and characterization of fruitless P1 promoter mutant in Drosophila melanogaster
Published in Journal of Neurogenetics, 2021
Megan C. Neville, Alexander Eastwood, Aaron M. Allen, Ammerins de Haan, Tetsuya Nojima, Stephen F. Goodwin
Our intension was to disrupt the core P1 promoter region in fruitless since the SDH regulates only transcripts from this promoter. We first examined the transcriptional start site (TSS) of the P1 promoter between closely related Drosophila species (Figure S1). We found the TSS sequence annotated in Flybase matches the Initiator (Inr) motif consensus associated with ‘sharp’ initiation (Bhardwaj, Semplicio, Erdogdu, Manke, & Akhtar, 2019), common in adult tissue-specific genes and terminally differentiated cell-specific genes (Haberle & Stark, 2018). Examination of male and female head Cap Analysis Gene Expression (CAGE) data, available through ModEncode (modENCODE CAGE), reinforced sharp initiation near this TSS in both sexes. Species comparisons identified a downstream promoter element (DPE; Figure S1) and a lack of an exact TATA-binding sequence upstream of the TSS. The fru P1 core promoter appears to be what is classified as a type-2 promoter by modENCODE, containing an Inr and DPE, with no recognisable TATA-box region (Chen et al., 2014).
Retrospective analysis of model-based predictivity of human pharmacokinetics for anti-IL-36R monoclonal antibody MAB92 using a rat anti-mouse IL-36R monoclonal antibody and RNA expression data (FANTOM5)
Published in mAbs, 2019
Jennifer Ahlberg, Craig Giragossian, Hua Li, Maria Myzithras, Ernie Raymond, Gary Caviness, Christine Grimaldi, Su-Ellen Brown, Rocio Perez, Danlin Yang, Rachel Kroe-Barrett, David Joseph, Chandrasena Pamulapati, Kelly Coble, Peter Ruus, Joseph R. Woska, Rajkumar Ganesan, Steven Hansel, M. Lamine Mbow
The purpose of the experiments outlined herein is to characterize the PK of the anti-mouse IL-36R antibody, MAB04, in mice in support of the first-in-human (FIH) clinical trial. In this retrospective analysis, we incorporated molecule- and species-specific parameters, such as volume of distribution (Vc), intercompartmental transfer rates (k12 and k21), linear elimination (kel), binding affinity (KD), internalization rate of the drug–target complex (kint), target degradation rate (kdeg), and target abundance (R0), into a semi-mechanistic model. Two different methods of assigning target abundance were evaluated: the first assumed comparable expression between human and mouse, and the second utilized FANTOM5 RNA transcriptome data in a subset of matched tissues as a surrogate for expression in each respective species. FANTOM5 is a comprehensive expression dataset that includes ~1000 human and ~400 mouse tissues, primary cells, and cancer cell lines.22 This dataset is based on cap analysis of gene expression (CAGE), a method developed at RIKEN in Japan that characterizes transcription start sites across the entire genome at single-base resolution level.22–26 Since eukaryotic transcription factors are typically activating, the number of transcription factors on a promotor is predictive of breadth of expression.27 Human PK profiles were then simulated based on a semi-mechanistic TMDD model incorporating critical target-specific parameters for both the human candidate and mouse surrogate antibodies with R0 either assumed to be the same as that of mouse or corrected for the differences in RNA transcriptome data between species. For the human model utilizing the model-estimated mouse target abundance, Cmax was well predicted; however, AUC0-∞ was substantially underpredicted. After correcting for relative differences in RNA transcriptome data between species, the model-predicted human AUC0-∞ and Cmax were largely within 1.5-fold that observed for both nonsaturating and saturating doses.