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Herpesvirus microRNAs for Use in Gene Therapy Immune-Evasion Strategies
Published in Yashwant Pathak, Gene Delivery, 2022
Vineet Mahajan, Shruti Saptarshi, Yashwant Pathak
miRNA sequences appear to be conserved across species. A huge variety of miRNAs have been identified in plants and animals, including humans and viruses. Currently, over 2,000 human miRNAs are recognized in the comprehensive miRNA database miRbase 22 (http://www.mirbase.org). It is estimated that about 60% of human genes may be subjected to miRNA regulation.4 As miRNAs and their targets are not paired, a single miRNA can potentially affect more than 300 different mRNA transcripts, having a profound impact on gene expression patterns.
Tapping into the Formalin-Fixed Paraffin-Embedded (FFPE) Tissue Gold Mine for Individualization of Breast Cancer Treatment
Published in Brian Leyland-Jones, Pharmacogenetics of Breast Cancer, 2020
Mark Abramovitz, Brian Leyland-Jones
These low-molecular weight miRNAs can be extracted at the same time as total RNA is being isolated from FFPE tissue samples. qRT-PCR can be used to screen tumor samples for miRNA expression. The TaqMan™ array human miRNA panel from Applied Biosystems allows for the expression profiling of 365 miRNAs from one RNA sample. Illumina, Inc., has recently adapted its DASL assay for high-throughput multiplexed miRNA expression profiling (Fig. 1B). It has made a human miRNA panel that can be used to detect 470 miRNAs described in the miRBase database and an additional 265 potential miRNAs that have been identified in an RNA-primed array-based Klenow extension (RAKE) analysis study (57,58). With these arrays and tools now available, our understanding of the role that miRNAs play in breast cancer will be greatly enhanced.
Biomarkers for Organophosphate Poisoning: Physiological and Pathological Responses
Published in Brian J. Lukey, James A. Romano, Salem Harry, Chemical Warfare Agents, 2019
Arik Eisenkraft, Avshalom Falk, Kevin G. McGarry Jr.
Selection of Candidate Biomarkers, Validation, and Assessment. Eligible biomarker candidates from the profiling studies are miRNAs with statistically significant (P < 0.05) and at least 1.5-fold differential expression between the disease and control profiles. The selected candidates are verified by quantitative RT-PCR (qRT-PCR) analysis of each miRNA in individual patient samples. The experimental design in biomarker validation differs between the studies, which generally employ independent and larger patient cohorts. The diagnostic and predictive accuracy are evaluated by ROC analysis (see Zou et al., 2007 for a concise account), and the relevance to the disease pathology is assessed by bioinformatic analyses of miRNA target prediction, gene ontology (GO), and regulatory pathway enrichment. The bioinformatic aspect is highly important in miRNA biology due to the enormous number of known miRNA species (2872 mature miRNA species in humans in the current miRBase miRNA repository; Akhtar et al., 2016) and the “many-to-many” relationship of miRNAs and their target mRNAs. The methodology for selection of the most relevant targets and regulatory pathways is statistical analysis of target and pathway enrichment in disease versus control expression profiles (see an example in Gusev, 2008). A recent review (Akhtar et al., 2016) analyzes 129 bioinformatic tools for different applications of miRNA analysis, from target prediction and regulatory pathway identification to miRNA dysregulation in disease, including a circulating miRNA database (Russo et al., 2012).
E2 + norethisterone promotes the PI3K–AKT pathway via PGRMC1 to induce breast cancer cell proliferation
Published in Climacteric, 2022
L. Zhang, X. Ruan, M. Gu, A. O. Mueck
High-throughput RNA sequencing and analysis were performed as elsewhere described [20]. Total RNA from three cells was isolated by TRIzol (ThermoFisher Scientific). The RNA libraries were constructed by Illumina library preparation protocols. High-throughput sequencing was performed on HiSeq2000. For miRNA analysis, sequencing reads were mapped to the reference genome by Hierarchical Indexing for Spliced Alignment of Transcripts (HISAT) (D, 2015). The reads of miRNAs were counted by bedtools coverage (V2.17.0). Reference miRNAs downloaded from miRBase (Release 21) were used for miRNA analysis. Data from sample duplicates were pooled for further analyses as correlation coefficients of gene expression levels from biological duplicates were all greater than 0.95. The expression level of miRNAs was represented by FPKM calculated by Cufflinks (v2.0.2). The differentially expressed genes were identified with Cuffdiff based on the threshold of q-value being less than 0.05. RefSeq miRNAs downloaded from Bowtie2 (B, 2012) were used as the reference miRNAs.
Expression levels of microRNAs that are potential cytochrome P450 regulators in cynomolgus macaques
Published in Xenobiotica, 2020
Yasuhiro Uno, Hiroshi Yamazaki
MicroRNAs, important in gene regulatory networks, bind to the target sequences in the 3′-untranslated regions of mRNAs, resulting in mRNA degradation or translational repression (Bartel, 2004; Guo et al., 2010). Numerous microRNAs have been identified, named and deposited to database such as miRbase (Griffiths-Jones et al., 2007; Kozomara et al., 2019). In humans, hepatic expression of P450s is regulated by various nuclear receptors, transcription factors and microRNAs (Bolleyn et al., 2015; Dluzen & Lazarus, 2015; He et al., 2015; Yokoi & Nakajima, 2011; Yu et al., 2016), which are termed P450 regulators in this article. For example, the important drug-metabolizing enzyme CYP3A4 is regulated by hsa-miR-27b (Pan et al., 2009), whereas CYP2E1 is regulated by hsa-miR-378 (Mohri et al., 2010). The expression level of human CYP1A1 is correlated with the levels of hsa-miR-18b and hsa-miR-20b (Glubb & Innocenti, 2011; Wang et al., 2009), indicating the potential regulation of CYP1A1 expression by these microRNAs (He et al., 2015). However, the microRNAs involved in P450 regulation have not been investigated in cynomolgus macaques.
Extracellular vesicles from human iPSC-derived neural stem cells: miRNA and protein signatures, and anti-inflammatory and neurogenic properties
Published in Journal of Extracellular Vesicles, 2020
Raghavendra Upadhya, Leelavathi N. Madhu, Sahithi Attaluri, Daniel Leite Góes Gitaí, Marisa R Pinson, Maheedhar Kodali, Geetha Shetty, Gabriele Zanirati, Smrithi Kumar, Bing Shuai, Susan T Weintraub, Ashok K. Shetty
The enrichment pathway analysis was performed by using DIANA-279 miRPath v.3, a web tool that identifies enriched pathways targeted by selected miRNAs [28]. Since many predicted targets are false-positives, we have included only experimentally validated human target genes by using the Tarbase v7.0 database. The miR-ID was based on Mirbase version 21. For miRs with ambiguous IDs, we used both 3p and 5p arms (miRNA stem-loop). The functional annotation was performed according to the “Kyoto Encyclopedia” of Genes and Genomes (KEGG) databases. We used the “Pathways/Categories Union” algorithm to assess the combined action of selected miRNAs. Fisher´s exact test was chosen for enrichment analysis, with a microTthreshold of 0.8, false discovery rate (FDR) correction and p value threshold at ≤ 0.05. The heat map of microRNAs versus pathways was based on significance values, where darker colours represent lower p values.