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1. In silico model for miRNA-mediated regulatory network in cancer

   https://doi.org/10.1093/bib/bbab264  

   Ahmed, Khandakar Tanvir; Sun, Jiao; Chen, William; Martinez, Irene; Cheng, Sze; Zhang, Wencai; Yong, Jeongsik; Zhang, Wei (January 2021, Briefings in Bioinformatics)

   null (Ed.)

   Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introduce a graph-based learning model, PTNet, which simulates the microRNAs (miRNAs) that regulate gene expression post-transcriptionally in silico. Our model does not require large-scale proteomics studies to measure the protein expression and can successfully predict the protein levels by considering the miRNA–mRNA interaction network, the mRNA expression, and the miRNA expression. Large-scale experiments on simulations and real cancer high-throughput datasets using PTNet validated that (i) the miRNA-mediated interaction network affects the abundance of corresponding proteins and (ii) the predicted protein expression has a higher correlation with the proteomics data (ground-truth) than the mRNA expression data. The classification performance also shows that the predicted protein expression has an improved prediction power on cancer outcomes compared to the prediction done by the mRNA expression data only or considering both mRNA and miRNA. Availability: PTNet toolbox is available at http://github.com/CompbioLabUCF/PTNet 

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2. A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity

   https://doi.org/10.1038/s41467-018-04575-0  

   Gam, Jeremy J.; Babb, Jonathan; Weiss, Ron (December 2018, Nature Communications)
3. Anti-miRNA Oligonucleotide Therapy for Chondrosarcoma

   https://doi.org/10.1158/1535-7163.MCT-18-1020  

   Sun, X; Chen, Y; Yu, H; Machan, J T; Alladin, A; Ramirez, J; Taliano, R; Hart, J; Chen, Q; Terek, R M. (January 2019, Molecular cancer therapeutics)
4. Computational annotation of miRNA transcription start sites

   https://doi.org/10.1093/bib/bbz178  

   Wang, Saidi; Talukder, Amlan; Cha, Mingyu; Li, Xiaoman; Hu, Haiyan (January 2020, Briefings in Bioinformatics)

   Abstract Motivation MicroRNAs (miRNAs) are small noncoding RNAs that play important roles in gene regulation and phenotype development. The identification of miRNA transcription start sites (TSSs) is critical to understand the functional roles of miRNA genes and their transcriptional regulation. Unlike protein-coding genes, miRNA TSSs are not directly detectable from conventional RNA-Seq experiments due to miRNA-specific process of biogenesis. In the past decade, large-scale genome-wide TSS-Seq and transcription activation marker profiling data have become available, based on which, many computational methods have been developed. These methods have greatly advanced genome-wide miRNA TSS annotation. Results In this study, we summarized recent computational methods and their results on miRNA TSS annotation. We collected and performed a comparative analysis of miRNA TSS annotations from 14 representative studies. We further compiled a robust set of miRNA TSSs (RSmirT) that are supported by multiple studies. Integrative genomic and epigenomic data analysis on RSmirT revealed the genomic and epigenomic features of miRNA TSSs as well as their relations to protein-coding and long non-coding genes. Contact xiaoman@mail.ucf.edu, haihu@cs.ucf.edu 

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5. miRNA induced 3D bioprinted-heterotypic osteochondral interface

   https://doi.org/10.1088/1758-5090/ac7fbb  

   Celik, Nazmiye; Kim, Myoung Hwan; Yeo, Miji; Kamal, Fadia; Hayes, Daniel J; Ozbolat, Ibrahim T (August 2022, Biofabrication)

   Abstract The engineering of osteochondral interfaces remains a challenge. MicroRNAs (miRs) have emerged as significant tools to regulate the differentiation and proliferation of osteogenic and chondrogenic formation in the human musculoskeletal system. Here, we describe a novel approach to osteochondral reconstruction based on the three-dimensional (3D) bioprinting of miR-transfected adipose-derived stem cell (ADSC) spheroids to produce a heterotypic interface that addresses the intrinsic limitations of the traditional approach to inducing zonal differentiation via the use of diffusible cytokines. We evaluated the delivery of miR-148b for osteogenic differentiation and the codelivery of miR-140 and miR-21 for the chondrogenic differentiation of ADSC spheroids. Our results demonstrated that miR-transfected ADSC spheroids exhibited upregulated expression of osteogenic and chondrogenic differentiation related gene and protein markers, and enhanced mineralization and cell proliferation compared to spheroids differentiated using a commercially-available differentiation medium. Upon confirmation of the osteogenic and chondrogenic potential of miR-transfected ADSC spheroids, using aspiration-assisted bioprinting, these spheroids were 3D bioprinted into a dual-layer heterotypic osteochondral interface with a stratified arrangement of distinct osteogenic and chondrogenic zones. The proposed approach holds great promise for the biofabrication of stratified tissues, not only for the osteochondral interfaces presented in this work, but also for other composite tissues and tissue interfaces, such as, but not limited to, the bone-tendon-muscle interface and craniofacial tissues. 

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