scholarly journals Functional Screening Techniques to Identify Long Non-Coding RNAs as Therapeutic Targets in Cancer

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3695
Author(s):  
Kathleen M. Lucere ◽  
Megan M. R. O’Malley ◽  
Sarah D. Diermeier
Keyword(s):  
Antisense Oligonucleotides ◽  
High Throughput Screening ◽  
Large Scale ◽  
Therapeutic Targets ◽  
Functional Screening ◽  
Protein Coding ◽  
Non Coding Rnas ◽  
New Therapeutic Targets ◽  
Functional Screens ◽  
Screening Approaches

Recent technological advancements such as CRISPR/Cas-based systems enable multiplexed, high-throughput screening for new therapeutic targets in cancer. While numerous functional screens have been performed on protein-coding genes to date, long non-coding RNAs (lncRNAs) represent an emerging class of potential oncogenes and tumor suppressors, with only a handful of large-scale screens performed thus far. Here, we review in detail currently available screening approaches to identify new lncRNA drivers of tumorigenesis and tumor progression. We discuss the various approaches of genomic and transcriptional targeting using CRISPR/Cas9, as well as methods to post-transcriptionally target lncRNAs via RNA interference (RNAi), antisense oligonucleotides (ASOs) and CRISPR/Cas13. We discuss potential advantages, caveats and future applications of each method to provide an overview and guide on investigating lncRNAs as new therapeutic targets in cancer.

scholarly journals Functional Screening of a Metagenomic Library Reveals Operons Responsible for Enhanced Intestinal Colonization by Gut Commensal Microbes

2013 ◽  
Vol 79 (12) ◽  
pp. 3829-3838 ◽  
Author(s):  
Mi Young Yoon ◽  
Kang-Mu Lee ◽  
Yujin Yoon ◽  
Junhyeok Go ◽  
Yongjin Park ◽  
...  
Keyword(s):  
Bac Library ◽  
Metagenomic Library ◽  
Artificial Chromosome ◽  
Open Reading Frames ◽  
Functional Screening ◽  
Sequence Alignments ◽  
Protein Coding ◽  
Content Type ◽  
Usage Analysis ◽  
Functional Screens

ABSTRACTEvidence suggests that gut microbes colonize the mammalian intestine through propagation as an adhesive microbial community. A bacterial artificial chromosome (BAC) library of murine bowel microbiota DNA in the surrogate hostEscherichia coliDH10B was screened for enhanced adherence capability. Two out of 5,472 DH10B clones, 10G6 and 25G1, exhibited enhanced capabilities to adhere to inanimate surfaces in functional screens. DNA segments inserted into the 10G6 and 25G1 clones were 52 and 41 kb and included 47 and 41 protein-coding open reading frames (ORFs), respectively. DNA sequence alignments, tetranucleotide frequency, and codon usage analysis strongly suggest that these two DNA fragments are derived from species belonging to the genusBacteroides. Consistent with this finding, a large portion of the predicted gene products were highly homologous to those ofBacteroidesspp. Transposon mutagenesis and subsequent experiments that involved heterologous expression identified two operons associated with enhanced adherence.E. colistrains transformed with the 10a or 25b operon adhered to the surface of intestinal epithelium and colonized the mouse intestine more vigorously than did the control strain. This study has revealed the genetic determinants of unknown commensals (probably resemblingBacteroidesspecies) that enhance the ability of the bacteria to colonize the murine bowel.

scholarly journals Characterization and identification of long non-coding RNAs based on feature relationship

2018 ◽  
Author(s):  
Guangyu Wang ◽  
Hongyan Yin ◽  
Boyang Li ◽  
Chunlei Yu ◽  
Fan Wang ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Large Scale ◽  
Gc Content ◽  
Training Data ◽  
Significant Advance ◽  
Protein Coding ◽  
Reading Frame ◽  
Wide Range ◽  
Non Coding Rnas ◽  
Computational Identification

ABSTRACTThe significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging; it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations. Here we first characterize lncRNAs by contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between ORF (open reading frame) length and GC content presents universally substantial divergence in lncRNAs and protein-coding RNAs, as observed in a broad variety of species. Based on the feature relationship, accordingly, we further present LGC, a novel algorithm for identifying lncRNAs that is able to accurately distinguish lncRNAs from protein-coding RNAs in a cross-species manner without any prior knowledge. As validated on large-scale empirical datasets, comparative results show that LGC outperforms existing algorithms by achieving higher accuracy, well-balanced sensitivity and specificity, and is robustly effective (>90% accuracy) in discriminating lncRNAs from protein-coding RNAs across diverse species that range from plants to mammals. To our knowledge, this study, for the first time, differentially characterizes lncRNAs and protein-coding RNAs based on feature relationship, which is further applied in computational identification of lncRNAs. Taken together, our study represents a significant advance in characterization and identification of lncRNAs and LGC thus bears broad potential utility for computational analysis of lncRNAs in a wide range of species.

scholarly journals Common Features in lncRNA Annotation and Classification: A Survey

2021 ◽  
Vol 7 (4) ◽  
pp. 77
Author(s):  
Christopher Klapproth ◽  
Rituparno Sen ◽  
Peter F. Stadler ◽  
Sven Findeiß ◽  
Jörg Fallmann
Keyword(s):  
Gene Expression ◽  
Disease Progression ◽  
In Silico ◽  
Therapeutic Targets ◽  
Untranslated Regions ◽  
Protein Coding ◽  
Coding Sequence ◽  
Common Features ◽  
Research Gap ◽  
Non Coding Rnas

Long non-coding RNAs (lncRNAs) are widely recognized as important regulators of gene expression. Their molecular functions range from miRNA sponging to chromatin-associated mechanisms, leading to effects in disease progression and establishing them as diagnostic and therapeutic targets. Still, only a few representatives of this diverse class of RNAs are well studied, while the vast majority is poorly described beyond the existence of their transcripts. In this review we survey common in silico approaches for lncRNA annotation. We focus on the well-established sets of features used for classification and discuss their specific advantages and weaknesses. While the available tools perform very well for the task of distinguishing coding sequence from other RNAs, we find that current methods are not well suited to distinguish lncRNAs or parts thereof from other non-protein-coding input sequences. We conclude that the distinction of lncRNAs from intronic sequences and untranslated regions of coding mRNAs remains a pressing research gap.

scholarly journals Long Non-Coding RNAs: The Regulatory Mechanisms, Research Strategies, and Future Directions in Cancers

2020 ◽  
Vol 10 ◽  
Author(s):  
Na Gao ◽  
Yueheng Li ◽  
Jing Li ◽  
Zhengfan Gao ◽  
Zhenzhen Yang ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Tumor Development ◽  
Therapeutic Targets ◽  
Regulatory Mechanisms ◽  
Clinical Implementation ◽  
Research Strategies ◽  
Protein Coding ◽  
Functional Studies ◽  
Diagnosis And Prognosis ◽  
Non Coding Rnas

The development and application of whole genome sequencing technology has greatly broadened our horizons on the capabilities of long non-coding RNAs (lncRNAs). LncRNAs are more than 200 nucleotides in length and lack protein-coding potential. Increasing evidence indicates that lncRNAs exert an irreplaceable role in tumor initiation, progression, as well as metastasis, and are novel molecular biomarkers for diagnosis and prognosis of cancer patients. Furthermore, lncRNAs and the pathways they influence might represent promising therapeutic targets for a number of tumors. Here, we discuss the recent advances in understanding of the specific regulatory mechanisms of lncRNAs. We focused on the signal, decoy, guide, and scaffold functions of lncRNAs at the epigenetic, transcription, and post-transcription levels in cancer cells. Additionally, we summarize the research strategies used to investigate the roles of lncRNAs in tumors, including lncRNAs screening, lncRNAs characteristic analyses, functional studies, and molecular mechanisms of lncRNAs. This review will provide a short but comprehensive description of the lncRNA functions in tumor development and progression, thus accelerating the clinical implementation of lncRNAs as tumor biomarkers and therapeutic targets.

scholarly journals Non-Coding RNAs as New Therapeutic Targets in the Context of Renal Fibrosis

2019 ◽  
Vol 20 (8) ◽  
pp. 1977 ◽  
Author(s):  
Cynthia Van der Hauwaert ◽  
François Glowacki ◽  
Nicolas Pottier ◽  
Christelle Cauffiez
Keyword(s):  
Renal Fibrosis ◽  
Molecular Mechanisms ◽  
Tissue Injury ◽  
Kidney Diseases ◽  
Therapeutic Targets ◽  
Future Research ◽  
Kidney Fibrosis ◽  
Extracellular Matrix Components ◽  
Non Coding Rnas ◽  
New Therapeutic Targets

Fibrosis, or tissue scarring, is defined as the excessive, persistent and destructive accumulation of extracellular matrix components in response to chronic tissue injury. Renal fibrosis represents the final stage of most chronic kidney diseases and contributes to the progressive and irreversible decline in kidney function. Limited therapeutic options are available and the molecular mechanisms governing the renal fibrosis process are complex and remain poorly understood. Recently, the role of non-coding RNAs, and in particular microRNAs (miRNAs), has been described in kidney fibrosis. Seminal studies have highlighted their potential importance as new therapeutic targets and innovative diagnostic and/or prognostic biomarkers. This review will summarize recent scientific advances and will discuss potential clinical applications as well as future research directions.

scholarly journals Unique genomic features and deeply-conserved functions of long non-coding RNAs in the Cancer LncRNA Census (CLC)

2017 ◽  
Author(s):  
Joana Carlevaro-Fita ◽  
Andrés Lanzós ◽  
Lars Feuerbach ◽  
Chen Hong ◽  
David Mas-Ponte ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Cancer Genomics ◽  
De Novo ◽  
Cancer Type ◽  
Cancer Genes ◽  
Driver Genes ◽  
Protein Coding ◽  
Evidence Type ◽  
Non Coding Rnas ◽  
Functional Screens

AbstractLong non-coding RNAs (lncRNAs) that drive tumorigenesis are a growing focus of cancer genomics studies. To facilitate further discovery, we have created the “Cancer LncRNA Census” (CLC), a manually-curated and strictly-defined compilation of lncRNAs with causative roles in cancer. CLC has two principle applications: first, as a resource for training and benchmarking de novo identification methods; and second, as a dataset for studying the fundamental properties of these genes.CLC Version 1 comprises 122 lncRNAs implicated in 29 distinct cancers. LncRNAs are included based on functional or genetic evidence for causative roles in cancer progression. All belong to the GENCODE reference annotation, to enable integration across projects and datasets. For each entry, the evidence type, biological activity (oncogene or tumour suppressor), source reference and cancer type are recorded. Supporting its usefulness, CLC genes are significantly enriched amongst de novo predicted driver genes from PCAWG. CLC genes are distinguished from other lncRNAs by a series of features consistent with biological function, including gene length, high expression and sequence conservation of both exons and promoters. We identify a trend for CLC genes to be co-localised with known protein-coding cancer genes along the human genome. Finally, by integrating data from transposon-mutagenesis functional screens, we show that mouse orthologues of CLC genes tend also to be cancer genes.Thus CLC represents a valuable resource for research into long non-coding RNAs in cancer. Their evolutionary and genomic properties have implications for understanding disease mechanisms and point to conserved functions across ~80 million years of evolution.

scholarly journals Functional and transcriptional profiling of non-coding RNAs in yeast reveal context-dependent phenotypes and in trans effects on the protein regulatory network

PLoS Genetics ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. e1008761
Author(s):  
Laura Natalia Balarezo-Cisneros ◽  
Steven Parker ◽  
Marcin G. Fraczek ◽  
Soukaina Timouma ◽  
Ping Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Transcriptional Profiling ◽  
Growth Conditions ◽  
Phenotypic Data ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Genome Wide ◽  
In Trans ◽  
Non Coding Rnas ◽  
The Impact

Non-coding RNAs (ncRNAs), including the more recently identified Stable Unannotated Transcripts (SUTs) and Cryptic Unstable Transcripts (CUTs), are increasingly being shown to play pivotal roles in the transcriptional and post-transcriptional regulation of genes in eukaryotes. Here, we carried out a large-scale screening of ncRNAs in Saccharomyces cerevisiae, and provide evidence for SUT and CUT function. Phenotypic data on 372 ncRNA deletion strains in 23 different growth conditions were collected, identifying ncRNAs responsible for significant cellular fitness changes. Transcriptome profiles were assembled for 18 haploid ncRNA deletion mutants and 2 essential ncRNA heterozygous deletants. Guided by the resulting RNA-seq data we analysed the genome-wide dysregulation of protein coding genes and non-coding transcripts. Novel functional ncRNAs, SUT125, SUT126, SUT035 and SUT532 that act in trans by modulating transcription factors were identified. Furthermore, we described the impact of SUTs and CUTs in modulating coding gene expression in response to different environmental conditions, regulating important biological process such as respiration (SUT125, SUT126, SUT035, SUT432), steroid biosynthesis (CUT494, SUT053, SUT468) or rRNA processing (SUT075 and snR30). Overall, these data capture and integrate the regulatory and phenotypic network of ncRNAs and protein-coding genes, providing genome-wide evidence of the impact of ncRNAs on cellular homeostasis.

scholarly journals Characterization and identification of long non-coding RNAs based on feature relationship

2019 ◽  
Vol 35 (17) ◽  
pp. 2949-2956 ◽  
Author(s):  
Guangyu Wang ◽  
Hongyan Yin ◽  
Boyang Li ◽  
Chunlei Yu ◽  
Fan Wang ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Large Scale ◽  
Gc Content ◽  
Training Data ◽  
Supplementary Information ◽  
Significant Advance ◽  
Protein Coding ◽  
Wide Range ◽  
Non Coding Rnas ◽  
Computational Identification

Abstract Motivation The significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging; it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations. Results Here we first characterize lncRNAs in contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between open reading frame length and guanine-cytosine (GC) content presents universally substantial divergence in lncRNAs and protein-coding RNAs, as observed in a broad variety of species. Based on the feature relationship, accordingly, we further present LGC, a novel algorithm for identifying lncRNAs that is able to accurately distinguish lncRNAs from protein-coding RNAs in a cross-species manner without any prior knowledge. As validated on large-scale empirical datasets, comparative results show that LGC outperforms existing algorithms by achieving higher accuracy, well-balanced sensitivity and specificity, and is robustly effective (>90% accuracy) in discriminating lncRNAs from protein-coding RNAs across diverse species that range from plants to mammals. To our knowledge, this study, for the first time, differentially characterizes lncRNAs and protein-coding RNAs based on feature relationship, which is further applied in computational identification of lncRNAs. Taken together, our study represents a significant advance in characterization and identification of lncRNAs and LGC thus bears broad potential utility for computational analysis of lncRNAs in a wide range of species. Availability and implementation LGC web server is publicly available at http://bigd.big.ac.cn/lgc/calculator. The scripts and data can be downloaded at http://bigd.big.ac.cn/biocode/tools/BT000004. Supplementary information Supplementary data are available at Bioinformatics online.

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