The Growth-Arrest-Specific (GAS)-5 Long Non-Coding RNA: A Fascinating lncRNA Widely Expressed in Cancers

Long non-coding RNA (lncRNA) genes encode non-messenger RNAs that lack open reading frames (ORFs) longer than 300 nucleotides, lack evolutionary conservation in their shorter ORFs, and do not belong to any classical non-coding RNA category. LncRNA genes equal, or exceed in number, protein-coding genes in mammalian genomes. Most mammalian genomes harbor ~20,000 protein-coding genes that give rise to conventional messenger RNA (mRNA) transcripts. These coding genes exhibit sweeping evolutionary conservation in their ORFs. LncRNAs function via different mechanisms, including but not limited to: (1) serving as “enhancer” RNAs regulating nearby coding genes in cis; (2) functioning as scaffolds to create ribonucleoprotein (RNP) complexes; (3) serving as sponges for microRNAs; (4) acting as ribo-mimics of consensus transcription factor binding sites in genomic DNA; (5) hybridizing to other nucleic acids (mRNAs and genomic DNA); and, rarely, (6) as templates encoding small open reading frames (smORFs) that may encode short proteins. Any given lncRNA may have more than one of these functions. This review focuses on one fascinating case—the growth-arrest-specific (GAS)-5 gene, encoding a complicated repertoire of alternatively-spliced lncRNA isoforms. GAS5 is also a host gene of numerous small nucleolar (sno) RNAs, which are processed from its introns. Publications about this lncRNA date back over three decades, covering its role in cell proliferation, cell differentiation, and cancer. The GAS5 story has drawn in contributions from prominent molecular geneticists who attempted to define its tumor suppressor function in mechanistic terms. The evidence suggests that rodent Gas5 and human GAS5 functions may be different, despite the conserved multi-exonic architecture featuring intronic snoRNAs, and positional conservation on syntenic chromosomal regions indicating that the rodent Gas5 gene is the true ortholog of the GAS5 gene in man and other apes. There is no single answer to the molecular mechanism of GAS5 action. Our goal here is to summarize competing, not mutually exclusive, mechanistic explanations of GAS5 function that have compelling experimental support.

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The indirect antiviral potential of long non-coding RNAs encoded by IFITM pseudogenes

Journal of Virology ◽

10.1128/jvi.00680-21 ◽

2021 ◽

Author(s):

Kazi Rahman ◽

Alex A. Compton

Keyword(s):

Influenza A ◽

Virus Entry ◽

Gene Families ◽

Mrna Levels ◽

Protein Coding ◽

Protein Coding Genes ◽

Non Coding Rna ◽

Selective Forces ◽

Non Coding Rnas ◽

Long Non Coding Rna

The interferon-induced transmembrane ( IFITM ) family performs multiple functions in immunity, including inhibition of virus entry into cells. The IFITM repertoire varies widely between species and consists of protein-coding genes and pseudogenes. The selective forces driving pseudogenization within gene families are rarely understood. In this issue, the human pseudogene IFITM4P is characterized as a virus-induced, long non-coding RNA that contributes to restriction of Influenza A virus by regulating mRNA levels of IFITM1 , IFITM2 , and IFITM3 .

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Clinical significance of long non-coding RNA DUXAP8 and its protein coding genes in hepatocellular carcinoma

Journal of Cancer ◽

10.7150/jca.47902 ◽

2020 ◽

Vol 11 (20) ◽

pp. 6140-6156

Author(s):

Xiang-Kun Wang ◽

Xi-Wen Liao ◽

Rui Huang ◽

Jian-Lu Huang ◽

Zi-Jun Chen ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Clinical Significance ◽

Protein Coding ◽

Protein Coding Genes ◽

Non Coding Rna ◽

Long Non Coding Rna

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Utilizing Amino Acid Composition and Entropy of Potential Open Reading Frames to Identify Protein-Coding Genes

Microorganisms ◽

10.3390/microorganisms9010129 ◽

2021 ◽

Vol 9 (1) ◽

pp. 129

Author(s):

Katelyn McNair ◽

Carol L. Ecale Zhou ◽

Brian Souza ◽

Stephanie Malfatti ◽

Robert A. Edwards

Keyword(s):

Amino Acid ◽

Gene Prediction ◽

Training Model ◽

Entropy Density ◽

Open Reading Frames ◽

Initial Training ◽

Training Set ◽

Protein Coding ◽

Protein Coding Genes ◽

Reading Frames

One of the main steps in gene-finding in prokaryotes is determining which open reading frames encode for a protein, and which occur by chance alone. There are many different methods to differentiate the two; the most prevalent approach is using shared homology with a database of known genes. This method presents many pitfalls, most notably the catch that you only find genes that you have seen before. The four most popular prokaryotic gene-prediction programs (GeneMark, Glimmer, Prodigal, Phanotate) all use a protein-coding training model to predict protein-coding genes, with the latter three allowing for the training model to be created ab initio from the input genome. Different methods are available for creating the training model, and to increase the accuracy of such tools, we present here GOODORFS, a method for identifying protein-coding genes within a set of all possible open reading frames (ORFS). Our workflow begins with taking the amino acid frequencies of each ORF, calculating an entropy density profile (EDP), using KMeans to cluster the EDPs, and then selecting the cluster with the lowest variation as the coding ORFs. To test the efficacy of our method, we ran GOODORFS on 14,179 annotated phage genomes, and compared our results to the initial training-set creation step of four other similar methods (Glimmer, MED2, PHANOTATE, Prodigal). We found that GOODORFS was the most accurate (0.94) and had the best F1-score (0.85), while Glimmer had the highest precision (0.92) and PHANOTATE had the highest recall (0.96).

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LncRNA-Encoded Peptide: Functions and Predicting Methods

Frontiers in Oncology ◽

10.3389/fonc.2020.622294 ◽

2021 ◽

Vol 10 ◽

Author(s):

Jiani Xing ◽

Haizhou Liu ◽

Wei Jiang ◽

Lihong Wang

Keyword(s):

Mrna Stability ◽

Muscle Function ◽

Tumor Therapy ◽

Open Reading Frames ◽

Regulatory Pathways ◽

Non Coding Rna ◽

Non Coding Rnas ◽

Long Non Coding Rna ◽

Reading Frames ◽

Predicting Methods

Long non-coding RNA (lncRNA) was originally defined as the representative of the non-coding RNAs and unable to encode. However, recent reports suggest that some lncRNAs actually contain open reading frames that encode peptides. These coding products play important roles in the pathogenesis of many diseases. Here, we summarize the regulatory pathways of mammalian lncRNA-encoded peptides in influencing muscle function, mRNA stability, gene expression, and so on. We also address the promoting and inhibiting functions of the peptides in different cancers and other diseases. Then we introduce the computational predicting methods and data resources to predict the coding ability of lncRNA. The intention of this review is to provide references for further coding research and contribute to reveal the potential prospects for targeted tumor therapy.

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Long non-coding RNAs and splicing

Essays in Biochemistry ◽

10.1042/ebc20200087 ◽

2021 ◽

Author(s):

David Staněk

Keyword(s):

Dominant Role ◽

Splice Sites ◽

Protein Coding ◽

Protein Coding Genes ◽

Non Coding Rna ◽

Splicing Efficiency ◽

Non Coding Rnas ◽

Intron Removal ◽

Long Non Coding Rna

Abstract In this review I focus on the role of splicing in long non-coding RNA (lncRNA) life. First, I summarize differences between the splicing efficiency of protein-coding genes and lncRNAs and discuss why non-coding RNAs are spliced less efficiently. In the second half of the review, I speculate why splice sites are the most conserved sequences in lncRNAs and what additional roles could splicing play in lncRNA metabolism. I discuss the hypothesis that the splicing machinery can, besides its dominant role in intron removal and exon joining, protect cells from undesired transcripts.

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Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes

Briefings in Bioinformatics ◽

10.1093/bib/bby129 ◽

2019 ◽

Vol 21 (2) ◽

pp. 637-648 ◽

Cited By ~ 1

Author(s):

Aritro Nath ◽

Paul Geeleher ◽

R Stephanie Huang

Keyword(s):

Expression Patterns ◽

Imputation Accuracy ◽

High Quality ◽

Protein Coding ◽

Protein Coding Genes ◽

Non Coding Rna ◽

Non Coding Rnas ◽

Cancer Tissues ◽

Long Non Coding Rna

Abstract Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

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Coregulatory long non-coding RNA and protein-coding genes in serum starved cells

Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms ◽

10.1016/j.bbagrm.2018.11.004 ◽

2019 ◽

Vol 1862 (1) ◽

pp. 84-95 ◽

Cited By ~ 2

Author(s):

Fan Wang ◽

Rui Liang ◽

Benjamin Soibam ◽

Jin Yang ◽

Yu Liu

Keyword(s):

Protein Coding ◽

Protein Coding Genes ◽

Non Coding Rna ◽

Long Non Coding Rna

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HSF2 Co-regulates Protein-coding and Long Non-coding RNA Genes Specific to Black Tissues of the Black Chicken, Yeonsan Ogye

10.1101/223644 ◽

2017 ◽

Author(s):

Hyosun Hong ◽

Han-Ha Chai ◽

Kyoungwoo Nam ◽

Dajeong Lim ◽

Kyung-Tai Lee ◽

...

Keyword(s):

Dna Methylation ◽

Entire Body ◽

Specific Expression ◽

Protein Coding ◽

Tissue Specific ◽

Tissue Specific Expression ◽

Protein Coding Genes ◽

Non Coding Rna ◽

Black Coloring ◽

Long Non Coding Rna

AbstractThe Yeonsan Ogye (Ogye) is a rare Korean domestic chicken breed, the entire body of which, including its feathers and skin, has a unique black coloring. Although some protein-coding genes related to this unique feature have been examined, non-coding elements have not been globally investigated. In this study, high-throughput RNA sequencing and DNA methylation sequencing were performed to dissect the expression landscape of 14,264 Ogye protein-coding and 6900 long non-coding RNA (lncRNA) genes along with DNA methylation landscape in twenty different Ogye tissues. About 75% of Ogye lncRNAs showed tissue-specific expression whereas about 45% of protein-coding genes did. For some genes, the tissue-specific expression levels were inversely correlated with DNA methylation levels in their promoters. About 39% of the tissue-specific lncRNAs displayed functional association with proximal or distal protein-coding genes. In particular, heat shock transcription factor 2 (HSF2)-associated lncRNAs were discovered to be functionally linked to protein-coding genes that are specifically expressed in black skin tissues, tended to be more syntenically conserved in mammals, and were differentially expressed in black tissues relative to white tissues. Our results not only facilitate understanding how the non-coding genome regulates unique phenotypes but also should be of use for future genomic breeding of chickens.

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