Evidence map›Paper›PMID 31390792›Full record

ArticleGenes2019

Long Non-Coding RNA Expression Levels Modulate Cell-Type-Specific Splicing Patterns by Altering Their Interaction Landscape with RNA-Binding Proteins.

Felipe Wendt Porto, Swapna Vidhur Daulatabad, Sarath Chandra Janga

Open access · goldAbstract read
In one paragraph

Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. From the genome's perspective: Bearing somatic retrotransposition to leverage the regulatory potential of L1 RNAs.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025
    Review
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  6. Article
  7. Review
  8. [Long non-coding RNA UPK1A-AS1 promotes glycolysis in hepatocellular carcinoma cellsNan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021
    Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Felipe Wendt PortoDepartment of BioHealth Informatics, School of Informatics and Computing, IUPUI, Indianapolis, IN 46202, USA.
Swapna Vidhur DaulatabadDepartment of BioHealth Informatics, School of Informatics and Computing, IUPUI, Indianapolis, IN 46202, USA.ORCID 0000-0001-5288-8599
Sarath Chandra JangaDepartment of BioHealth Informatics, School of Informatics and Computing, IUPUI, Indianapolis, IN 46202, USA. scjanga@iupui.edu.ORCID 0000-0001-7351-6268
Indiana University – Purdue University Indianapolis · USIndiana University School of Medicine

Funding

Mapping RNA protein interaction networks in the human genomeR01GM123314 · NIGMS · INDIANA UNIVERSITY INDIANAPOLIS · PI JANGA, SARATH CHANDRA · 2017 to 2021
$1.7M
NIGMS NIH HHS R01 GM123314
6 · The paper itself

Abstract

Recent developments in our understanding of the interactions between long non-coding RNAs (lncRNAs) and cellular components have improved treatment approaches for various human diseases including cancer, vascular diseases, and neurological diseases. Although investigation of specific lncRNAs revealed their role in the metabolism of cellular RNA, our understanding of their contribution to post-transcriptional regulation is relatively limited. In this study, we explore the role of lncRNAs in modulating alternative splicing and their impact on downstream protein-RNA interaction networks. Analysis of alternative splicing events across 39 lncRNA knockdown and wildtype RNA-sequencing datasets from three human cell lines-HeLa (cervical cancer), K562 (myeloid leukemia), and U87 (glioblastoma)-resulted in the high-confidence (false discovery rate (fdr) < 0.01) identification of 11,630 skipped exon events and 5895 retained intron events, implicating 759 genes to be impacted at the post-transcriptional level due to the loss of lncRNAs. We observed that a majority of the alternatively spliced genes in a lncRNA knockdown were specific to the cell type. In tandem, the functions annotated to the genes affected by alternative splicing across each lncRNA knockdown also displayed cell-type specificity. To understand the mechanism behind this cell-type-specific alternative splicing pattern, we analyzed RNA-binding protein (RBP)-RNA interaction profiles across the spliced regions in order to observe cell-type-specific alternative splice event RBP binding preference. Despite limited RBP binding data across cell lines, alternatively spliced events detected in lncRNA perturbation experiments were associated with RBPs binding in proximal intron-exon junctions in a cell-type-specific manner. The cellular functions affected by alternative splicing were also affected in a cell-type-specific manner. Based on the RBP binding profiles in HeLa and K562 cells, we hypothesize that several lncRNAs are likely to exhibit a sponge effect in disease contexts, resulting in the functional disruption of RBPs and their downstream functions. We propose that such lncRNA sponges can extensively rewire post-transcriptional gene regulatory networks by altering the protein-RNA interaction landscape in a cell-type-specific manner.

Indexed as

Alternative SplicingHeLa CellsHumansNeoplasmsOrgan SpecificityProtein BindingRNA-Binding ProteinsRNA, Long NoncodingRNA-Binding ProteinsRNA, Long Noncodingalternative splicingcancercell-type-specificfunctional enrichmentlong non-coding RNAprotein binding lncRNA spongesRNA-binding proteinssecondary RNA structure

Identifiers

PMID31390792
PMCPMC6722645
OpenAlexW2965871523

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.