July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Identification of putative small open reading frame peptides in the retinal transcriptome
Author Affiliations & Notes
  • Lina Zelinger
    NNRL, National Eye Institute, Bethesda, Maryland, United States
  • Vijender Chaitankar
    NNRL, National Eye Institute, Bethesda, Maryland, United States
    Bioinformatics and Computational Biology Core Facility, NHLBI, Bethesda, Maryland, United States
  • Jacqueline Minehart
    NNRL, National Eye Institute, Bethesda, Maryland, United States
  • Angel Aponte
    Proteomics Core Facility, NHLBI, Bethesda, Maryland, United States
  • Anand Swaroop
    NNRL, National Eye Institute, Bethesda, Maryland, United States
  • Footnotes
    Commercial Relationships   Lina Zelinger, None; Vijender Chaitankar, None; Jacqueline Minehart, None; Angel Aponte, None; Anand Swaroop, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 6023. doi:
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    • Get Citation

      Lina Zelinger, Vijender Chaitankar, Jacqueline Minehart, Angel Aponte, Anand Swaroop; Identification of putative small open reading frame peptides in the retinal transcriptome. Invest. Ophthalmol. Vis. Sci. 2018;59(9):6023.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Our goal is to provide a comprehensive description of the transcriptomic output in the retina, and particularly the photoreceptors. In the current study, we focus on the potential of non-coding RNAs (ncRNAs) to produce small open reading frame (smORF) peptides and identify their function in the retina. This will serve as a basis to study mechanisms and gene regulatory networks underlying development and disease.

Methods : RNA was isolated from (Nrlp-GFP) rods and S-cone like (Nrlp-GFP;Nrl-/-) photoreceptors purified from mice retina by fluorescence-activated cell sorting. RNA-seq profiles and, genome guided de novo transcriptome assembly was done using TopHat2 v2.0.11 and Cufflinks v2.2.1. Ensembl annotation (v84) and transcriptomic profiles were used to build a custom database of ncRNAs. NcRNAs were translated using ORFfinder to generate a list of putative smORF peptides ranging from 30 to 120 amino acids in size. Total protein was extracted from P28 WT retina, trypsinized and chromatographically separated (high-pH RPLC) into 15 concatenated fractions for LC-MS/MS analysis on Orbitrap Lumos Tribrid mass spectrometer. The raw data was searched against a proteomic database, consisting of smORF peptides and mouse protein sequences (UniProt) to identify peptides originating in ncRNAs.

Results : Bioinformatics analysis yielded over 90 thousand potential peptides. 1545 peptides matching 1399 ncRNAs genes were identified by LC-MS/MS analysis (FDR of 95%). Out of these 1071 (70%), are unique hits, matching only one ncRNA gene. We then filtered the data, to eliminate any hits matching pseudogenes, leaving 817 genes. Out of these 2% (14/817) are processed transcripts, 8% (64/817) genes are antisense RNAs, 14% (117/817) are annotated lncRNAs, and 56% (462/817) originate from previously un-annotated transcripts. To further focus our analysis on the highest confidence peptides we eliminated any genes with a single peptide match, leaving 13 ncRNA genes, with peptides ranging in size from 30 to 113 amino acid. Additional studies are now being performed to validate these findings and characterize the function of the peptides.

Conclusions : Using bioinformatics and proteomic data analysis we identified over 1000 putative smORF peptides originating in genes categorized as ncRNA. These findings help understand the role of understudied elements of the transcriptome, and elucidate their roles in development and disease.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

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