July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Transcriptome landscape of early lens development
Author Affiliations & Notes
  • Salil Anil Lachke
    Department of Biological Sciences, University of Delaware, Newark, Delaware, United States
    Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States
  • Atul Kakrana
    Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States
  • Archana D Siddam
    Department of Biological Sciences, University of Delaware, Newark, Delaware, United States
  • Irfan Saadi
    Department of Anatomy and Cell Biology, University of Kansas Medical Center, Kansas City, Kansas, United States
  • Deepti Anand
    Department of Biological Sciences, University of Delaware, Newark, Delaware, United States
  • Footnotes
    Commercial Relationships   Salil Lachke, None; Atul Kakrana, None; Archana Siddam, None; Irfan Saadi, None; Deepti Anand, None
  • Footnotes
    Support  NIH/NEI R01 EY021505
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 894. doi:
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    • Get Citation

      Salil Anil Lachke, Atul Kakrana, Archana D Siddam, Irfan Saadi, Deepti Anand; Transcriptome landscape of early lens development. Invest. Ophthalmol. Vis. Sci. 2018;59(9):894.

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

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Abstract

Purpose : Transcriptome analysis of early lens development has been performed using microarray analysis. However, microarray-based expression profiling, while effective, is limited by the number of probes and prior sequence knowledge. Therefore, microarrays do not comprehensively inform on the full repertoire of coding and non-coding RNA in a given cell or tissue type. To address this deficit, we generated high-throughput RNA-sequencing (RNA-seq) data from early through late stages of mouse embryonic lens to identify new transcripts with potential function in lens development.

Methods : New paired-end RNA-seq data was generated from mouse lens at embryonic stages E10.5, E12.5, E14.5 and E16.5. A meta-analysis was performed whereby these new RNA-seq data were analyzed in the context of previously generated publicly available mouse lens RNA-seq profiles at embryonic (E15.0, E18.0) and post-natal (P0, P3, P6 and P9) stages. The analysis was performed using standard tools such as TopHat (for mapping reds), Cufflink, Cuffmerge, and Cuffnorm (fpor transcript assembly, merging, and normalization). A pipeline was developed to identify stage-specific transcripts and transcripts that are differentially expressed transcripts between different lens stages.

Results : We identified 1,145 million genome-mapped read-pairs from ten lens development stages. These reflected 44,939 high-confidence transcripts from 10,270 distinct loci that comprise 39,702 coding transcripts and 5,237 non-coding transcripts expressed in the lens across all these stages. The non-coding transcripts are classified – based on their position and function – as lincRNA (long intergenic RNA), lncRNA (long non-coding RNA), antisense and sense overlap, and transcripts of unknown potential. The average length and number of exons of non-coding transcripts is 799 nucleotides (nt) and 3 exons, while that of coding transcripts is 3,032 nt and 10 exons, respectively. The average open reading frame for non-coding transcripts is 157bp and for coding transcripts is 1,494bp. Finally, this analysis confirmed the high lens expression of known protein-coding genes and identified several new candidate genes in the lens.

Conclusions : These RNA-seq data define a comprehensive temporal atlas of coding and non-coding transcripts in mouse lens development. In silico subtraction of these RNA-seq datasets allows prioritization of new candidate genes with potential function in distinct stages of lens development.

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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