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Title: Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction

Abstract

Here, the molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized. As a result, to identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation. We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription. Our results demonstrate the feasibility of assigning molecular function to proteins ofmore » unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators.« less

Authors:
 [1];  [1];  [2];  [1];  [2];  [3];  [2];  [1]
  1. Carnegie Institution for Science, Stanford, CA (United States)
  2. Univ. of Pennsylvania, Philadelphia, PA (United States)
  3. Carnegie Institution for Science, Stanford, CA (United States); Stanford Univ., Stanford, CA (United States)
Publication Date:
Research Org.:
Carnegie Institution for Science, Stanford, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1393512
Grant/Contract Number:
SC0008769
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
BMC Genomics
Additional Journal Information:
Journal Volume: 18; Journal Issue: 1; Journal ID: ISSN 1471-2164
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; Genes with unknown function; Transcriptional regulators; Coactivators; Polycomb repressive complex 2

Citation Formats

Bossi, Flavia, Fan, Jue, Xiao, Jun, Chandra, Lilyana, Shen, Max, Dorone, Yanniv, Wagner, Doris, and Rhee, Seung Y. Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction. United States: N. p., 2017. Web. doi:10.1186/s12864-017-3853-9.
Bossi, Flavia, Fan, Jue, Xiao, Jun, Chandra, Lilyana, Shen, Max, Dorone, Yanniv, Wagner, Doris, & Rhee, Seung Y. Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction. United States. doi:10.1186/s12864-017-3853-9.
Bossi, Flavia, Fan, Jue, Xiao, Jun, Chandra, Lilyana, Shen, Max, Dorone, Yanniv, Wagner, Doris, and Rhee, Seung Y. Mon . "Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction". United States. doi:10.1186/s12864-017-3853-9. https://www.osti.gov/servlets/purl/1393512.
@article{osti_1393512,
title = {Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction},
author = {Bossi, Flavia and Fan, Jue and Xiao, Jun and Chandra, Lilyana and Shen, Max and Dorone, Yanniv and Wagner, Doris and Rhee, Seung Y.},
abstractNote = {Here, the molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized. As a result, to identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation. We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription. Our results demonstrate the feasibility of assigning molecular function to proteins of unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators.},
doi = {10.1186/s12864-017-3853-9},
journal = {BMC Genomics},
number = 1,
volume = 18,
place = {United States},
year = {Mon Jun 26 00:00:00 EDT 2017},
month = {Mon Jun 26 00:00:00 EDT 2017}
}

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