Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure
Artikel i vetenskaplig tidskrift, 2020
Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.
Författare
Jan Zrimec
Chalmers, Biologi och bioteknik, Systembiologi
Christoph Sebastian Börlin
Novo Nordisk Foundation Center for Biosustainability
Chalmers, Biologi och bioteknik, Systembiologi
Filip Buric
Chalmers, Biologi och bioteknik, Systembiologi
Muhammad Azam Sheikh
Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd, Data Science Research Engineers
Rongzhen Chen
Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd, Data Science Research Engineers
Verena Siewers
Chalmers, Biologi och bioteknik, Systembiologi
Vilhelm Verendel
Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd, Data Science Research Engineers
Jens B Nielsen
Novo Nordisk Foundation Center for Biosustainability
Chalmers, Biologi och bioteknik, Systembiologi
Mats Töpel
Göteborgs universitet
Gothenburg Global Biodiversity Centre
Aleksej Zelezniak
Science for Life Laboratory (SciLifeLab)
Chalmers, Biologi och bioteknik, Systembiologi
Nature Communications
2041-1723 (ISSN)
Vol. 11 1 6141Ämneskategorier
Evolutionsbiologi
Bioinformatik och systembiologi
Genetik
DOI
10.1038/s41467-020-19921-4
PubMed
33262328