The functions of genomic features and how they regulate gene expression are poorly understood. A recently developed approach, native elongating transcript sequencing (NET-seq), directly monitors transcriptional activity of elongating RNA polymerase II (RNAPII) with single-nucleotide resolution across a genome. Initial application of NET-seq to S. cerevisiae revealed previously unknown features of transcription. We created and tested models of how transcription is influenced by genomic features utilizing comparison of S. cerevisiae NET-seq data with published datasets. To determine the transcriptional consequences of features of the human genome, including the molecular mechanisms of global transcription regulation, there is a critical need to adapt the NET-seq approach to observe transcription in human cells. The long-term goal is to determine how human transcription and co-transcriptional processes are regulated by DNA sequence, chromatin modifications, and transcription factors. Through expanding the NET-seq protocol, the objective of this proposal is to form a fundamental understanding of transcriptional control through NET-seq analysis of yeast mutants and a range of human cell lines covering distinct areas of human biology. The rationale of this proposal is that through a deep understanding of transcriptional activity, it will be possible to dissect the mechanisms by which genomic features and cellular factors control gene expression activity. The following three specific aims have been formulated to accomplish these objectives: 1) Streamline, optimize, and expand the NET-seq approach;2) determine how transcriptional activity is modulated by key factors in S. cerevisiae;and 3) adapt native elongating transcript sequencing (NET-seq) for human cells. Under the first aim, upgrades to NET-seq will allow it to be used more broadly, both in yeast and in other organisms. Under the second aim, by obtaining NET-seq profiles for a comprehensive set of 55 S. cerevisiae mutants, fundamental insight into mechanisms of transcriptional control will be revealed, which will help to guide future NET-seq experiments in human cells. Under the third aim, establishing NET-seq as a straightforward and high-resolution approach for the study of human transcription will make critical connections between features of the human genome and transcriptional activity. The proposed research is innovative, because it employs and expands NET-seq, a methodology that substantially surpasses the established methodologies in both resolution and in simplicity of the approach. Furthermore, broad application of NET-seq is highly likely to uncover novel aspects of transcription regulation and reveal new mechanisms that control transcription in biological processes. This contribution is significant because it wil supply the field, and beyond, with a foundational understanding of how aspects of transcription are controlled and provide a tool that can be broadly used in all types of mechanistic studies involving transcription. Ultimately, such knowledge has the potential to impact all human diseases as transcription regulation is a critical component of most human biological processes.

Public Health Relevance

The proposed research is relevant to public health, because discovery of regulatory mechanisms in transcription at high resolution is ultimately expected to significantly impact our understanding of most human disease. As such, the proposed research is relevant to the part of the NIH's mission that seeks to develop fundamental knowledge to inform our diagnosis and treatment of human disease.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
1R01HG007173-01
Application #
8480073
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Pazin, Michael J
Project Start
2013-04-01
Project End
2018-03-31
Budget Start
2013-04-01
Budget End
2014-03-31
Support Year
1
Fiscal Year
2013
Total Cost
$423,438
Indirect Cost
$173,438
Name
Harvard University
Department
Genetics
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
02115
Marquardt, Sebastian; Escalante-Chong, Renan; Pho, Nam et al. (2014) A chromatin-based mechanism for limiting divergent noncoding transcription. Cell 157:1712-23