Structural basis of SNAPc-dependent snRNA transcription initiation by RNA polymerase II

Human promoter directionality is determined by transcriptional initiation and the opposing activities of INTS11 and CDK9

RNA polymerase II (RNAPII) transcription initiates bidirectionally at many human protein-coding genes. Sense transcription usually dominates and leads to messenger RNA production, whereas antisense transcription rapidly terminates. The basis for this directionality is not fully understood. Here, we show that sense transcriptional initiation is more efficient than in the antisense direction, which establishes initial promoter directionality. After transcription begins, the opposing functions of the endonucleolytic subunit of Integrator, INTS11, and cyclin-dependent kinase 9 (CDK9) maintain directionality. Specifically, INTS11 terminates antisense transcription, whereas sense transcription is protected from INTS11-dependent attenuation by CDK9 activity. Strikingly, INTS11 attenuates transcription in both directions upon CDK9 inhibition, and the engineered recruitment of CDK9 desensitises transcription to INTS11. Therefore, the preferential initiation of sense transcription and the opposing activities of CDK9 and INTS11 explain mammalian promoter directionality.

Structural visualization of transcription initiation in action

Transcription initiation is a complex process, and its mechanism is incompletely understood. We determined the structures of de novo transcribing complexes TC2 to TC17 with RNA polymerase II halted on G-less promoters when nascent RNAs reach 2 to 17 nucleotides in length, respectively. Connecting these structures generated a movie and a working model. As initially synthesized RNA grows, general transcription factors (GTFs) remain bound to the promoter and the transcription bubble expands. Nucleoside triphosphate (NTP)-driven RNA-DNA translocation and template-strand accumulation in a nearly sealed channel may promote the transition from initially transcribing complexes (ITCs) (TC2 to TC9) to early elongation complexes (EECs) (TC10 to TC17). Our study shows dynamic processes of transcription initiation and reveals why ITCs require GTFs and bubble expansion for initial RNA synthesis, whereas EECs need GTF dissociation from the promoter and bubble collapse for promoter escape.

RNA polymerase II transcription with partially assembled TFIID complexes

The recognition of core promoter sequences by the general transcription factor TFIID is the first step in the process of RNA polymerase II (Pol II) transcription initiation. Metazoan holo-TFIID is composed of the TATA binding protein (TBP) and of 13 TBP associated factors (TAFs). Inducible Taf7 knock out (KO) results in the formation of a Taf7-less TFIID complex, while Taf10 KO leads to serious defects within the TFIID assembly pathway. Either TAF7 or TAF10 depletions correlate with the detected TAF occupancy changes at promoters, and with the distinct phenotype severities observed in mouse embryonic stem cells or mouse embryos. Surprisingly however, under either Taf7 or Taf10 deletion conditions, TBP is still associated to the chromatin, and no major changes are observed in nascent Pol II transcription. Thus, partially assembled TFIID complexes can sustain Pol II transcription initiation, but cannot replace holo-TFIID over several cell divisions and/or development.

Predicting Corynebacterium glutamicum promoters based on novel feature descriptor and feature selection technique

The promoter is an important noncoding DNA regulatory element, which combines with RNA polymerase to activate the expression of downstream genes. In industry, artificial arginine is mainly synthesized by Corynebacterium glutamicum. Replication of specific promoter regions can increase arginine production. Therefore, it is necessary to accurately locate the promoter in C. glutamicum. In the wet experiment, promoter identification depends on sigma factors and DNA splicing technology, this is a laborious job. To quickly and conveniently identify the promoters in C. glutamicum, we have developed a method based on novel feature representation and feature selection to complete this task, describing the DNA sequences through statistical parameters of multiple physicochemical properties, filtering redundant features by combining analysis of variance and hierarchical clustering, the prediction accuracy of the which is as high as 91.6%, the sensitivity of 91.9% can effectively identify promoters, and the specificity of 91.2% can accurately identify non-promoters. In addition, our model can correctly identify 181 promoters and 174 non-promoters among 400 independent samples, which proves that the developed prediction model has excellent robustness.