SummaryNumerous non-coding RNAs are known to be involved in the regulation of gene expression. In this work, we analyzed RNAs that coimmunoprecipitated with human RNA polymerase II from mitotic cell extracts and identified U1 small nuclear RNA (snRNA) as a major species. To investigate a possible splicing-independent recruitment of U1 snRNA to transcription units, we established cell lines having integrated a reporter gene containing a functional intron or a splicing-deficient construction. Recruitment of U snRNAs and some splicing factors to transcription sites was evaluated using fluorescence in situ hybridization (FISH) and immunofluorescence. To analyze imaging data, we developed a quantitative procedure, 'radial analysis', based on averaging data from multiple fluorescence images. The major splicing snRNAs (U2, U4 and U6 snRNAs) as well as the U2AF65 and SC35 splicing factors were found to be recruited only to transcription units containing a functional intron. By contrast, U1 snRNA, the U1-70K (also known as snRNP70) U1-associated protein as well as the ASF/SF2 (also known as SFRS1) serine/arginine-rich (SR) protein were efficiently recruited both to normally spliced and splicing-deficient transcription units. The constitutive association of U1 small nuclear ribonucleoprotein (snRNP) with the transcription machinery might play a role in coupling transcription with pre-mRNA maturation.

During the past decade, numerous ncRNAs (non-coding RNAs) have been identified as regulators of transcription. This review focuses on a few examples of ncRNAs that directly interact with and regulate components of the transcription machinery. Artificial RNA aptamers have been selected against components of the transcriptional machinery. The bacterial 6S RNA and the eukaryotic B2 RNA directly target RNA polymerases. The 7SK RNA, U1 snRNA (small nuclear RNA) and SRA (steroid receptor RNA activator) RNA bind to and regulate the activity of transcription factors. Xist (X-inactive-specific transcript) and roX (RNA on the X) RNAs are involved in epigenetic regulation of transcription through the recruitment of histone-modifying enzymes.

Sensorimotor (SMR) neurofeedback is a promising therapy for several health disorders but is still not widely used due to the high cost of the equipment. URGOnight offers a low-cost solution to democratize these therapies by providing an at-home EEG headband with dry electrodes connected to a mobile application. The first aim of this study is both to validate the URGOnight EEG signal and to compare it to Enobio-20, a medical grade EEG device. The second aim of the study is to propose a new method to detect SMR rhythm based on its oscillatory properties and discriminate it from alpha oscillations. In our study, we compared the URGOnight headband EEG signal (C3/C4) to Enobio- 20 (CP3/CP4), placed on subjects simultaneously equipped with the two headbands. All subjects (n=33) performed a dual blocking task inspired by Kulhman (1978) based on the blocking effect of movement and eyes opening on SMR and alpha respectively. This task was followed by SSVEP stimulations to evaluate the frequency response of the two EEG devices. The performance of the EEG headbands was statistically identical for most of the characteristics of the EEG signal, including the frequency response to SSVEP (from 4Hz to 20Hz). The main difference was a larger amplitude in the 8-15Hz due to the location of the reference in URGOnight that did not impair the detection of both alpha and SMR. In addition, we show that our new method allows to discriminate alpha and SMR rhythms based on their oscillatory properties with a single recording site (C3/C4). The method is fast enough to be used in real time. We show that the detected SMR rhythm is modulated by movement as opposed to the 12-15Hz frequency band often used as indicator of SMR in most neurofeedback studies. Altogether, our results validate the quality of the EEG recordings obtained with URGOnight since it gives similar results as the one obtained with Enobio-20, a validated EEG medical grade system. In addition, we provide a new method allowing the identification and the separation of the alpha and SMR with a single recording site C3/C4. This method opens up a new research lead to improve SMR neurofeedback efficiency and thus of its clinical possibilities by focusing on the reinforcement of the SMR oscillation strictly speaking.