Palacio, M. | bioRxiv Science

bioRxiv2025

Real-time visualization of reconstituted transcription reveals RNA polymerase II activation mechanisms at single promoters

RNA polymerase II (RNAPII) is regulated by sequence-specific transcription factors (TFs) and the pre-initiation complex (PIC): TFIIA, TFIIB, TFIID, TFIIE, TFIIF, TFIIH, Mediator. TFs and Mediator contain intrinsically-disordered regions (IDRs) and form phase-separated condensates, but how IDRs control RNAPII function remains poorly understood. Using purified PIC factors, we developed a Real-time In-vitro Fluorescence Transcription assay (RIFT) for second-by-second visualization of RNAPII transcription at hundreds of promoters simultaneously. We show rapid RNAPII activation is IDR-dependent, without condensate formation. For example, the MED1-IDR can functionally replace a native TF, activating RNAPII with similar (not identical) kinetics; however, MED1-IDR squelches transcription as a condensate, but activates as a single-protein. TFs and Mediator cooperatively activate RNAPII bursting and re-initiation and surprisingly, Mediator can drive TF-promoter recruitment, without TF-DNA binding. Collectively, RIFT addressed questions largely intractable with cell-based methods, yielding mechanistic insights about IDRs, condensates, enhancer-promoter communication, and RNAPII bursting that complement live-cell imaging data.

biochemistry↗

bioRxiv2021

Optimizing Polymerase Chain Reaction (PCR) using Machine Learning

Despite substantial standardization, polymerase chain reaction (PCR) experiments frequently fail. Troubleshooting failed PCRs can be costly in both time and money. Using a crowdsourced data set spanning 290 real PCRs from six active research laboratories, we investigate the degree to which PCR success rates can be improved by machine learning. While human designed PCRs succeed at a rate of 55-63%, we find that a machine learning model can accurately predict reaction outcome 81% of the time. We validate this level of improvement by then using the model to guide the design and predict the outcome of 39 new PCR experiments. In addition to improving outcomes, the model identifies 15 features of PCRs that researchers did not optimize well compared to the learned model. These results suggest that PCR success rates can easily be improved by 17-26%, potentially saving millions of dollars and thousands of hours of researcher time each year across the scientific community. Other common laboratory methods may benefit from similar data-driven optimization effort.

bioinformatics↗