The CST complex (CTC1-STN1-TEN1) has been shown to inhibit telomerase extension of the G-strand of telomeres and facilitate the switch to C-strand synthesis by DNA polymerase alpha-primase (pol α-primase). Recently the structure of human CST was solved by cryo-EM, allowing the design of mutant proteins defective in telomeric ssDNA binding and prompting the reexamination of CST inhibition of telomerase. The previous proposal that human CST inhibits telomerase by sequestration of the DNA primer was tested with a series of DNA-binding mutants of CST and modeled by a competitive binding simulation. The DNA-binding mutants had substantially reduced ability to inhibit telomerase, as predicted from their reduced affinity for telomeric DNA. These results provide strong support for the previous primer sequestration model. We then tested whether addition of CST to an ongoing processive telomerase reaction would terminate DNA extension. Pulse-chase telomerase reactions with addition of either wild-type CST or DNA-binding mutants showed that CST has no detectable ability to terminate ongoing telomerase extension in vitro. The same lack of inhibition was observed with or without pol α-primase bound to CST. These results suggest how the switch from telomerase extension to C-strand synthesis may occur.

Transcriptional systems involving discrete, stochastic events are naturally modeled using Chemical Master Equations (CMEs). These can be solved for microstate probabilities over time and state space for a better understanding of biological rates and system dynamics. However, closed form solutions to CMEs are available in only the simplest cases. Probing systems of higher complexity is challenging due to the computational cost of finding solutions and often compromises accuracy by treating infinite systems as finite. We use statistical understanding of system behavior and the generalizability of neural networks to approximate steady-state joint distribution solutions for a two-species model of the life cycle of RNA. We define a set of kernel functions using moments of the system and learn optimal weights for kernel functions with a neural network trained to minimize statistical distance between approximated and numerically calculated distributions. We show that this method of kernel weight regression (KWR) approximation is as accurate as lower-order generating-function solutions to the system, but faster; KWR approximation reduces the time for likelihood evaluation by several orders of magnitude. KWR also generalizes to produce probability predictions for system rates outside of training sets, thereby enabling efficient transcriptional parameter exploration and system analysis.

We motivate and present biVI, which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent and mature RNA distributions. In simulated benchmarking, biVI accurately recapitulates key properties of interest, including cell type structure, parameter values, and copy number distributions. In biological datasets, biVI provides a route for the identification of the biophysical mechanisms underlying differential expression. The analytical approach outlines a generalizable strategy for representing multimodal datasets generated by single-cell RNA sequencing.

The CST complex (CTC1-STN1-TEN1) has been shown to inhibit telomerase extension of the G-strand of telomeres and facilitate the switch to C-strand synthesis by DNA polymerase alpha-primase (pol α-primase). Recently the structure of human CST was solved by cryo-EM, allowing the design of mutant proteins defective in telomeric ssDNA binding and prompting the reexamination of CST inhibition of telomerase. The previous proposal that human CST inhibits telomerase by sequestration of the DNA primer was tested with a series of DNA-binding mutants of CST and modeled by a competitive binding simulation. The DNA-binding mutants had substantially reduced ability to inhibit telomerase, as predicted from their reduced affinity for telomeric DNA. These results provide strong support for the previous primer sequestration model. We then tested whether addition of CST to an ongoing processive telomerase reaction would terminate DNA extension. Pulse-chase telomerase reactions with addition of either wild-type CST or DNA-binding mutants showed that CST has no detectable ability to terminate ongoing telomerase extension in vitro. The same lack of inhibition was observed with or without pol α-primase bound to CST. These results suggest how the switch from telomerase extension to C-strand synthesis may occur.