S-adenosylmethionine-dependent methyltransferase inhibitor DZNep blocks transcription and translation of SARS-CoV-2 genome with a low tendency to select for drug-resistant viral variants.

We report the in vitro antiviral activity of DZNep (3-Deazaneplanocin A; an inhibitor of S-adenosylmethionine-dependent methyltransferase) against SARS-CoV-2, besides demonstrating its protective efficacy against lethal infection of infectious bronchitis virus (IBV, a member of the Coronaviridae family). DZNep treatment resulted in reduced synthesis of SARS-CoV-2 RNA and proteins without affecting other steps of viral life cycle. We demonstrated that deposition of N6-methyl adenosine (m6A) in SARS-CoV-2 RNA in the infected cells recruits heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), an RNA binding protein which serves as a m6A reader. DZNep inhibited the recruitment of hnRNPA1 at m6A-modified SARS-CoV-2 RNA which eventually suppressed the synthesis of the viral genome. In addition, m6A-marked RNA and hnRNPA1 interaction was also shown to regulate early translation to replication switch of SARS-CoV-2 genome. Furthermore, abrogation of methylation by DZNep also resulted in defective synthesis of the 5' cap of viral RNA, thereby resulting in its failure to interact with eIF4E (a cap-binding protein), eventually leading to a decreased synthesis of viral proteins. Most importantly, DZNep-resistant mutants could not be observed upon long-term sequential passage of SARS-CoV-2 in cell culture. In summary, we report the novel role of methylation in the life cycle of SARS-CoV-2 and propose that targeting the methylome using DZNep could be of significant therapeutic value against SARS-CoV-2 infection.

Finite volume and asymptotic methods for stochastic neuron models with correlated inputs.

We consider a pair of stochastic integrate and fire neurons receiving correlated stochastic inputs. The evolution of this system can be described by the corresponding Fokker-Planck equation with non-trivial boundary conditions resulting from the refractory period and firing threshold. We propose a finite volume method that is orders of magnitude faster than the Monte Carlo methods traditionally used to model such systems. The resulting numerical approximations are proved to be accurate, nonnegative and integrate to 1. We also approximate the transient evolution of the system using an Ornstein-Uhlenbeck process, and use the result to examine the properties of the joint output of cell pairs. The results suggests that the joint output of a cell pair is most sensitive to changes in input variance, and less sensitive to changes in input mean and correlation.

A finite volume method for stochastic integrate-and-fire models.

The stochastic integrate and fire neuron is one of the most commonly used stochastic models in neuroscience. Although some cases are analytically tractable, a full analysis typically calls for numerical simulations. We present a fast and accurate finite volume method to approximate the solution of the associated Fokker-Planck equation. The discretization of the boundary conditions offers a particular challenge, as standard operator splitting approaches cannot be applied without modification. We demonstrate the method using stationary and time dependent inputs, and compare them with Monte Carlo simulations. Such simulations are relatively easy to implement, but can suffer from convergence difficulties and long run times. In comparison, our method offers improved accuracy, and decreases computation times by several orders of magnitude. The method can easily be extended to two and three dimensional Fokker-Planck equations.