RNA Replication

We extend the biphasic model by including the dynamics of intracellular viral RNA (vRNA). Let be the quantity of intracellular genomic (i.e. positive-strand) vRNA present in an infected cell. The dynamics of depend on the tradeoff between vRNA production and loss due to degradation and assembly/secretion as virions and can be described by the following equation where is the age of infection, i.e., the time that has elapsed since an HCV virion has entered the cell. The parameters , and are the age-dependent rates of vRNA production, degradation and assembly/secretion, respectively. For simplicity we do not distinguish between vRNA being packaged into a virion and the virion being secreted. Once vRNA is packaged it is no longer available for replication or degradation, and we assume the packaged virion is secreted. A more complex model would distinguish these processes but at the expense of additional parameters. We assume that a cell is infected by a single virion and hence there is only one vRNA in an infected cell at age 0, i.e., . A model similar to Eq. (3) but with constant parameters has been successfully used to fit intracellular vRNA levels in an in vitro replicon system [19] (link), giving us confidence that a simple model can capture many of the major events in vRNA replication. More complex models exist (e.g., Dahari et al. [20] (link) in which the model has 9 equations and 18 parameters) but because they involve many parameters whose values are not known as well as many other intracellular molecules they are not well suited for our purpose here of understanding the major effects of PI therapy.
Combining the equations governing the vRNA kinetics and the cell infection dynamics given by Eq. (1), an age-structured multiscale model of HCV kinetics results that can be described by the following partial differential equations (PDE): The initial distributions of infected cells and intracellular vRNAs are assumed to be and , respectively. If is chosen to be the time of initial infection, then . The equation would become an ODE (Eq. 3) if were the steady state distribution since no further time evolution would occur.
Unlike the standard biphasic model, three different antiviral effects of therapy with DAAs can be distinguished in the multiscale model, namely blocking vRNA production (i.e., reducing by a factor ), reducing assembly/secretion of virus (i.e., reducing by a factor ), and enhancing the rate of vRNA degradation (i.e., increasing by a factor ), where and are the effectivenesses of therapy in affecting different processes in the viral life cycle. The full model combining both intra and extracellular viral kinetics under therapy is where is the time at which treatment is initiated, and are the steady state distribution of infected cells and intracellular vRNAs, respectively, before therapy, which will be calculated in Results. An additional potential antiviral effect of the therapy on intracellular replication templates will be incorporated into the model later (see the subsection of Long-term Approximation).

The molecular docking simulation was used to determine the binding energy of six antiretrovirals to the RdRp, ExoN-NSP10 and 3CLpro proteins of SARS-CoV-2. These proteins are necessary for viral RNA replication and polyprotein processing [12] (link). The crystal structures of RdRp (Identification code, ID: 6M71) [8] (link), ExoN-NSP10 (ID:7MC6) [37] (link) and 3CLpro (ID: 6M2N) [38] (link) were obtained from the Protein Data Bank (PDB) [39] (link). The resolution structures selected were lower than 3 Å [40] (link). The proteins were subjected to preparation by using Discovery Studio [41] and AutoDockTools (ADT). The active forms of the antiretrovirals [42] (link) were drawn and optimized by using Avogadro software [43] (link) and ADT. Remdesivir [44] (link),[45] (link), pibrentasvir [46] (link) and CQ [47] (link),[48] (link) were used as positive controls of the interaction with RdRp, ExoN-NSP10 and 3CLpro, respectively.
PrankWeb [49] (link) was used to determine the number of pockets and the amino acid residues that comprise them. This program also described the size (volume), depth, surface area or general hydrophobicity of each pocket (Table 1). In addition, Protein plus [50] (link) was implemented to verify the number of pockets obtained by PrankWeb [49] (link), and to describe their characteristics (size, shapes, amino acids composition and descriptor functional groups). The pockets were selected according to the active site or catalytic domain for each protein, as based on previous reports [16] (link),[51] (link),[52] (link).
Couplings were carried out using AutoDock Vina version 4.2.6 [53] (link), with an exhaustiveness value of 20 and a grid box of 24 Å × 24 Å × 24 Å, centered at (116.7829 Å, 109.9570 Å, 123.9430 Å) (XYZ coordinates) for RdRp (PDB ID: 6M71), (28.6904 Å, −1.9647 Å, 13.6836 Å) for ExoN-NSP10 (PDB: 7MC6) and (−47.585 Å, 1.135 Å, −5.600 Å) for 3CLpro (PDB ID:6M2N) (Table 1). The best docking conformation of protein-ligand interactions was predicted based on the binding energy value (kcal/mol). The docked structures were analyzed and visualized by using BIOVIA Discovery Studio Visualizer 16.1.

Ch25h^fl/fl and Ch25h^ECKO female mice were injected with tamoxifen. Nine brains per genotype were pooled and primary brain microvascular endothelial cells (pMBMEC) were isolated and plated in a 96‐well plate (2 wells/brain). Confluent pMBMEC were left unstimulated or stimulated IL‐1β for 24 h. RNA of three wells was pooled to obtain one replicate for RNA sequencing. The Lausanne Genomic Technologies Facility performed the RNA‐seq. RNA quality was assessed on a Fragment Analyzer (Agilent Technologies), and all RNAs had a RQN between 8.7 and 10. RNA‐seq libraries were prepared from 500 ng of total RNA with the Illumina TruSeq Stranded mRNA reagents (Illumina) using a unique dual indexing strategy, and following the official protocol automated on a Sciclone liquid handling robot (PerkinElmer). Libraries were quantified by a fluorometric method (QubIT, Life Technologies) and their quality assessed on a Fragment Analyzer (Agilent Technologies).
Cluster generation was performed with 2 nM of an equimolar pool from the resulting libraries using the Illumina HiSeq 3000/4000 SR Cluster Kit reagents and sequenced on the Illumina HiSeq 4000 using HiSeq 3000/4000 SBS Kit reagents for 150 cycles (single end). Sequencing data were demultiplexed using the bcl2fastq2 Conversion Software (version 2.20, Illumina).