Chemically induced accumulation of GAGs delays PrP(Sc) clearance but prolongs prion disease incubation time

Adaptation of the protein misfolding cyclic amplification (PMCA) technique for the screening of anti-prion compounds

Prion diseases result from the misfolding of the physiological prion protein (PrPC) to a pathogenic conformation (PrPSc). Compelling evidence indicates that prevention and/or reduction of PrPSc replication are promising therapeutic strategies against prion diseases. However, the existence of different PrPSc conformations (or strains) associated with disease represents a major problem when identifying anti-prion compounds. Efforts to identify strain-specific anti-prion molecules are limited by the lack of biologically relevant high-throughput screening platforms to interrogate compound libraries. Here, we describe adaptations to the protein misfolding cyclic amplification (PMCA) technology (able to faithfully replicate PrPSc strains) that increase its throughput to facilitate the screening of anti-prion molecules. The optimized PMCA platform includes a reduction in sample and reagents, as well as incubation/sonication cycles required to efficiently replicate and detect rodent-adapted and cervid PrPSc strains. The visualization of PMCA products was performed via dot blots, a method that contributed to reduced processing times. These technical changes allowed us to evaluate small molecules with previously reported anti-prion activity. This proof-of-principle screening was evaluated for six rodent-adapted prion strains. Our data show that these compounds targeted either none, all or some PrPSc strains at variable concentrations, demonstrating that this PMCA system is suitable to test compound libraries for putative anti-prion molecules targeting specific PrPSc strains. Further analyses of a small compound library against deer prions demonstrate the potential of this new PMCA format to identify strain-specific anti-prion molecules. The data presented here demonstrate the use of the PMCA technique in the selection of prion strain-specific anti-prion compounds.

Full-length prion protein incorporated into prion aggregates is a marker for prion strain-specific destabilization of aggregate structure following cellular uptake

Accumulation of insoluble aggregates of infectious, partially protease-resistant prion protein (PrPD) generated via the misfolding of protease sensitive prion protein (PrPC) into the same infectious conformer, is a hallmark of prion diseases. Aggregated PrPD is taken up and degraded by cells, a process likely involving changes in aggregate structure that can be monitored by accessibility of the N-terminus of full-length PrPD to cellular proteases. We therefore tracked the protease sensitivity of full-length PrPD before and after cellular uptake for two murine prion strains, 22L and 87V. For both strains, PrPD aggregates were less stable following cellular uptake with increased accessibility of the N-terminus to cellular proteases across most aggregate sizes. However, a limited size range of aggregates was able to better protect the N-termini of full-length PrPD, with the N-terminus of 22L-derived PrPD more protected than that of 87V. Interestingly, changes in aggregate structure were associated with minimal changes to the protease-resistant core of PrPD. Our data show that cells destabilize the aggregate quaternary structure protecting PrPD from proteases in a strain-dependent manner, with structural changes exposing protease sensitive PrPD having little effect on the protease-resistant core, and thus conformation, of aggregated PrPD.

Cell biology of prion strains in vivo and in vitro

The properties of infectious prions and the pathology of the diseases they cause are dependent upon the unique conformation of each prion strain. How the pathology of prion disease correlates with different strains and genetic backgrounds has been investigated via in vivo assays, but how interactions between specific prion strains and cell types contribute to the pathology of prion disease has been dissected more effectively using in vitro cell lines. Observations made through in vivo and in vitro assays have informed each other with regard to not only how genetic variation influences prion properties, but also how infectious prions are taken up by cells, modified by cellular processes and propagated, and the cellular components they rely on for persistent infection. These studies suggest that persistent cellular infection results from a balance between prion propagation and degradation. This balance may be shifted depending upon how different cell lines process infectious prions, potentially altering prion stability, and how fast they can be transported to the lysosome. Thus, in vitro studies have given us a deeper understanding of the interactions between different prions and cell types and how they may influence prion disease phenotypes in vivo.

Cell biology of prion infection

The development of multiple cell culture models of prion infection over the last two decades has led to a significant increase in our understanding of how prions infect cells. In particular, new techniques to distinguish exogenous from endogenous prions have allowed us for the first time to look in depth at the earliest stages of prion infection through to the establishment of persistent infection. These studies have shown that prions can infect multiple cell types, both neuronal and nonneuronal. Once in contact with the cell, they are rapidly taken up via multiple endocytic pathways. After uptake, the initial replication of prions occurs almost immediately on the plasma membrane and within multiple endocytic compartments. Following this acute stage of prion replication, persistent prion infection may or may not be established. Establishment of a persistent prion infection in cells appears to depend upon the achievement of a delicate balance between the rate of prion replication and degradation, the rate of cell division, and the efficiency of prion spread from cell to cell. Overall, cell culture models have shown that prion infection of the cell is a complex and variable process which can involve multiple cellular pathways and compartments even within a single cell.

Efficacy of Tilorone Dihydrochloride against Ebola Virus Infection

Tilorone dihydrochloride (tilorone) is a small-molecule, orally bioavailable drug that is used clinically as an antiviral outside the United States. A machine-learning model trained on anti-Ebola virus (EBOV) screening data previously identified tilorone as a potent in vitro EBOV inhibitor, making it a candidate for the treatment of Ebola virus disease (EVD). In the present study, a series of in vitro ADMET (absorption, distribution, metabolism, excretion, toxicity) assays demonstrated the drug has excellent solubility, high Caco-2 permeability, was not a P-glycoprotein substrate, and had no inhibitory activity against five human CYP450 enzymes (3A4, 2D6, 2C19, 2C9, and 1A2). Tilorone was shown to have 52% human plasma protein binding with excellent plasma stability and a mouse liver microsome half-life of 48 min. Dose range-finding studies in mice demonstrated a maximum tolerated single dose of 100 mg/kg of body weight. A pharmacokinetics study in mice at 2- and 10-mg/kg dose levels showed that the drug is rapidly absorbed, has dose-dependent increases in maximum concentration of unbound drug in plasma and areas under the concentration-time curve, and has a half-life of approximately 18 h in both males and females, although the exposure was ∼2.5-fold higher in male mice. Tilorone doses of 25 and 50 mg/kg proved efficacious in protecting 90% of mice from a lethal challenge with mouse-adapted with once-daily intraperitoneal (i.p.) dosing for 8 days. A subsequent study showed that 30 mg/kg/day of tilorone given i.p. starting 2 or 24 h postchallenge and continuing through day 7 postinfection was fully protective, indicating promising activity for the treatment of EVD.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC ₅₀ values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC ₅₀ values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Bone morphogenetic protein-inducer tilorone identified by high-throughput screening is antifibrotic in vivo

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with a poor prognosis and very few therapeutic options. On the molecular level, patients with IPF have increased amounts of the bone morphogenetic protein (BMP) inhibitor gremlin in their lungs, which results in decreased BMP signaling, and an increase in transforming growth factor-β signaling. Based on these findings, we hypothesized that restoration of the impaired BMP signaling would offer a novel strategy for the prevention of fibrosis progression or for the treatment of pulmonary fibrosis. We used reporter cell lines and high-throughput screening of a chemical compound library as an approach to finding molecules that increase BMP signaling in lung epithelial cells, without increasing transforming growth factor-β signaling. The most promising candidate drug was analyzed further by studying its effects on BMP target gene expression, Smad protein phosphorylation, and a mouse model of silica-induced pulmonary fibrosis. The most promising drug candidate, tilorone, induced BMP signaling in the reporter cells and increased the expression of BMP-7 and a BMP target gene, Id3, in lung epithelial A549 cells. In a mouse model of pulmonary fibrosis, tilorone decreased lung hydroxyproline content and the expression of collagen genes Col1A1 and Col3A1. Mice treated with tilorone showed markedly decreased histological changes, compared with untreated mice. These findings indicate that tilorone has biologically significant antifibrotic properties.

Glycosaminoglycans and neuroprotection

Glycosaminoglycans (GAGs) are basic building blocks of the ground substance of the extracellular matrix and present at the cellular level as an important component of the glycocalyx covering the cell membrane. In addition to the general role of GAGs in maintaining the integrity of the cell and extracellular matrix by retaining water, certain GAGs exhibit anticoagulant and neuroprotective properties and serve as cell-surface receptors for various molecules. Although heparin, a highly sulfated GAG, has been used as a drug for more than 70 years due to its anticoagulant attributes, the neuroprotective properties of GAGs came into focus only in recent years. The discovery of some of the roles GAGs play in the pathomechanism of numerous neurodegenerative disorders as well as shedding light on the neuroprotective properties of these compounds in animal studies raised the possibility that GAGs may provide an entirely new avenue in the treatment of neurodegenerative diseases. Indeed, some GAGs were successfully used to improve the cognitive function of patients with various neurodegenerative conditions (Ban et al. (1991, 1992); Conti et al. (1989a, b); Passeri and Cucinotta, (1989); Santini (1989). Although the mechanism by which the GAGs exhibit neuroprotective properties is not entirely clear, there is a general consensus that the major factors of the neuroprotective attributes of GAGs include the impact of GAGs on amyloidogenesis and the regulatory action of GAGs in the apoptotic pathway.

A systems approach to prion disease

Prions cause transmissible neurodegenerative diseases and replicate by conformational conversion of normal benign forms of prion protein (PrP^C) to disease-causing PrP^Sc isoforms. A systems approach to disease postulates that disease arises from perturbation of biological networks in the relevant organ. We tracked global gene expression in the brains of eight distinct mouse strain–prion strain combinations throughout the progression of the disease to capture the effects of prion strain, host genetics, and PrP concentration on disease incubation time. Subtractive analyses exploiting various aspects of prion biology and infection identified a core of 333 differentially expressed genes (DEGs) that appeared central to prion disease. DEGs were mapped into functional pathways and networks reflecting defined neuropathological events and PrP^Sc replication and accumulation, enabling the identification of novel modules and modules that may be involved in genetic effects on incubation time and in prion strain specificity. Our systems analysis provides a comprehensive basis for developing models for prion replication and disease, and suggests some possible therapeutic approaches.