The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
We examined the vulnerability to excitotoxicity of rat oligodendrocytes in dissociated cell culture at different developmental stages. Mature oligodendrocytes that express myelin basic protein were resistant to excitotoxic injury produced by kainate, whereas earlier stages in the oligodendrocyte lineage were vulnerable to this insult. To test the hypothesis that the sensitivity of immature oligodendrocytes and the resistance of mature oligodendrocytes to kainate toxicity were due to differences in membrane responsiveness to kainate, we used whole-cell patch-clamp recording. Oligodendrocyte precursors in cultures vulnerable to kainate toxicity responded to 500 microM kainate with large inward currents, whereas mature myelin basic protein-expressing oligodendrocytes in cultures resistant to kainate toxicity showed no clear response to application of this agonist. We assayed expression of glutamate receptor subunits (GluR) -2, -4, -6, -7, and KA2 using immunoblot analysis and found that expression of all of these glutamate receptors was significantly down-regulated in mature oligodendrocytes. These results suggest a striking developmental regulation of glutamate receptors in oligodendrocytes and suggest that the vulnerability of oligodendrocytes to non- N-methyl-D-aspartate receptor-mediated excitotoxicity might be much greater in developing oligodendrocytes than after the completion of myelination.
We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-standing open question of how attractor count in critical random Boolean networks scales with network size and whether the scaling matches biological observations. Via 80-fold improvement in probed network size (N = 16,384), we find the unexpectedly low scaling exponent of 0.12 ± 0.05, approximately one-tenth the analytical upper bound. We demonstrate a general principle: A system’s relationship to its time reversal and state-space inversion constrains its repertoire of emergent behaviors.
Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.
BackgroundAnalyzing the long-term behaviors (attractors) of dynamic models of biological systems can provide valuable insight into biological phenotypes and their stability. In this paper we identify the allowed long-term behaviors of a multi-level, 70-node dynamic model of the stomatal opening process in plants.ResultsWe start by reducing the model’s huge state space. We first reduce unregulated nodes and simple mediator nodes, then simplify the regulatory functions of selected nodes while keeping the model consistent with experimental observations. We perform attractor analysis on the resulting 32-node reduced model by two methods: 1. converting it into a Boolean model, then applying two attractor-finding algorithms; 2. theoretical analysis of the regulatory functions. We further demonstrate the robustness of signal propagation by showing that a large percentage of single-node knockouts does not affect the stomatal opening level.ConclusionsCombining both methods with analysis of perturbation scenarios, we conclude that all nodes except two in the reduced model have a single attractor; and only two nodes can admit oscillations. The multistability or oscillations of these four nodes do not affect the stomatal opening level in any situation. This conclusion applies to the original model as well in all the biologically meaningful cases. In addition, the stomatal opening level is resilient against single-node knockouts. Thus, we conclude that the complex structure of this signal transduction network provides multiple information propagation pathways while not allowing extensive multistability or oscillations, resulting in robust signal propagation. Our innovative combination of methods offers a promising way to analyze multi-level models.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0327-7) contains supplementary material, which is available to authorized users.
The SARS-CoV-2 pandemic has caused widespread illness, loss of life, and socioeconomic disruption that is unlikely to resolve until vaccines are widely adopted, and effective therapeutic treatments become established. Here, a well curated and annotated library of 6710 clinical and preclinical molecules, covering diverse chemical scaffolds and known host targets was evaluated for inhibition of SARS-CoV-2 infection in multiple infection models. Multi-concentration, high-content immunocytofluorescence-based screening identified 172 strongly active small molecules, including 52 with submicromolar potencies. The active molecules were extensively triaged by in vitro mechanistic assays, including human primary cell models of infection and the most promising, obatoclax, was tested for in vivo efficacy. Structural and mechanistic classification of compounds revealed known and novel chemotypes and potential host targets involved in each step of the virus replication cycle including BET proteins, microtubule function, mTOR, ER kinases, protein synthesis and ion channel function. In the mouse disease model obatoclax effectively reduced lung virus load by 10-fold. Overall, this work provides an important, publicly accessible, foundation for development of novel treatments for COVID-19, establishes human primary cell-based pharmacological models for evaluation of therapeutics and identifies new insights into SARS-CoV-2 infection mechanisms.
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