Target-agnostic discovery of Rett Syndrome therapeutics by coupling computational network analysis and CRISPR-enabled in vivo disease modeling

Abstract

It is difficult to develop effective treatments for neurodevelopmental genetic disorders, such as Rett syndrome, which are caused by a single gene mutation but trigger changes in numerous other genes, and thereby also severely impair functions of organs beyond the central nervous system (CNS). This challenge is further complicated by the lack of sufficiently broad and biologically relevant drug screens, and the inherent complexity in identifying clinically relevant targets responsible for diverse phenotypes. Here, we combined human gene regulatory network-based computational drug prediction with in vivo screening in a population-level diversity, CRISPR-edited, Xenopus laevis tadpole model of Rett syndrome to carry out target-agnostic drug discovery, which rapidly led to the identification of the FDA-approved drug vorinostat as a potential repurposing candidate. Vorinostat broadly improved both CNS and non-CNS (e.g., gastrointestinal, respiratory, inflammatory) abnormalities in a pre-clinical mouse model of Rett syndrome. This is the first Rett syndrome treatment to demonstrate pre-clinical efficacy across multiple organ systems when dosed after the onset of symptoms, and network analysis revealed a putative therapeutic mechanism for its cross-organ normalizing effects based on its impact on acetylation metabolism and post-translational modifications of microtubules.

Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients

“…The NeMoCAD gene network analysis tool 16 was used to identify FDA-approved drugs predicted to normalize the COVID-19 gene expression profile based on transcriptomic signatures of human cells or organoids infected with SARS-CoV-2 as well as cells or tissues obtained from COVID-19 patients or healthy control subjects. NeMoCAD identified gene changes across the transcriptome, compared them with gene expression changes induced by approved drugs in existing databases (e.g., LINCS, KEGG, TRRUST, CTD), and then prioritized compounds based on their ability to shift the disease transcriptomic signature state back to a healthy state ( Figure 1A ).…”

“…12 NeMoCAD is a drug repurposing algorithm that performs correlation analysis of transcriptional gene signatures and a Bayesian statistical analysis of a network comprised of drug-gene and drug-drug interactions to identify compounds capable of changing a transcriptional signature indicative of disease to a healthy state. 12 Using 14 publicly available transcriptomic datasets derived from human patients, tissue samples, organoids, and cells (Table 1), [14][15][16][17][18][19][20] NeMoCAD identified transcriptome-wide differential expression profiles between the control and COVID-19 states for each dataset and defined a target normalization signature to mimic, which would shift the transcriptome from a COVID-19 disease to control state (Supplemental Methods). To understand underlying differences in LINCS drug-gene probability signatures that could influence drug predictions, drugs were compared by principal component analysis using the packages ggfortify and ggplot2 (R version 4.0.5).…”

“…The drug prediction software, NeMoCAD (Network Modeling for Causal Discovery), was used to predict compounds that would mimic the shift from a COVID-19-positive state to a control state. 16 NeMoCAD is a drug repurposing algorithm that performs correlation analysis of transcriptional gene signatures and a Bayesian statistical analysis of a network comprised of drug-gene and drug-drug interactions to identify compounds capable of changing a transcriptional signature indicative of disease to a healthy state. 16 Using 14 publicly available transcriptomic datasets derived from human patients, tissue samples, organoids, and cells (Table 1), [19][20][21][22][23][24][25] NeMoCAD identified transcriptome-wide differential expression profiles between the control and COVID-19 states for each dataset and defined a target normalization signature to mimic, which would shift the transcriptome from a COVID-19 disease to control state (Supplemental Methods).…”

“…With drug repurposing in mind, we used a Network Model for Causality-Aware Discovery (NeMoCAD) computational tool based on Bayesian statistical network modeling 16 to analyze transcriptomics signatures in tissue samples obtained from COVID-19 positive patients or SARS-CoV-2 infected human cell or organoid cultures to identify FDA-approved drugs that shift the host transcriptomic response to SARS-CoV-2 towards a healthy state ( Figure 1A ). This approach was agnostic as it was accomplished without an a priori defined drug target or mechanism of action.…”

“…A related approach has previously been used to identify repurposed drugs for coronary artery disease by integration of protein-protein interaction network proximity and large-scale patient-level longitudinal data. 15 With drug repurposing in mind, we used a Network Model for Causality-Aware Discovery (NeMoCAD) computational tool based on Bayesian statistical network modeling 16 to analyze transcriptomics signatures in tissue samples obtained from COVID-19 positive patients or SARS-CoV-2 infected human cell or organoid cultures to identify FDA-approved drugs that shift the host transcriptomic response to SARS-CoV-2 towards a healthy state (Figure 1A). This approach was agnostic as it was accomplished without an a priori defined drug target or mechanism of action.…”