Biology subjects

Realubit, R. B.

Publications and source records attributed to Realubit, R. B..

4 recordsLinked to original sources

bioRxiv2021

Network-based identification and pharmacological targeting of host cell master regulators induced by SARS-CoV-2 infection

Precise characterization and targeting of host cell transcriptional machinery hijacked by SARS-CoV-2 remains challenging. To identify therapeutically targetable mechanisms that are critical for SARS-CoV-2 infection, here we elucidated the Master Regulator (MR) proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2. The analysis revealed coordinated inactivation of MR-proteins linked to regulatory programs potentiating efficiency of viral replication (detrimental host MR-signature) and activation of MR-proteins governing innate immune response programs (beneficial MR-signature). To identify MR-inverting compounds capable of rescuing activity of inactivated host MR-proteins, with-out adversely affecting the beneficial MR-signature, we developed the ViroTreat algorithm. Overall, >80% of drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 infection, without affecting cell viability. ViroTreat is fully generalizable and can be extended to identify drugs targeting the host cell-based MR signatures induced by virtually any pathogen.

microbiology↗

bioRxiv2020

The Host Cell ViroCheckpoint: Identification and Pharmacologic Targeting of Novel Mechanistic Determinants of Coronavirus-Mediated Hijacked Cell States

Most antiviral agents are designed to target virus-specific proteins and mechanisms rather than the host cell proteins that are critically dysregulated following virus-mediated reprogramming of the host cell transcriptional state. To overcome these limitations, we propose that elucidation and pharmacologic targeting of host cell Master Regulator proteins--whose aberrant activities govern the reprogramed state of infected-coronavirus cells--presents unique opportunities to develop novel mechanism-based therapeutic approaches to antiviral therapy, either as monotherapy or as a complement to established treatments. Specifically, we propose that a small module of host cell Master Regulator proteins (ViroCheckpoint) is hijacked by the virus to support its efficient replication and release. Conventional methodologies are not well suited to elucidate these potentially targetable proteins. By using the VIPER network-based algorithm, we successfully interrogated 12h, 24h, and 48h signatures from Calu-3 lung adenocarcinoma cells infected with SARS-CoV, to elucidate the time-dependent reprogramming of host cells and associated Master Regulator proteins. We used the NYS CLIA-certified Darwin OncoTreat algorithm, with an existing database of RNASeq profiles following cell perturbation with 133 FDA-approved and 195 late-stage experimental compounds, to identify drugs capable of virtually abrogating the virus-induced Master Regulator signature. This approach to drug prioritization and repurposing can be trivially extended to other viral pathogens, including SARS-CoV-2, as soon as the relevant infection signature becomes available.

systems biology↗

bioRxiv2019

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

Our ability to predict the effects of drug combinations is limited, in part by limited understanding of how the transcriptional response of two monotherapies results in that of their combination. We performed the first analysis of matched time course RNAseq profiling of cells treated with both single drugs and their combinations. The transcriptional signature of the synergistic combination we studied had unique gene expression not seen in either constituent monotherapy. This can be explained mechanistically by the sequential activation of transcription factors in time in the gene regulatory network. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.84 in the prediction of synergistic drug combinations in an independent dataset.

cell biology↗

bioRxiv2019

Unbiased Assessment of H-STS cells as high-fidelity models for gastro-enteropancreatic neuroendocrine tumor drug mechanism of action analysis

Quantitative metrics to objectively assess the fidelity of cancer models, such as cell lines, organoids, or patient-derived xenografts (PDXs), remain elusive, with histological criteria or the presence of specific mutations often used as driving principles. We show that molecular criteria, based on the regulatory proteins responsible for maintaining transcriptional cell state and its regulatory network, are effective in identifying models that can recapitulate drug mechanism of action and drug sensitivity, independent of histological consideration.

cancer biology↗