Demonstration Study: A Protocol to Combine Online Tools and Databases for Identifying Potentially Repurposable Drugs

Chaparala

Ganapathiraju

2019

Sci Rep

We previously presented the protein-protein interaction network of schizophrenia associated genes, and from it, the drug-protein interactome which showed the drugs that target any of the proteins in the interactome. Here, we studied these drugs further to identify whether any of them may potentially be repurposable for schizophrenia. In schizophrenia, gene expression has been described as a measurable aspect of the disease reflecting the action of risk genes. We studied each of the drugs from the interactome using the BaseSpace Correlation Engine, and shortlisted those that had a negative correlation with differential gene expression of schizophrenia. This analysis resulted in 12 drugs whose differential gene expression (drug versus normal) had an anti-correlation with differential expression for schizophrenia (disorder versus normal). Some of these drugs were already being tested for their clinical activity in schizophrenia and other neuropsychiatric disorders. Several proteins in the protein interactome of the targets of several of these drugs were associated with various neuropsychiatric disorders. The network of genes with opposite drug-induced versus schizophrenia-associated expression profiles were significantly enriched in pathways relevant to schizophrenia etiology and GWAS genes associated with traits or diseases that had a pathophysiological overlap with schizophrenia. Drugs that targeted the same genes as the shortlisted drugs, have also demonstrated clinical activity in schizophrenia and other related disorders. This integrated computational analysis will help translate insights from the schizophrenia drug-protein interactome to clinical research - an important step, especially in the field of psychiatric drug development which faces a high failure rate.

Section: Resultsmentioning

confidence: 99%

Potentially repurposable drugs for schizophrenia identified from its interactome

Chaparala

Ganapathiraju

2019

Sci Rep

“…There are 513 unique drugs that target 206 of these proteins (of which 28 are novel interactors that are targeted by 147 drugs) ( Figure 5 and Data File S4 ). We adopted the established approach of comparing drug-induced versus disease-associated differential expression using the BaseSpace correlation software (previously called NextBio) [ 31 , 32 ], to identify five drugs that could be potentially repurposable for MPM ( Table 3 ; the table also shows corresponding information for two known MPM drugs). These are: cabazitaxel , used in the treatment of refractory prostate cancer; primaquine and pyrimethamine , two anti-parasitic drugs; trimethoprim , an antibiotic; and gliclazide , an anti-diabetic drug (See Appendix A , titled ‘Repurposable Drugs for Treatment of Malignant Pleural Mesothelioma’).…”

Section: Resultsmentioning

confidence: 99%

Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions

Yanamala

Boyce

et al. 2021

Cancers

Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the outer lining of the lung, with a median survival of less than one year. We constructed an ‘MPM interactome’ with over 300 computationally predicted protein-protein interactions (PPIs) and over 2400 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from Biological General Repository for Interaction Datasets (BioGRID) and Human Protein Reference Database (HPRD). Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. We validated five novel predicted PPIs experimentally. The interactome is significantly enriched with genes differentially ex-pressed in MPM tumors compared with normal pleura and with other thoracic tumors, genes whose high expression has been correlated with unfavorable prognosis in lung cancer, genes differentially expressed on crocidolite exposure, and exosome-derived proteins identified from malignant mesothelioma cell lines. 28 of the interactors of MPM proteins are targets of 147 U.S. Food and Drug Administration (FDA)-approved drugs. By comparing disease-associated versus drug-induced differential expression profiles, we identified five potentially repurposable drugs, namely cabazitaxel, primaquine, pyrimethamine, trimethoprim and gliclazide. Preclinical studies may be con-ducted in vitro to validate these computational results. Interactome analysis of disease-associated genes is a powerful approach with high translational impact. It shows how MPM-associated genes identified by various high throughput studies are functionally linked, leading to clinically translatable results such as repurposed drugs. The PPIs are made available on a webserver with interactive user interface, visualization and advanced search capabilities.

“…We compared drug-induced versus SARS-associated differential expression using the BaseSpace Correlation Engine (previously called NextBio) ( https://www.nextbio.com ), 16 , 17 to identify drugs for nCoV19. We compiled a list of 933 chemical compounds whose differential gene expression profile (drug versus no drug) were negatively correlated with at least one of the four SARS differential gene expression datasets (infected versus non-infected); the 4 SARS datasets we studied were: Calu–3 epithelial cells infected for 48 hours with SARS coronavirus versus mock infected cells (GSE17400), Calu–3 lung cells infected for 72 hours with SARS CoV Urbani versus mock infected cells (GSE37827), lung fibroblast MRC5 cells 24 hours post SARS coronavirus infection, high multiplicity of infection MOI versus mock infection (GSE56189) and peripheral blood mononuclear cells (PBMCs) from patients with SARS versus healthy subjects (GSE1739 18 ).…”

Section: Resultsmentioning

confidence: 99%

“…The list of chemical compounds whose gene expression profiles correlated negatively with four SARS datatsets and one COVID–19 dataset were compiled using the BaseSpace correlation software ( https://www.nextbio.com ) following the protocol described in prior work 17 (List 1). The datasets considered were human bronchial epithelial (NHBE) and lung cancer (A549) cells infected with the SARS-CoV–2 strain USA-WA1/2020 (GSE147507 19 ), Calu–3 epithelial cells infected for 48 hours with SARS coronavirus versus mock infected cells (GSE17400), Calu–3 lung cells infected for 72 hours with SARS CoV Urbani versus mock infected cells (GSE37827), lung fibroblast MRC5 cells 24 hours post SARS coronavirus infection, high MOI (3) versus mock infection (GSE56189) and peripheral blood mononuclear cells (PBMCs) from patients with SARS versus healthy subjects (GSE1739 18 ).…”

Section: Methodsmentioning

confidence: 99%

Potentially repurposable drugs for COVID-19 identified from SARS-CoV-2 Host Protein Interactome

Ganapathiraju

2020

Preprint

Self Cite

We previously presented the protein-protein interaction network - the ‘HoP’ or the host protein interactome - of 332 host proteins that were identified to interact with 27 nCoV19 viral proteins by Gordon et al. Here, we studied drugs targeting the proteins in this interactome to identify whether any of them may potentially be repurposable against SARS-CoV-2. We studied each of the drugs using the BaseSpace Correlation Engine and identified those that induce gene expression profiles negatively correlated with SARS-associated expression profile. This analysis resulted in 20 drugs whose differential gene expression (drug versus normal) had an anti-correlation with differential expression for SARS (viral infection versus normal). These included drugs that were already being tested for their clinical activity against SARS-CoV-2, those with proven activity against SARS-CoV/MERS-CoV, broad-spectrum antiviral drugs, and those identified/prioritized by other computational re-purposing studies. In summary, our integrated computational analysis of the HoP interactome in conjunction with drug-induced transcriptomic data resulted in drugs that may be repurposable for COVID-19.