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01.12.2017 | Research article | Ausgabe 1/2017 Open Access

Arthritis Research & Therapy 1/2017

Support for phosphoinositol 3 kinase and mTOR inhibitors as treatment for lupus using in-silico drug-repurposing analysis

Zeitschrift:
Arthritis Research & Therapy > Ausgabe 1/2017
Autoren:
Daniel Toro-Domínguez, Pedro Carmona-Sáez, Marta E. Alarcón-Riquelme
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s13075-017-1263-7) contains supplementary material, which is available to authorized users.

Abstract

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease with few treatment options. Current therapies are not fully effective and show highly variable responses. In this regard, large efforts have focused on developing more effective therapeutic strategies. Drug repurposing based on the comparison of gene expression signatures is an effective technique for the identification of new therapeutic approaches. Here we present a drug-repurposing exploratory analysis using gene expression signatures from SLE patients to discover potential new drug candidates and target genes.

Methods

We collected a compendium of gene expression signatures comprising peripheral blood cells and different separate blood cell types from SLE patients. The Lincscloud database was mined to link SLE signatures with drugs, gene knock-down, and knock-in expression signatures. The derived dataset was analyzed in order to identify compounds, genes, and pathways that were significantly correlated with SLE gene expression signatures.

Results

We obtained a list of drugs that showed an inverse correlation with SLE gene expression signatures as well as a set of potential target genes and their associated biological pathways. The list includes drugs never or little studied in the context of SLE treatment, as well as recently studied compounds.

Conclusion

Our exploratory analysis provides evidence that phosphoinositol 3 kinase and mammalian target of rapamycin (mTOR) inhibitors could be potential therapeutic options in SLE worth further future testing.
Zusatzmaterial
Additional file 1: presents information about quality control of the data, SLE signatures, and significant results obtained: Sheet 1 shows information about datasets including their GEO identifiers, the SLE state, the cell type, and the microarray platform for each, number of control and case samples, PubMed identifier, and date of publication; Sheet 2 shows results of the quality control based on the percentage of missing values and the number of significant genes in each dataset of SLE; Sheet 3 shows the signatures of each dataset used to query on Lincscloud, significant genes sorted by fold change in each signature; and Sheet 4 shows the lists of drugs and knock-in and knockdown genes with similarity scores, median of similarity scores across datasets, and significance values (XLSX 182 kb)
13075_2017_1263_MOESM1_ESM.xlsx
Additional file 2: is a figure showing the PI3K molecular signaling pathway. Plot constructed based on the information of different PI3K interaction graphs from the KEGG database. Red, drug targets with positive similarity scores; blue, drug targets with negative similarity scores (PDF 373 kb)
13075_2017_1263_MOESM2_ESM.pdf
Additional file 3: shows a description of the functional analysis of gene targets obtained and their significance, and is divided into three sections: methods, results, and references. (PDF 116 kb)
13075_2017_1263_MOESM3_ESM.pdf
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