Table1_Enrichment analysis of phenotypic data for drug repurposing in rare diseases.DOCX

Drug-induced Behavioral Signature Analysis (DBSA), is a machine learning (ML) method for in silico screening of compounds, inspired by analytical methods quantifying gene enrichment in genomic analyses. When applied to behavioral data it can identify drugs that can potentially reverse in vivo behavioral symptoms in animal models of human disease and suggest new hypotheses for drug discovery and repurposing. We present a proof-of-concept study aiming to assess Drug-induced Behavioral Signature Analysis (DBSA) as a systematic approach for drug discovery for rare disorders. We applied Drug-induced Behavioral Signature Analysis to high-content behavioral data obtained with SmartCube®, an automated in vivo phenotyping platform. The therapeutic potential of several dozen approved drugs was assessed for phenotypic reversal of the behavioral profile of a Huntington’s Disease (HD) murine model, the Q175 heterozygous knock-in mice. The in silico Drug-induced Behavioral Signature Analysis predictions were enriched for drugs known to be effective in the symptomatic treatment of Huntington’s Disease, including bupropion, modafinil, methylphenidate, and several SSRIs, as well as the atypical antidepressant tianeptine. To validate the method, we tested acute and chronic effects of tianeptine (20 mg/kg, i. p.) in vivo, using Q175 mice and wild type controls. In both experiments, tianeptine significantly rescued the behavioral phenotype assessed with the SmartCube® platform. Our target-agnostic method thus showed promise for identification of symptomatic relief treatments for rare disorders, providing an alternative method for hypothesis generation and drug discovery for disorders with huge disease burden and unmet medical needs.</p

FANTOM 5: Discovering TSS-specific transcriptional regulation

<p>We use the FANTOM5 data in order to check for the existance of transcirption start site (TSS)-specific regulation. We apply our proven methods to reverse engineering the regulatory network controlling TSS-specific expression.</p

Schematic representations of the CINDy algorithm.

<p>A collection of gene expression profiles is required to calculate Conditional Mutual Information between lists of modulators, transcription factors and putative target genes, with the final output of inferred modulation events.</p

Default parameters used for running MINDy.

<p>NA: Not applicable.</p><p>Default parameters used for running MINDy.</p

Example of novel prediction by CINDy.

<p>Proposed mechanism for modulation of HMGA1 by CDK2.</p

Comparative performance of MINDy and CINDy.

<p>Precision and recall values are compared in the B-cell lymphoma dataset (panel A) and Lung Adenocarcinoma dataset (panel B), calculated by matching the predictions with a gold standard dataset set obtained from four different databases of experimentally validated PPIs between modulators and transcription factors. Precision and recall are further compared at different robustness threshold for MINDy (blue line) and CINDy (red line) in the B-cell dataset (panel C) and in the Lung dataset (panel D, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109569#s4" target="_blank"><b>Materials and Methods</b></a>).</p

Alternative three-way network topologies including a Transcription Factor (TF), a Target gene (Tg) and a Modulator gene (M).

<p>(A) depicts the independent regulation of the target gene by a modulator and a TF; (B) describes a three-way interaction between the TF, the target gene and the modulator.</p

QQ-plot (panel A) and GSEA plots (panel B) of the KEGG ABC transporter pathway.

<p>The QQ-plot is constructed using the genotyped SNPs whose snp-map contains at least one ABC transporter pathway gene. The GSEA plot shows the enrichment score of the ABC transporter pathway. The top portion of the plot shows the running enrichment score for the pathway genes as the analysis moves down the ranked list. The peak score is the enrichment score for the gene set. The bottom portion of the plot shows the value of the ranking metric as it moves down the list of ranked genes. The plots for the other two enriched pathways (Proteasome and Propanoate metabolism) look similar (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131038#pone.0131038.s002" target="_blank">S2 Fig</a>).</p

Additional file 1: of A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data

Supplementary Data. Supplementary analysis and supplementary figures and tables. (DOCX 2030 kb

Key steps of the Pointer algorithm.

<p>Key steps of the Pointer algorithm.</p