Genome-Wide Expression Profiles Drive Discovery of Novel Compounds that Reduce Binge Drinking in Mice

Abstract

Transcriptome-based drug discovery has identified new treatments for some complex diseases, but has not been applied to alcohol use disorder (AUD) or other psychiatric diseases, where there is a critical need for improved pharmacotherapies. High Drinking in the Dark (HDID-1) mice are a genetic model of AUD risk that have been selectively bred (from the HS/Npt line) to achieve intoxicating blood alcohol levels (BALs) after binge-like drinking. We compared brain gene expression of HDID-1 and HS/Npt mice, to determine a molecular signature for genetic risk for high intensity, binge-like drinking. Using multiple computational methods, we queried LINCS-L1000 (Library of Integrated Network-Based Cellular Signatures), a database containing gene expression signatures of thousands of compounds, to predict candidate drugs with the greatest potential to decrease alcohol consumption. Our analyses predicted novel compounds for testing, many with anti-inflammatory properties, providing further support for a neuroimmune mechanism of excessive alcohol drinking. We validated the top 2 candidates in vivo as a proof-of-concept. Terreic acid (a Bruton's tyrosine kinase inhibitor) and pergolide (a dopamine and serotonin receptor agonist) robustly reduced alcohol intake and BALs in HDID-1 mice, providing the first evidence for transcriptome-based drug discovery to target an addiction trait. Effective drug treatments for many psychiatric diseases are lacking, and the emerging tools and approaches outlined here offer researchers studying complex diseases renewed opportunities to discover new or repurpose existing compounds and expedite treatment options.

From gene networks to drugs: systems pharmacology approaches for AUD

“…Most studies operate under the transcriptional “reversal hypothesis”, which assumes that drugs with negative connectivity scores (i.e., with gene expression signatures that revert the disease’s effects on gene expression to the control state) would ameliorate disease phenotype. Five of the 19 studies outlined in Table 3 have functionally validated this hypothesis, in that the candidate compounds ameliorated some disease phenotype when tested behaviorally (though none have confirmed that the beneficial effects of the compound were due to the restoration of gene expression to the “normal” state) (Chandran et al 2016; Ferguson et al 2017; Mirza et al 2017; Papassotiropoulos et al 2013; Smalley et al 2016). …”

“…Some researchers make no attempt to summarize across cell lines, doses, and time points to attain a composite compound-level view, which could lead to spurious results, particularly if the cell line is vastly different from the cellular makeup of the tissue used to generate the genomic signature used as the input query. For these reasons, we propose that using multiple expression datasets, algorithm parameter settings, and methods for prioritizing compounds (as taken by (Ferguson et al 2017; Gao et al 2014; Guedj et al 2016; Siavelis et al 2016)) are critical to identify an effective drug candidate, at least until proper gold standard datasets exist with which to benchmark the optimal settings (see below).…”

“…Even if brain-relevant cell lines were included in the expression profiles of LINCS-L1000 or other databases of drug-related transcriptomes, it is unclear how relevant in vitro results are to the biology of an intact organism, which is why in vivo experimental validation is critical. Only 6 of the 19 studies for brain disease listed in Table 3 performed in vivo validation of the proposed pharmaceutical candidates (Azim et al 2017; Chandran et al 2016; Ferguson et al 2017; Mirza et al 2017; Papassotiropoulos et al 2013; Smalley et al 2016), and none directly tested the underlying assumption of in silico connectivity mapping. Specifically, it is important to address the following question: if a candidate compound is effective in treating a given disease phenotype, was it the result of a reversal in expression of disease-related genes by the compound?…”

“…Each of the 6 studies discussed in the previous paragraph used a different approach to identify a candidate compound that ameliorated disease phenotype when tested (Azim et al 2017; Chandran et al 2016; Ferguson et al 2017; Mirza et al 2017; Papassotiropoulos et al 2013; Smalley et al 2016), and it is critical to identify the approach(s) with the greatest predictive accuracy. In other words, which choices at each of the main steps outlined above are the most likely to identify compounds that actually ameliorate the disease state?…”

Persistence of Drug Memories: Melting Transcriptomes

“…Another potentially exciting application of these data is to use computational approaches that predict drug treatments from transcriptome changes (8). This was recently applied using a genetic mouse model of high alcohol consumption to predict novel drugs that reduced alcohol intake (9). The explosion of transcriptome profiling in addiction biology, and all of neuroscience, raises the question of how this information can inform diagnosis and treatment.…”