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. 2019 Dec 16;9(12):381.
doi: 10.3390/brainsci9120381.

A Pathway-Based Genomic Approach to Identify Medications: Application to Alcohol Use Disorder

Laura B Ferguson  1   2   3 , Shruti Patil  1   2 , Bailey A Moskowitz  1   3 , Igor Ponomarev  4 , Robert A Harris  1   2 , Roy D Mayfield  1   2 , Robert O Messing  1   2   3
Affiliations

A Pathway-Based Genomic Approach to Identify Medications: Application to Alcohol Use Disorder

Laura B Ferguson et al. Brain Sci. .

Abstract

Chronic, excessive alcohol use alters brain gene expression patterns, which could be important for initiating, maintaining, or progressing the addicted state. It has been proposed that pharmaceuticals with opposing effects on gene expression could treat alcohol use disorder (AUD). Computational strategies comparing gene expression signatures of disease to those of pharmaceuticals show promise for nominating novel treatments. We reasoned that it may be sufficient for a treatment to target the biological pathway rather than lists of individual genes perturbed by AUD. We analyzed published and unpublished transcriptomic data using gene set enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify biological pathways disrupted in AUD brain and by compounds in the Library of Network-based Cellular Signatures (LINCS L1000) and Connectivity Map (CMap) databases. Several pathways were consistently disrupted in AUD brain, including an up-regulation of genes within the Complement and Coagulation Cascade, Focal Adhesion, Systemic Lupus Erythematosus, and MAPK signaling, and a down-regulation of genes within the Oxidative Phosphorylation pathway, strengthening evidence for their importance in AUD. Over 200 compounds targeted genes within those pathways in an opposing manner, more than twenty of which have already been shown to affect alcohol consumption, providing confidence in our approach. We created a user-friendly web-interface that researchers can use to identify drugs that target pathways of interest or nominate mechanism of action for drugs. This study demonstrates a unique systems pharmacology approach that can nominate pharmaceuticals that target pathways disrupted in disease states such as AUD and identify compounds that could be repurposed for AUD if sufficient evidence is attained in preclinical studies.

Keywords: alcohol dependence; alcohol use disorder; gene expression; systems pharmacology.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Cell Type Enrichment Results. We determined whether genes preferentially expressed in specific cell types were enriched in the genes differentially expressed between alcohol-dependent and control brain tissue using the userlistEnrichment function from the WGCNA package in R (see Methods). The human alcohol gene expression datasets are the rows (brain region of the dataset is shown in the first column) and the cell types are columns. Yellow indicates that the genes preferentially expressed in the cell type are up-regulated in alcohol-dependent brain tissue and blue indicates genes preferentially expressed in the cell type are down-regulated in alcohol-dependent brain tissue (Bonferroni-corrected p < 0.05). The p values associated with the enrichment are shown. If a cell type had more than one cell type marker gene list associated with it (from multiple publications, for example), the most significant p value is shown in the figure. See Table S2 for the full table of p values resulting from the enrichment analysis for all datasets. Some of the cell types were enriched in both the up-regulated and down-regulated datasets. The direction chosen for the figure was based on a more significant enrichment and greater number of enriched datasets for that cell type if applicable. These occurrences are denoted in the figure and described below. * Type I microglial genes were enriched in the down-regulated genes: purple_M4_Microglia(Type1)__CTX (p = 4.61 × 10−5) and pink_M10_Microglia(Type1)__HumanMeta (p = 6.43 × 10−5). ** magenta_M8_Microglia(Type2)_MouseMeta genes were enriched in the down-regulated genes (p = 3.11 × 10−7). + Astrocyte_probably__Cahoy genes were enriched in the up-regulated genes (p = 0.000178). ++ brown_M15_Astrocyte__CTX genes were enriched in the down-regulated genes (p = 0.00131). # Oligodendrocyte_probable__Cahoy genes were enriched in the down-regulated genes (p = 9.75 × 10−5). Note that Oligodendrocyte_definite__Cahoy genes were enriched in the up-regulated but not down-regulated genes for this dataset. BLA: basolateral amygdala, CNA: central nucleus of the amygdala, PFC: prefrontal cortex, NAC: nucleus accumbens, VTA: ventral tegmental area, HPC: hippocampus, Glut: glutamatergic.
Figure 2
Figure 2
Drug-Pathway Prediction Results. We determined whether genes within KEGG pathways (within the MSigDB v6.2 dataset) were significantly up-regulated or down-regulated by drugs in CMap and L1000 databases using Gene Set Enrichment Analysis (GSEA). We downloaded the drug gene expression signatures for CMap from ftp://ftp.broadinstitute.org/distribution/cmap/ (amplitudeMatrix.txt) and the L1000 signatures from Gene Expression Omnibus (Level 5 data; Phase I: GSE92742, Phase II: GSE7013). Histograms of the number of pathways predicted to be targeted by drugs in CMap (A) or L1000 (C) databases. Histograms of the number of drugs in CMap (B) or L1000 (D) databases predicted to target pathways. The blue dashed line represents the median number of pathways in A and C or drugs in B and D.
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