Fish connectivity mapping: linking chemical stressors by their mechanisms of action-driven transcriptomic profiles.

Rong-Lin Wang,David C Bencic,Daniel L Villeneuve,Natalia Garcia-Reyero,Adam D Biales,Gerald T Ankley,Edward J Perkins

doi:10.1186/s12864-016-2406-y

Rong-Lin Wang, David C Bencic + Show 5 more

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https://doi.org/10.1186/s12864-016-2406-y

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Abstract

BackgroundA very large and rapidly growing collection of transcriptomic profiles in public repositories is potentially of great value to developing data-driven bioinformatics applications for toxicology/ecotoxicology. Modeled on human connectivity mapping (Cmap) in biomedical research, this study was undertaken to investigate the utility of an analogous Cmap approach in ecotoxicology. Over 3500 zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) transcriptomic profiles, each associated with one of several dozen chemical treatment conditions, were compiled into three distinct collections of rank-ordered gene lists (ROGLs) by species and microarray platforms. Individual query signatures, each consisting of multiple gene probes differentially expressed in a chemical condition, were used to interrogate the reference ROGLs.ResultsInformative connections were established at high success rates within species when, as defined by their mechanisms of action (MOAs), both query signatures and ROGLs were associated with the same or similar chemicals. Thus, a simple query signature functioned effectively as an exposure biomarker without need for a time-consuming process of development and validation. More importantly, a large reference database of ROGLs also enabled a query signature to cross-interrogate other chemical conditions with overlapping MOAs, leading to novel groupings and subgroupings of seemingly unrelated chemicals at a finer resolution. This approach confirmed the identities of several estrogenic chemicals, as well as a polycyclic aromatic hydrocarbon and a neuro-toxin, in the largely uncharacterized water samples near several waste water treatment plants, and thus demonstrates its future potential utility in real world applications.ConclusionsThe power of Cmap should grow as chemical coverages of ROGLs increase, making it a framework easily scalable in the future. The feasibility of toxicity extrapolation across fish species using Cmap needs more study, however, as more gene expression profiles linked to chemical conditions common to multiple fish species are needed.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2406-y) contains supplementary material, which is available to authorized users.

Highlights

A very large and rapidly growing collection of transcriptomic profiles in public repositories is potentially of great value to developing data-driven bioinformatics applications for toxicology/ecotoxicology
The performance of connectivity mapping (Cmap) was evaluated by examining the rank-ordered gene lists (ROGLs) hits with connectivity scores ranked highest either by individual query signatures or across signatures, based on fish samples profiled on each of the three microarray platforms: ZF 21K, ZF 43K, and FHM 15K
The findings in this study have demonstrated the effectiveness of this approach to make connections among chemical conditions associated with a query signature and a set of ROGLs from independent experiments, especially when both are from the same microarray platform/species

Summary

Introduction

A very large and rapidly growing collection of transcriptomic profiles in public repositories is potentially of great value to developing data-driven bioinformatics applications for toxicology/ecotoxicology. Wang et al BMC Genomics (2016) 17:84 recommending broader utilization of in vitro, in silico, and short term in vivo assays with a greater focus on mechanistic pathways in testing of chemicals and assessing their toxicological risks. Each GEP represents the collective expression states of all genes, as measured by a given microarray, for a sample under study Many of these GEPs are linked to chemical treatment or other biological conditions of potential relevance to toxicology. These abundant transcriptomic data contain a wealth of information and present opportunities for toxicologists to explore computational assessment of chemical toxicity by a datadriven approach. To date, there has been little research effort in this area in the field of toxicology

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