Published in

The Company of Biologists, Disease Models and Mechanisms, 2015

DOI: 10.1242/dmm.021238

Links

Tools

Export citation

Search in Google Scholar

A gene expression resource generated by genome-wide lacZ profiling in the mouse

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

Knowledge of the expression profile of a gene is a critical piece of information required to build an understanding of the normal and essential functions of that gene, and any role it may play in the development or progression of disease. High throughput, large scale efforts are on-going internationally to characterise reporter tagged knockout mouse lines. As part of that effort, we report an open access adult mouse expression resource in which the expression profile of 424 genes has been assessed in up to 47 different organs, tissues and sub-structures using a lacZ reporter gene. Many specific and informative expression patterns were noted. Expression was most commonly observed in the testis and brain, and was most restricted in white adipose tissue and mammary gland. Over half of the genes assessed presented with an absent or localised expression pattern (categorised as 0-10 positive structures). A link between complexity of expression profile and viability of homozygous null animals was observed; inactivation of genes expressed in ≥21 structures was more likely to result in reduced viability by postnatal day 14 compared with more restricted expression profiles. For validation purposes, this mouse expression resource was compared with Bgee, a federated composite of RNA based expression datasets. Strong agreement was observed indicating a high degree of specificity in our data. Furthermore, there were 1,207 observations of expression of a particular gene in an anatomical structure where Bgee had no data, indicating a large amount of novelty in our dataset. Examples of expression data corroborating and extending genotype-phenotype associations and supporting disease gene candidacy are presented to demonstrate the potential of this powerful resource.