An integrative approach to analyze microarray datasets for prioritization of genes relevant to lens biology and disease

Abstract

Microarray-based profiling represents an effective method to analyze cellular or tissue-specific gene expression on the genome-level. However, in comparative analyses between control and mutant samples, microarrays often identify a large number of differentially expressed genes, in turn making it challenging to isolate the select "high-priority candidates" that are most relevant to an observed mutant phenotype. Here, we describe an integrative approach for mouse mutant lens microarray gene expression analysis using publically accessible systems-level information such as wild-type mouse lens expression data in iSyTE (integrated Systems Tool for Eye gene discovery), protein-protein interaction data in public databases, gene ontology enrichment data, and transcription factor binding profile data. This strategy, when applied to small Maf Mafg-/-:Mafk+/- mouse lens microarray datasets (deposited in NCBI Gene Expression Omnibus database with accession number GSE65500) in Agrawal et al. 2015 [1], led to the effective prioritization of candidate genes linked to lens defects in these mutants. Indeed, from the original list of genes that are differentially expressed at ±1.5-fold and p<0.05 in Mafg-/-:Mafk+/- mutant lenses, this analysis led to the identification of thirty-six high-priority candidates, in turn reducing the number of genes for further study by approximately 1/3^rd of the total. Moreover, eight of these genes are linked to mammalian cataract in the published literature, validating the efficacy of this approach. Additionally, these high-priority candidates contribute valuable information for the assembly of a gene regulatory network in the lens. In sum, the pipeline outlined in this report represents an effective approach for initial as well as downstream microarray expression data analysis to identify genes important for lens biology and cataracts. We anticipate that this integrative strategy can be extended to prioritize phenotypically relevant candidate genes from microarray data in other cells and tissues.

Keywords: Cataract; Gene expression; Lens; Microarrays; iSyTE.

Fig. 1

Workflow of microarray design, data pre-processing and analysis of differentially expressed genes.

Fig. 2

Selection of mutant stage for lens microarray analysis. Light microscopy based grid imaging of lenses from (A) control and (B) Mafg −/−:Mafk +/− compound mutant mice demonstrates no opacities at post-natal stage (P)60 or 2 months (2 mo.). High-resolution scanning electron microscopy (SEM) analysis of lenses from (A’) control and (B’) Mafg −/−:Mafk +/− compound mutant mice confirm the absence of overt abnormalities in mutant fiber cells at stage 2 mo. However, grid imaging analysis at age 7 months (7 mo.) demonstrates that while (C) control mice have transparent lenses, (D) Mafg −/−:Mafk +/− compound mutant mice exhibit lens opacities (asterisk). SEM analysis at age 7 months demonstrates that while (C’) Control lenses have normal fiber cells, (D’) Mafg −/−:Mafk +/− compound mutant mice exhibit severe fiber cell defects (asterisk). Based on this analysis, the age P60 (2 mo.), when Mafg −/−:Mafk +/− compound mutant mice do not exhibit overt lens defects, was selected as the stage to perform microarrays. This is based on the consideration that microarrays at P60 will increase the likelihood of detecting gene expression changes that reflect primary alterations prior to the manifestation of overt defects that occur with age. Scale bar in B’ and D’ is 10 μm.

Fig. 3

Microarray expression data quality assessment plots. Comparisons of raw unprocessed and normalized processed expression intensities between arrays of mutant and control datasets. PCA plots from (A) raw and (B) processed datasets. Histograms for (C) raw and (D) processed datasets. Boxplot for (E) raw and (F) processed datasets. Key to samples in each type of analysis is given below.