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Comparison of Transcript Profiles in Wild-type and o2 Maize Endosperm in Different Genetic Backgrounds

ABSTRACT: Mutations in the Opaque2 (O2) gene of maize (Zea Mays L.) improve the nutritional value of maize by reducing the level of zeins in the kernel. The phenotype of o2 grain is controlled by many modifier genes and is therefore strongly dependent on the genetic background. We sought to distinguish between two hypotheses to explain differences in phenotypic severity: 1. Differences in phenotypic severity are due to genotype specific differentially expressed genes and 2. Differences in phenotypic severity are due to differences in the degree of differential expression of a set of differentially expressed genes. Eight pairs of inbred lines and their o2 counterparts were characterized for phenotypes controlled by o2 (lysine content, kernel density, seed weight and shoot to root ratio of seedlings). The inbred line B46 was identified as having severe o2 effects while the line M14 was identified as having minimal o2 effects. Transcripts that were differentially expressed between the wild-type and o2 versions of these two lines were identified by microarray hybridization. While genotype specific differentially expressed transcripts were identified, many transcripts were identified with reduced degree of differential expression in M14 relative to B46. Thus, a combination of genotype specific transcripts and degree of differential expression of transcripts can explain differences in phenotypic severity in these two genetic backgrounds. To identify the transcripts most likely to be differentially expressed in a given genetic background, we used RNA from eight genotype pools in microarray hybridization. By averaging across genotypes in this way, genotype-specific changes are minimized. Keywords: Genetic modification

ORGANISM(S): Zea mays

PROVIDER: GSE3490 | GEO | 2005/10/22

SECONDARY ACCESSION(S): PRJNA93311

REPOSITORIES: GEO

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Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived BMDM transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis Methods: BMDM mRNA profiles of 6-week-old wild-type (WT) and METTL14 knockout (M14−/−) mice with or without LPS treatment were generated by deep sequencing, in triplicate, using Illumina Hi-Seq 4000. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: Using an optimized data analysis workflow, we mapped about 20 million sequence reads per sample to the mouse genome (build mm9) and identified 11,902 transcripts in WT and M14−/− BMDMs with BWA workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8798. Approximately 20% of the transcripts showed differential expression between the WT and M14−/− BMDMs, with a fold change ≥1.5 and p value <0.05. Altered expression of 25 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to BMDM function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of BMDM transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

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Comparison of Transcript Profiles in Wild-type and o2 Maize Endosperm in Different Genetic Backgrounds

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