title: Correlation of mRNA and protein abundance in the developing maize leaf.
abstract: To help understand regulation of maize leaf blade development, including sink-source transitions and induction of C4 photosynthesis, we compared large-scale quantitative proteome and transcriptomes collected at specific stages along the developmental maize leaf blade gradient. Proteome data were based on label-free shotgun proteomics (spectral counting) and transcript data were based on RNA-seq using the same source materials, and had been published previously (Nat Genet, 42, 2010, 1060-1067; The Plant Cell, 22, 2010, 3509-3542). Transcript and protein abundance followed near normal distributions, in contrast with several studies with other organisms. Protein observability correlated with transcript abundance following a 'lazy step function' similar to that in bacteria and yeast. mRNA and protein abundance showed significant positive correlations (up to 0.8) for log-transformed length-weighted normalized spectral abundance factor (NSAF) and reads per kilobase of exon model per million mapped reads (RPKM) and non-weighted abundances (NadjSPC and COV) in dependence of function and development. Correlations were much weaker in the leaf 'sink-source' transition zone, i.e. the zone with massive investments in leaf chloroplast biogenesis and build-up of photosynthetic capacity. Clustering analyses of gene-specific protein-mRNA ratios revealed co-ordinated shifts in control points in gene expression along the leaf blade developmental gradient. The highest protein-mRNA ratio for each gene generally corresponded to leaf developmental stages in which the protein function was most important, with the exception of the 80S ribosome. Specific examples are discussed in the context of C4 photosynthesis, leaf development and sink-source transitions. This large-scale mRNA-protein correlation analysis in plants (maize) using label-free spectral counting for protein quantification and RNA-seq for mRNA abundance will provide a template for future mRNA-protein correlation studies.
authors.affiliation: Computational Biology Service Unit, Cornell University, Ithaca, NY, 14853, USA.
authors.forename: Lalit
authors.initials: L
authors.lastname: Ponnala
authors.forename: Yupeng
authors.initials: Y
authors.lastname: Wang
authors.forename: Qi
authors.initials: Q
authors.lastname: Sun
authors.forename: Klaas J
authors.initials: KJ
authors.lastname: van Wijk
mesh_terms: D016000:Cluster Analysis; D018506:Gene Expression Regulation, Plant; D010788:Photosynthesis; D018515:Plant Leaves / Q000235:genetics / Q000254:growth & development* / Q000378:metabolism; D010940:Plant Proteins / Q000235:genetics / Q000378:metabolism*; D046988:Proteasome Endopeptidase Complex / Q000378:metabolism; D012333:RNA, Messenger / Q000378:metabolism*; D012335:RNA, Ribosomal / Q000378:metabolism; D013213:Starch / Q000378:metabolism; D013395:Sucrose / Q000378:metabolism; D059467:Transcriptome; D003313:Zea mays / Q000235:genetics / Q000502:physiology*
chemical_list: D010940:Plant Proteins; D012333:RNA, Messenger; D012335:RNA, Ribosomal; D013395:Sucrose; D013213:Starch; D046988:Proteasome Endopeptidase Complex
keywords: Zea mays; leaf blade development; mRNA-protein correlation; photosynthesis; systems biology