Tissue-specific gene expression and protein abundance patterns are associated with fractionation bias in maize

Abstract

Background: Maize experienced a whole-genome duplication event approximately 5 to 12 million years ago. Because this event occurred after speciation from sorghum, the pre-duplication subgenomes can be partially reconstructed by mapping syntenic regions to the sorghum chromosomes. During evolution, maize has had uneven gene loss between each ancient subgenome. Fractionation and divergence between these genomes continue today, constantly changing genetic make-up and phenotypes and influencing agronomic traits. Results: Here we regenerate the subgenome reconstructions for the most recent maize reference genome assembly. Based on both expression and abundance data for homeologous gene pairs across multiple tissues, we observed functional divergence of genes across subgenomes. Although the genes in the larger maize subgenome are often expressing more highly than their homeologs in the smaller subgenome, we observed cases where homeolog expression dominance switches in different tissues. We demonstrate for the first time that protein abundances are higher in the larger subgenome, but they also show tissue-specific dominance, a pattern similar to RNA expression dominance. We also find that pollen expression is uniquely decoupled from protein abundance. Conclusion: Our study shows that the larger subgenome has a greater range of functional assignments and that there is a relative lack of overlap between the subgenomes in terms of gene functions than would be suggested by similar patterns of gene expression and protein abundance. Our study also revealed that some reactions are catalyzed uniquely by the larger and smaller subgenomes. The tissue-specific, nonequivalent expression-level dominance pattern observed here implies a change in regulatory control which favors differentiated selective pressure on the retained duplicates leading to eventual change in gene functions.

The Heterogeneity in the Landscape of Gene Dominance in Maize is Accompanied by Unique Chromatin Environments

“…Next, we analyzed the protein abundance using publicly available data from 148 samples of 23 tissues with the same cutoff as that for RNA expression ( Walley et al 2016 ; Walsh et al 2020 ). In all the four categories, maize1 dominates protein abundance in most of the samples ( supplementary fig.…”

Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach

“…It is worth noting that the pollen classifiers consistently performed significantly worse than any other tissue across all experiments. Other studies including (Walsh et al, 2020) have shown that as compared to other tissues, maize pollen has fewer expressed genes, a higher rate of expressed transcription factors, and that pollen mRNA abundance has low correlation with protein abundance. The same study showed that out of the same 23 tissues only "pollen" and "endosperm crown 27 days after pollination" had higher expression in the non-dominant subgenome of maize.…”

“…The original expression data was from the gene expression tissue atlas data (Walley et al, 2016). The data was remapped to B73 RefGen v4 (Jiao et al, 2017) by (Walsh et al, 2020) and downloaded from MaizeGDB (Portwood et al, 2019). Each gene had 23 value pairs of tissuespecific RA and PA data.…”

Discovering relationships between genotype and phenotype: Machine learning approaches

“…It is worth noting the pollen classifiers consistently performed significantly worse than any other tissue across all of our experiments. Other studies including (Walsh et. al, (Walsh et al, 2020)) have shown that as compared to other tissues maize pollen has fewer expressed genes, a higher rate of expressed transcription factors, and that mRNA abundance has low correlation with protein abundance.…”

“…The original expression data is from the gene expression tissue atlas data . The data was remapped to B73 Refgen v4 by (Walsh et al, 2020) and downloaded from MaizeGDB (Portwood et al, 2019). Each gene has 23 values of RA and PA data which correspond to each tissue.…”