The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been studied by transcriptomic and proteomic profiling. Here, we review the relationship between protein and mRNA levels under various scenarios, such as steady state, long-term state changes, and short-term adaptation, demonstrating the complexity of gene expression regulation, especially during dynamic transitions. The spatial and temporal variations of mRNAs, as well as the local availability of resources for protein biosynthesis, strongly influence the relationship between protein levels and their coding transcripts. We further discuss the buffering of mRNA fluctuations at the level of protein concentrations. We conclude that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes.
Complete reference maps or datasets, like the genomic map of an organism, are highly beneficial tools for biological and biomedical research. Attempts to generate such reference datasets for a proteome so far failed to reach complete proteome coverage, with saturation apparent at approximately two thirds of the proteomes tested, even for the most thoroughly characterized proteomes. Here, we used a strategy based on high-throughput peptide synthesis and mass spectrometry to generate a close to complete reference map (97% of the genome-predicted proteins) of the S. cerevisiae proteome. We generated two versions of this mass spectrometric map one supporting discovery- (shotgun) and the other hypothesis-driven (targeted) proteomic measurements. The two versions of the map, therefore, constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. The reference libraries can be browsed via a web-based repository and associated navigation tools. To demonstrate the utility of the reference libraries we applied them to a protein quantitative trait locus (pQTL) analysis, which requires measurement of the same peptides over a large number of samples with high precision. Protein measurements over a set of 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, impacting on the levels of related proteins. Our results suggest that selective pressure favors the acquisition of sets of polymorphisms that maintain the stoichiometry of protein complexes and pathways.
Plant nucleotide binding/leucine-rich repeat (NLR) immune receptors are activated by pathogen effectors to trigger host defenses and cell death. Toll-interleukin 1 receptor domain NLRs (TNLs) converge on the ENHANCED DISEASE SUSCEPTIBILITY1 (EDS1) family of lipase-like proteins for all resistance outputs. In Arabidopsis (Arabidopsis thaliana) TNL-mediated immunity, AtEDS1 heterodimers with PHYTOALEXIN DEFICIENT4 (AtPAD4) transcriptionally induced basal defenses. AtEDS1 uses the same surface to interact with PAD4-related SENESCENCE-ASSOCIATED GENE101 (AtSAG101), but the role of AtEDS1-AtSAG101 heterodimers remains unclear. We show that AtEDS1-AtSAG101 functions together with N REQUIRED GENE1 (AtNRG1) coiled-coil domain helper NLRs as a coevolved TNL cell death-signaling module. AtEDS1-AtSAG101-AtNRG1 cell death activity is transferable to the Solanaceous species Nicotiana benthamiana and cannot be substituted by AtEDS1-AtPAD4 with AtNRG1 or AtEDS1-AtSAG101 with endogenous NbNRG1. Analysis of EDS1-family evolutionary rate variation and heterodimer structure-guided phenotyping of AtEDS1 variants and AtPAD4-AtSAG101 chimeras identify closely aligned ɑ-helical coil surfaces in the AtEDS1-AtSAG101 partner C-terminal domains that are necessary for reconstituted TNL cell death signaling. Our data suggest that TNL-triggered cell death and pathogen growth restriction are determined by distinctive features of EDS1-SAG101 and EDS1-PAD4 complexes and that these signaling machineries coevolved with other components within plant species or clades to regulate downstream pathways in TNL immunity.
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