Lighting the shadows: methods that expose nuclear and cytoplasmic gene regulatory control

Gene regulatory circuitry of plant-environment interactions: scaling from cells to the field

Plant growth and development is the product of layers of sensing and regulation that are modulated by multifactorial environmental cues. Innovations in genomics currently allow gene regulatory control to be quantified at multiple scales and high resolution in defined cell populations and even in individual cells or nuclei in plants. The application of these 'omic technologies in highly controlled, as well as field environments is revolutionizing the recognition of factors critical to spatial and temporal responses to single or multiple environmental cues. Within and pan-species comparisons illuminate deeply conserved circuitry and targets of selection. This knowledge can benefit the breeding and engineering of crops with greater resilience to climate variability and the ability to augment nutrition through plant-microbial interactions.

In vivo homopropargylglycine incorporation enables sampling, isolation and characterization of nascent proteins from Arabidopsis thaliana

Determining which proteins are actively synthesized at a given point in time and extracting a representative sample for analysis is important to understand plant responses. Here we show that the methionine (Met) analogue homopropargylglycine (HPG) enables Bio-Orthogonal Non-Canonical Amino acid Tagging (BONCAT) of a small sample of the proteins being synthesized in Arabidopsis plants or cell cultures, facilitating their click-chemistry enrichment for analysis. The sites of HPG incorporation could be confirmed by peptide mass spectrometry at Met sites throughout protein amino acid sequences and correlation with independent studies of protein labelling with ¹⁵ N verified the data. We provide evidence that HPG-based BONCAT tags a better sample of nascent plant proteins than azidohomoalanine (AHA)-based BONCAT in Arabidopsis and show that the AHA induction of Met metabolism and greater inhibition of cell growth rate than HPG probably limits AHA incorporation at Met sites in Arabidopsis. We show HPG-based BONCAT provides a verifiable method for sampling, which plant proteins are being synthesized at a given time point and enriches a small portion of new protein molecules from the bulk protein pool for identification, quantitation and subsequent biochemical analysis. Enriched nascent polypeptides samples were found to contain significantly fewer common post-translationally modified residues than the same proteins from whole plant extracts, providing evidence for age-related accumulation of post-translational modifications in plants.

Separation and Paired Proteome Profiling of Plant Chloroplast and Cytoplasmic Ribosomes

Conventional preparation methods of plant ribosomes fail to resolve non-translating chloroplast or cytoplasmic ribosome subunits from translating fractions. We established preparation of these ribosome complexes from Arabidopsis thaliana leaf, root, and seed tissues by optimized sucrose density gradient centrifugation of protease protected plant extracts. The method co-purified non-translating 30S and 40S ribosome subunits separated non-translating 50S from 60S subunits, and resolved assembled monosomes from low oligomeric polysomes. Combining ribosome fractionation with microfluidic rRNA analysis and proteomics, we characterized the rRNA and ribosomal protein (RP) composition. The identity of cytoplasmic and chloroplast ribosome complexes and the presence of ribosome biogenesis factors in the 60S-80S sedimentation interval were verified. In vivo cross-linking of leaf tissue stabilized ribosome biogenesis complexes, but induced polysome run-off. Omitting cross-linking, the established paired fractionation and proteome analysis monitored relative abundances of plant chloroplast and cytoplasmic ribosome fractions and enabled analysis of RP composition and ribosome associated proteins including transiently associated biogenesis factors.

Systematic Review of Plant Ribosome Heterogeneity and Specialization

Plants dedicate a high amount of energy and resources to the production of ribosomes. Historically, these multi-protein ribosome complexes have been considered static protein synthesis machines that are not subject to extensive regulation but only read mRNA and produce polypeptides accordingly. New and increasing evidence across various model organisms demonstrated the heterogeneous nature of ribosomes. This heterogeneity can constitute specialized ribosomes that regulate mRNA translation and control protein synthesis. A prominent example of ribosome heterogeneity is seen in the model plant, Arabidopsis thaliana, which, due to genome duplications, has multiple paralogs of each ribosomal protein (RP) gene. We support the notion of plant evolution directing high RP paralog divergence toward functional heterogeneity, underpinned in part by a vast resource of ribosome mutants that suggest specialization extends beyond the pleiotropic effects of single structural RPs or RP paralogs. Thus, Arabidopsis is a highly suitable model to study this phenomenon. Arabidopsis enables reverse genetics approaches that could provide evidence of ribosome specialization. In this review, we critically assess evidence of plant ribosome specialization and highlight steps along ribosome biogenesis in which heterogeneity may arise, filling the knowledge gaps in plant science by providing advanced insights from the human or yeast fields. We propose a data analysis pipeline that infers the heterogeneity of ribosome complexes and deviations from canonical structural compositions linked to stress events. This analysis pipeline can be extrapolated and enhanced by combination with other high-throughput methodologies, such as proteomics. Technologies, such as kinetic mass spectrometry and ribosome profiling, will be necessary to resolve the temporal and spatial aspects of translational regulation while the functional features of ribosomal subpopulations will become clear with the combination of reverse genetics and systems biology approaches.

Integration of large-scale multi-omic datasets: a protein-centric view

Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.