The Secret Life of RNA: Lessons from Emerging Methodologies

Regulating translation in aging: from global to gene-specific mechanisms

Aging is characterized by a decline in various biological functions that is associated with changes in gene expression programs. Recent transcriptome-wide integrative studies in diverse organisms and tissues have revealed a gradual uncoupling between RNA and protein levels with aging, which highlights the importance of post-transcriptional regulatory processes. Here, we provide an overview of multi-omics analyses that show the progressive uncorrelation of transcriptomes and proteomes during the course of healthy aging. We then describe the molecular changes leading to global downregulation of protein synthesis with age and review recent work dissecting the mechanisms involved in gene-specific translational regulation in complementary model organisms. These mechanisms include the recognition of regulated mRNAs by trans-acting factors such as miRNA and RNA-binding proteins, the condensation of mRNAs into repressive cytoplasmic RNP granules, and the pausing of ribosomes at specific residues. Lastly, we mention future challenges of this emerging field, possible buffering functions as well as potential links with disease.

Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH

Advances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein. We introduce a range of analytical techniques to interrogate single-molecule RNA fluorescence in situ hybridization (smFISH) data in combination with protein immunolabeling. DypFISH is suited to study patterns of clustering of molecules, the association of mRNA-protein subcellular localization with microtubule organizing center orientation, and interdependence of mRNA-protein spatial distributions. We showcase how our analytical tools can achieve biological insights by utilizing cell micropatterning to constrain cellular architecture, which leads to reduction in subcellular mRNA distribution variation, allowing for the characterization of their localization patterns. Furthermore, we show that our method can be applied to physiological systems such as skeletal muscle fibers.

Demystifying biotrophs: FISHing for mRNAs to decipher plant and algal pathogen-host interaction at the single cell level

Plant-pathogen interactions follow spatial and temporal developmental dynamics where gene expression in pathogen and host undergo crucial changes. Therefore, it is of great interest to detect, quantify and localise where and when key genes are active to understand these processes. Many pathosystems are not accessible for genetic amendments or other spatially-resolved gene expression monitoring methods. Here, we adapt single molecule FISH techniques to demonstrate the presence and activity of mRNAs at the single-cell level using phytomyxids in their plant and algal host in lab and field material. This allowed us to monitor and quantify the expression of genes from the clubroot pathogen Plasmodiophora brassicae, several species of its Brassica hosts, and of several brown algae, including the genome model Ectocarpus siliculosus, infected with the phytomyxid Maullinia ectocarpii. We show that mRNAs are localised along a spatiotemporal gradient, thus providing a proof-of-concept of the usefulness of single-molecule FISH to increase knowledge about the interactions between plants, algae and phytomyxids. The methods used are easily applicable to any interaction between microbes and their algal or plant host, and have therefore the potential to rapidly increase our understanding of key, spatially- and temporally-resolved processes underpinning complex plant-microbe interactions.

mRNA Metabolism in Cardiac Development and Disease: Life After Transcription

The central dogma of molecular biology illustrates the importance of mRNAs as critical mediators between genetic information encoded at the DNA level and proteomes/metabolomes that determine the diverse functional outcome at the cellular and organ levels. Although the total number of protein-producing (coding) genes in the mammalian genome is ~20,000, it is evident that the intricate processes of cardiac development and the highly regulated physiological regulation in the normal heart, as well as the complex manifestation of pathological remodeling in a diseased heart, would require a much higher degree of complexity at the transcriptome level and beyond. Indeed, in addition to an extensive regulatory scheme implemented at the level of transcription, the complexity of transcript processing following transcription is dramatically increased. RNA processing includes post-transcriptional modification, alternative splicing, editing and transportation, ribosomal loading, and degradation. While transcriptional control of cardiac genes has been a major focus of investigation in recent decades, a great deal of progress has recently been made in our understanding of how post-transcriptional regulation of mRNA contributes to transcriptome complexity. In this review, we highlight some of the key molecular processes and major players in RNA maturation and post-transcriptional regulation. In addition, we provide an update to the recent progress made in the discovery of RNA processing regulators implicated in cardiac development and disease. While post-transcriptional modulation is a complex and challenging problem to study, recent technological advancements are paving the way for a new era of exciting discoveries and potential clinical translation in the context of cardiac biology and heart disease.

Local Translation in Axons: When Membraneless RNP Granules Meet Membrane-Bound Organelles

Eukaryotic cell compartmentalization relies on long-known membrane-delimited organelles, as well as on more recently discovered membraneless macromolecular condensates. How these two types of organelles interact to regulate cellular functions is still largely unclear. In this review, we highlight how membraneless ribonucleoprotein (RNP) organelles, enriched in RNAs and associated regulatory proteins, cooperate with membrane-bound organelles for tight spatio-temporal control of gene expression in the axons of neuronal cells. Specifically, we present recent evidence that motile membrane-bound organelles are used as vehicles by RNP cargoes, promoting the long-range transport of mRNA molecules to distal axons. As demonstrated by recent work, membrane-bound organelles also promote local protein synthesis, by serving as platforms for the local translation of mRNAs recruited to their outer surface. Furthermore, dynamic and specific association between RNP cargoes and membrane-bound organelles is mediated by bi-partite adapter molecules that interact with both types of organelles selectively, in a regulated-manner. Maintaining such a dynamic interplay is critical, as alterations in this process are linked to neurodegenerative diseases. Together, emerging studies thus point to the coordination of membrane-bound and membraneless organelles as an organizing principle underlying local cellular responses.

The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond

The application of statistics has been instrumental in clarifying our understanding of the genome. While insights have been derived for almost all levels of genome function, most importantly, statistics has had the greatest impact on improving our knowledge of transcriptional regulation. But the drive to extract the most meaningful inferences from big data can often force us to overlook the fundamental role that statistics plays, and specifically, the basic assumptions that we make about big data. Normality is a statistical property that is often swept up into an assumption that we may or may not be consciously aware of making. This review highlights the inherent value of non-normal distributions to big data analysis by discussing use cases of non-normality that focus on gene expression data. Collectively, these examples help to motivate the premise of why at this stage, now more than ever, non-normality is important for learning about gene regulation, transcriptomics, and more.

Neuronal RNP granules: from physiological to pathological assemblies

Neuronal cells rely on macro- and micro-cellular compartmentalization to rapidly process information, and respond locally to external stimuli. Such a cellular organization is achieved via the assembly of neuronal ribonucleoprotein (RNP) granules, dynamic membrane-less organelles enriched in RNAs and associated regulatory proteins. In this review, we discuss how these high-order structures transport mRNAs to dendrites and axons, and how they contribute to the spatio-temporal regulation of localized mRNA translation. We also highlight how recent biophysical studies have shed light on the mechanisms underlying neuronal RNP granule dynamic assembly, remodeling and maturation, in both physiological and pathological contexts.