Information on RNA localisation is essential for understanding physiological and pathological processes, such as gene expression, cell reprogramming, host–pathogen interactions, and signalling pathways involving RNA transactions at the level of membrane-less or membrane-bounded organelles and extracellular vesicles. In many cases, it is important to assess the topology of RNA localisation, i.e., to distinguish the transcripts encapsulated within an organelle of interest from those merely attached to its surface. This allows establishing which RNAs can, in principle, engage in local molecular interactions and which are prevented from interacting by membranes or other physical barriers. The most widely used techniques interrogating RNA localisation topology are based on the treatment of isolated organelles with RNases with subsequent identification of the surviving transcripts by northern blotting, qRT-PCR, or RNA-seq. However, this approach produces incoherent results and many false positives. Here, we describe Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq), a more refined subcellular transcriptomics approach that overcomes these pitfalls. CoLoC-seq starts by the purification of organelles of interest. They are then either left intact or lysed and subjected to a gradient of RNase concentrations to produce unique RNA degradation dynamics profiles, which can be monitored by northern blotting or RNA-seq. Through straightforward mathematical modelling, CoLoC-seq distinguishes true membrane-enveloped transcripts from degradable and non-degradable contaminants of any abundance. The method has been implemented in the mitochondria of HEK293 cells, where it outperformed alternative subcellular transcriptomics approaches. It is applicable to other membrane-bounded organelles, e.g., plastids, single-membrane organelles of the vesicular system, extracellular vesicles, or viral particles.

RNA-binding proteins are key actors of post-transcriptional networks. Almost exclusively studied in the light of their interactions with RNA ligands and the associated functional events, they are still poorly understood as evolutionary units. In this review, we discuss the FinO/ProQ family of bacterial RNA chaperones, how they evolve and spread across bacterial populations and what properties and opportunities they provide to their host cells. We reflect on major conserved and divergent themes within the family, trying to understand how the same ancestral RNA-binding fold, augmented with additional structural elements, could yield either highly specialised proteins or, on the contrary, globally acting regulatory hubs with a pervasive impact on gene expression. We also consider dominant convergent evolutionary trends that shaped their RNA chaperone activity and recurrently implicated the FinO/ProQ-like proteins in bacterial DNA metabolism, translation and virulence. Finally, we offer a new perspective in which FinO/ProQ-family regulators emerge as active evolutionary players with both negative and positive roles, significantly impacting the evolutionary modes and trajectories of their bacterial hosts.

Proper RNA localisation is essential for physiological gene expression. Various kinds of genome-wide approaches permit to comprehensively profile subcellular transcriptomes. Among them, cell fractionation methods, that couple RNase treatment of isolated organelles to the sequencing of protected transcripts, remain most widely used, mainly because they do not require genetic modification of the studied system and can be easily implemented in any cells or tissues, including in non-model species. However, they suffer from numerous false-positives since incompletely digested contaminant RNAs can still be captured and erroneously identified as resident transcripts. Here we introduce Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq) as a new subcellular transcriptomics approach that efficiently bypasses this caveat. CoLoC-seq leverages classical enzymatic kinetics and tracks the depletion dynamics of transcripts in a gradient of an exogenously added RNase, with or without organellar membranes. By means of straightforward mathematical modelling, CoLoC-seq infers the localisation topology of RNAs and robustly distinguishes between genuinely resident, luminal transcripts and merely abundant surface-attached contaminants. Our generic approach performed well on human mitochondria and is in principle applicable to other membrane-bounded organelles, including plastids, compartments of the vacuolar system, extracellular vesicles, and viral particles.

RNA-binding proteins are central players in post-transcriptional regulation. Some of them, such as the well-studied bacterial RNA chaperones Hfq and ProQ or the eukaryotic RNAi factor Argonaute, interact with hundreds-to-thousands of different RNAs and thereby globally affect gene expression. As a shared yet limited resource, these and other RNA-binding hubs drive strong competition between their multiple ligands. This creates a ground for significant cross-communication between RNA targets, which enables them to share information, “synchronise” their behaviour, and produce interesting biochemical effects, sometimes propagating across the highly connected RNA-protein network. This property is likely universally present in hub-centred networks and plays a key role in global gene expression programmes. It is also an important factor in biotechnology and synthetic biology applications of RNA/protein-based circuits. However, few studies so-far focused on describing and explaining this phenomenon from first principles. Here we introduce an information theory-based framework to comprehensively and exactly describe information flow in hub-centred networks. We show that information sharing can achieve significant levels in relatively small networks, provided the hub is present in limiting concentrations. The transmitted information is sufficient to noticeably affect the binding probabilities of competing targets but drops exponentially along the network. Target overexpression can disrupt communication between other targets, while hub sequestration boosts the crosstalk. We also find that overlaps between the interactomes of two different hubs create both entropic challenges and new forms of long-range communication between RNAs and proteins.