Melissa Tomkins

RNA molecules can act as intercellular signals, with evidence of transport for both messenger RNA (mRNA) and small RNA over long distances. Such molecules that move from the cell in which they originated are termed mobile RNA. In grafted plants, when different genotypes are joined together, the movement of RNAs between tissues can be revealed by approaches that exploit natural polymorphisms between the genomes involved. However, current methods suffer from a high level of technological noise, and the lack of standardized approaches makes it challenging for researchers to robustly identify mobile RNA molecules. Here, we introduce an automated pipeline, mobileRNA, which uses a multi-genome approach based on simultaneous alignment of RNA sequencing data to merged reference genomes. We validated our approach using simulated grafting combinations and compared to other methods. When applying mobileRNA to real data, we find poor overlap to existing mobile mRNA annotations, consistent with recent studies that question the reliability of previously reported mobile mRNAs. Introducing a genome mappability index as a criterion for the likely success of detecting mobile RNAs, we find that SNP-based or two-step mapping approaches are inherently limited due to errors and poor mappability compared to cross-species grafting with large evolutionary distances.

Summary: Stomata are controllable micropores on the leaf surface that regulate gas exchange and water loss. Pore aperture adjustment is a mechanical process whereby changes in guard cell turgor drive changes in cell shape, resulting in stomatal opening or closure. Previous computational models of kidney‐shaped stomata primarily relied on guard cells elongating upon turgor increase, a process dependent on cell wall properties, such as anisotropy in stiffness, strain‐stiffening, and polar fixing. Finite element method simulations using experimentally derived 3D meshes from confocal imaging of Allium cepa (onion) suggest an alternative mechanism for opening that is largely due to changes in guard cell cross‐sectional shape. This mechanism does not rely on the assumptions for cell wall parameters required in previous models based on idealised geometries. We propose that mechanical responsiveness of guard cells to turgor changes is determined by a combination of two processes: one dependent on shape and the other on wall material properties. This suggests the parameters governing guard cell dynamics can be viewed abstractly as a two‐dimensional (shape and material properties) ‘morphospace’, allowing multiple routes of shape adaptation to optimise gas exchange.

Short-read RNA-seq studies of grafted plants have led to the proposal that thousands of messenger RNAs (mRNAs) move over long distances between plant tissues^1–7, potentially acting as signals^8–12. Transport of mRNAs between cells and tissues has been shown to play a role in several physiological and developmental processes in plants, such as tuberization¹³, leaf development¹⁴ and meristem maintenance¹⁵; yet for most mobile mRNAs, the biological relevance of transport remains to be determined^16–19. Here we perform a meta-analysis of existing mobile mRNA datasets and examine the associated bioinformatic pipelines. Taking technological noise, biological variation, potential contamination and incomplete genome assemblies into account, we find that a high percentage of currently annotated graft-mobile transcripts are left without statistical support from available RNA-seq data. This meta-analysis challenges the findings of previous studies and current views on mRNA communication.

Summary: Stomata regulate plant gas exchange via repeated turgor‐driven changes of guard cell shape, thereby adjusting pore apertures. Grasses, which are among the most widespread plant families on the planet, are distinguished by their unique stomatal structure, which is proposed to have significantly contributed to their evolutionary and agricultural success. One component of their structure, which has received little attention, is the presence of a discontinuous adjoining cell wall of the guard cell pair. Here, we demonstrate the presence of these symplastic connections in a range of grasses and use finite element method simulations to assess hypotheses for their functional significance. Our results show that opening of the stomatal pore is maximal when the turgor pressure in dumbbell‐shaped grass guard cells is equal, especially under the low pressure conditions that occur during the early phase of stomatal opening. By contrast, we demonstrate that turgor pressure differences have less effect on the opening of kidney‐shaped guard cells, characteristic of the majority of land plants, where guard cell connections are rarely or not observed. Our data describe a functional mechanism based on cellular mechanics, which plausibly facilitated a major transition in plant evolution and crop development.

The concept of ‘Differentially Expressed Genes’ (DEGs) is central to RNA-Seq studies, yet their identification suffers from reproducibility issues. This is largely a consequence of the inherent biological and technical variation that cannot be captured with small numbers of replicates. When thresholds for p-values and log ₂ fold changes are introduced, this variability can propagate an incomplete description of the data, leading to differing interpretations. Here, we compare traditional binary DEG classification with a rank-based method, grounded in Bayesian statistics, using a published yeast dataset comprising over 40 replicates. This analysis reveals how the choice of thresholds and number of replicates results in discrepancies between studies and potentially interesting genes being overlooked. Furthermore, by comparing wild-type with wild-type samples, we show how variability in gene expression can be mistaken for differential expression. Evaluating current practices for navigating the accuracy-error trade-off in the search for differentially expressed genes leads us to advocate rank-based methods and Bayesian statistics to mitigate the limitations of binary classifications and communicate uncertainty.

Advances in sequencing technologies have revolutionised our ability to capture the complete RNA profile in tissue samples, allowing for comparative analyses of RNA levels between developmental processes, environmental responses, or treatments. However, quantifying changes in gene expression remains challenging, given inherent biological variability and technological limitations. To address this, we introduce a Bayesian framework for differential gene expression (DGE) analysis. Our framework unifies and streamlines a complex analysis, typically involving parameter estimations and multiple statistical tests, into a concise mathematical equation. This allows statistical evidence for differential expression to be computed rapidly and transparently. We show how this approach can be used to evaluate variabilty of individual genes between replicates. A comparison of our framework with existing tools revealed differences that can be explained by commonly employed thresh-olds in other packages. This motivated us to explore ranking genes based on their statistical evidence as opposed to a binary classification as DEGs. Our analysis leads us to advocate the use of Bayes factors within a rank-based approach. This framework offers enhanced computational efficiency and delivers a transparent way to analyse, interpret and communicate DGE results.

Short-read RNA-Seq analyses of grafted plants have led to the proposal that large numbers of mRNAs move over long distances between plant tissues, acting as potential signals. The detection of transported transcripts by RNA-Seq is both experimentally and computationally challenging, requiring successful grafting, delicate harvesting, rigorous contamination controls and data processing approaches that can identify rare events in inherently noisy data. Here, we perform a meta-analysis of existing datasets and examine the associated bioinformatic pipelines. Our analysis reveals that technological noise, biological variation and incomplete genome assemblies give rise to features in the data that can distort the interpretation. Taking these considerations into account, we find that a substantial number of transcripts that are currently annotated as mobile are left without support from the available RNA-Seq data. Whilst several annotated mobile mRNAs have been validated, we cannot exclude that others may be false positives. The identified issues may also impact other RNA-Seq studies, in particular those using single nucleotide polymorphisms (SNPs) to detect variants.

In Arabidopis a high number of distinct mRNAs move from shoot to root. We previously reported on the correlation of m ⁵ C-methylation and lack of mRNA transport in juvenile plants depending on the RNA methyltransferases DNMT2 NSUN2B . However, to our surprise we uncovered that lack of DNMT2 NSUN2B (writer) activity did not abolished transport of TCTP1 and HSC70.1 transcripts in flowering plants. We uncovered that transport of both transcripts is reinstated in dnmt2 nsun2b mutants after commitment to flowering. This finding suggests that additional factors are seemingly involved in regulating / mediating mRNA transport. In search of such candidates, we identified the two ALY2 and ALY4 nuclear mRNA export factors belonging to the ALYREF family as bona fide m ⁵ C readers mediating mRNA transport. We show that both proteins are allocated along the phloem and that they bind preferentially to mobile mRNAs. MST measurements indicate that ALY2 and ALY4 bind to mobile mRNAs with relative high affinity with ALY4 showing higher affinity towards m ⁵ C-methylated mobile mRNAs. An analysis of the graft-mobile transcriptome of juvenile heterografted-grafted wild type, dnmt2 nsun2b , aly2 and aly4 mutants revealed that the nuclear export factors are key regulators of mRNA transport. We suggest that depending on the developmental stage m ⁵ C methylation has a negative and positive regulatory function in mRNA transport and acts together with ALY2 and ALY4 to facilitate mRNA transport in both juvenile and flowering plants.

Plants are complex systems made up of many interacting components, ranging from architectural elements such as branches and roots, to entities comprising cellular processes such as metabolic pathways and gene regulatory networks. The collective behaviour of these components, along with the plant’s response to the environment, give rise to the plant as a whole. Properties that result from these interactions and cannot be attributed to individual parts alone are called emergent properties, occurring at different time and spatial scales. Deepening our understanding of plant growth and development requires computational tools capable of handling a large number of interactions and a multiscale approach connecting properties across scales. There currently exist few methods able to integrate models across scales, or models capable of predicting new emergent plant properties. This perspective explores current approaches to modelling emergent behaviour in plants, with a focus on how current and future tools can handle multiscale plant systems.