Noncell-autonomous HSC70.1 chaperone displays homeostatic feedback regulation by binding its own mRNA.

The HSC70/HSP70 family of heat shock proteins are evolutionarily conserved chaperones involved in protein folding, protein transport, and RNA binding. Arabidopsis HSC70 chaperones are thought to act as housekeeping chaperones and as such are involved in many growth-related pathways. Whether Arabidopsis HSC70 binds RNA and whether this interaction is functional has remained an open question. We provide evidence that the HSC70.1 chaperone binds its own mRNA via its C-terminal short variable region (SVR) and inhibits its own translation. The SVR encoding mRNA region is necessary for HSC70.1 transcript mobility to distant tissues and that HSC70.1 transcript and not protein mobility is required to rescue root growth and flowering time of hsc70 mutants. We propose that this negative protein-transcript feedback loop may establish an on-demand chaperone pool that allows for a rapid response to stress. In summary, our data suggest that the Arabidopsis HSC70.1 chaperone can form a complex with its own transcript to regulate its translation and that both protein and transcript can act in a noncell-autonomous manner, potentially maintaining chaperone homeostasis between tissues.

High expression of the MADS-box gene VRT2 increases the number of rudimentary basal spikelets in wheat.

Spikelets are the fundamental building blocks of Poaceae inflorescences, and their development and branching patterns determine the various inflorescence architectures and grain yield of grasses. In wheat (Triticum aestivum), the central spikelets produce the most and largest grains, while spikelet size gradually decreases acropetally and basipetally, giving rise to the characteristic lanceolate shape of wheat spikes. The acropetal gradient corresponds with the developmental age of spikelets; however, the basal spikelets are developed first, and the cause of their small size and rudimentary development is unclear. Here, we adapted G&T-seq, a low-input transcriptomics approach, to characterize gene expression profiles within spatial sections of individual spikes before and after the establishment of the lanceolate shape. We observed larger differences in gene expression profiles between the apical, central, and basal sections of a single spike than between any section belonging to consecutive developmental time points. We found that SHORT VEGETATIVE PHASE MADS-box transcription factors, including VEGETATIVE TO REPRODUCTIVE TRANSITION 2 (VRT-A2), are expressed highest in the basal section of the wheat spike and display the opposite expression gradient to flowering E-class SEPALLATA1 genes. Based on multi-year field trials and transgenic lines, we show that higher expression of VRT-A2 in the basal sections of the spike is associated with increased numbers of rudimentary basal spikelets. Our results, supported by computational modeling, suggest that the delayed transition of basal spikelets from vegetative to floral developmental programs results in the lanceolate shape of wheat spikes. This study highlights the value of spatially resolved transcriptomics to gain insights into developmental genetics pathways of grass inflorescences.

An update on passive transport in and out of plant cells.

Transport across membranes is critical for plant survival. Membranes are the interfaces at which plants interact with their environment. The transmission of energy and molecules into cells provides plants with the source material and power to grow, develop, defend, and move. An appreciation of the physical forces that drive transport processes is thus important for understanding the plant growth and development. We focus on the passive transport of molecules, describing the fundamental concepts and demonstrating how different levels of abstraction can lead to different interpretations of the driving forces. We summarize recent developments on quantitative frameworks for describing diffusive and bulk flow transport processes in and out of cells, with a more detailed focus on plasmodesmata, and outline open questions and challenges.

Towards modelling emergence in plant systems.

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.

Exact Bayesian inference for the detection of graft-mobile transcripts from sequencing data.

The long-distance transport of messenger RNAs (mRNAs) has been shown to be important for several developmental processes in plants. A popular method for identifying travelling mRNAs is to perform RNA-Seq on grafted plants. This approach depends on the ability to correctly assign sequenced mRNAs to the genetic background from which they originated. The assignment is often based on the identification of single-nucleotide polymorphisms (SNPs) between otherwise identical sequences. A major challenge is therefore to distinguish SNPs from sequencing errors. Here, we show how Bayes factors can be computed analytically using RNA-Seq data over all the SNPs in an mRNA. We used simulations to evaluate the performance of the proposed framework and demonstrate how Bayes factors accurately identify graft-mobile transcripts. The comparison with other detection methods using simulated data shows how not taking the variability in read depth, error rates and multiple SNPs per transcript into account can lead to incorrect classification. Our results suggest experimental design criteria for successful graft-mobile mRNA detection and show the pitfalls of filtering for sequencing errors or focusing on single SNPs within an mRNA.

A multi-layered mechanistic modelling approach to understand how effector genes extend beyond phytoplasma to modulate plant hosts, insect vectors and the environment.

Members of the Candidatus genus Phytoplasma are small bacterial pathogens that hijack their plant hosts via the secretion of virulence proteins (effectors) leading to a fascinating array of plant phenotypes, such as witch's brooms (stem proliferations) and phyllody (retrograde development of flowers into vegetative tissues). Phytoplasma depend on insect vectors for transmission, and interestingly, these insect vectors were found to be (in)directly attracted to plants with these phenotypes. Therefore, phytoplasma effectors appear to reprogram plant development and defence to lure insect vectors, similarly to social engineering malware, which employs tricks to lure people to infected computers and webpages. A multi-layered mechanistic modelling approach will enable a better understanding of how phytoplasma effector-mediated modulations of plant host development and insect vector behaviour contribute to phytoplasma spread, and ultimately to predict the long reach of phytoplasma effector genes.