Periodic Reporting for period 3 - PLAMORF (Plant Mobile RNAs: Function, Transport and Features)
Berichtszeitraum: 2022-04-01 bis 2023-09-30
An essential consequence of multi-cellularity is the need for intercellular and tissue-wide communication. As seen with animals, higher plants coordinate metabolic and developmental processes via signals transferred to different body parts. Plants use a dual vascular system consisting of phloem and xylem for long-distance transfer of metabolites and signalling molecules. In addition to small molecules, a remarkably large number of micro RNAs (miRNAs) and messenger RNAs (mRNAs) were identified in the phloem shown to move between plant parts. These mobile RNAs represent potential signalling molecules, and phloem RNA-binding proteins (RBPs) that seem to stabilize and facilitate transport of RNAs.
The main overall objectives of PLAMORF are to decipher the inter-organ communication pathway based on RNA signals and their role in coordinating nutrient allocation, and deficiency adaptation with plant growth. To achieve this, the project combines the expertise of three research groups in the fields of cell biology/macromolecular transport, mathematical modelling/bioinformatics and phloem function/protein biochemistry.
Based on the observation that stress conditions alter the composition of transported RNAs significantly, deciphering RNA signalling is relevant for our understanding of how crop plants adopt to growth limiting soil conditions. Moreover, since also plant pathogens such as viruses and fungi produce mobile RNAs that spread or facilitate infection, our results will also shed light on how pathogens infect plants. Thus, the results of PLAMORF can potentially be exploited to contribute to a more sustainable agriculture by increasing nutrient use efficiency and resistance to other stresses and diseases. Here, strategies include the use of selected naturally occurring gene variants for breeding or molecularly modified RNAs or RBPs.
The main overall objectives of PLAMORF are to decipher the inter-organ communication pathway based on RNA signals and their role in coordinating nutrient allocation, and deficiency adaptation with plant growth. To achieve this, the project combines the expertise of three research groups in the fields of cell biology/macromolecular transport, mathematical modelling/bioinformatics and phloem function/protein biochemistry.
Based on the observation that stress conditions alter the composition of transported RNAs significantly, deciphering RNA signalling is relevant for our understanding of how crop plants adopt to growth limiting soil conditions. Moreover, since also plant pathogens such as viruses and fungi produce mobile RNAs that spread or facilitate infection, our results will also shed light on how pathogens infect plants. Thus, the results of PLAMORF can potentially be exploited to contribute to a more sustainable agriculture by increasing nutrient use efficiency and resistance to other stresses and diseases. Here, strategies include the use of selected naturally occurring gene variants for breeding or molecularly modified RNAs or RBPs.
Different strategies were taken to identify mobile RNAs and RBPs, to characterise their function, and to predict and experimentally validate their mobility, binding motifs, and molecular structures. So far, we established the most important methods and platforms for the analysis of mobile RNAs and RBPs, which are single cell sequencing, functional-structural protein characterisation, computational prediction, data analysis, and modelling.
Looking at mobile RNAs, we have determined the transcript and small RNA complement of Brassica napus plants. We introduced Brassica Wisconsin FastPlants as an alternative to Arabidopsis to detect mobile RNAs. Heterografting experiments have been performed and sequencing data will be evaluated to identify the mobile RNA population. We have also established highly sensitive LAMP PCR assays to detect RNAs from minute sample amounts to enable time-resolved transcript and miRNA analysis from single phloem/tissue droplets from different plant body regions and various time points of growth.
By analysing data on mobile RNAs in Arabidopsis grafts, we found a significant overlap between mobile mRNAs and secondary modified (methylated) mRNAs and have shown that candidate transcript transport is abolished in plants lacking methyltransferase activity. Using non-mobile structurally changed transcript variants we could show that transcript mobility is necessary to induce or complement mutant phenotypes in grafted plants. This approach confirmed that some mobile mRNAs act as a signal changing growth behaviour of receiving tissues. Furthermore, we have established a database of RNAs transported from roots to leaves, stem, and/or flowers and vice versa on the whole tissue and single cell level. This revealed a high number of mobile candidate transcripts that are not expressed in receiving cells/tissues.
Concerning phloem RNA-binding proteins, we have identified additional RBPs from phloem samples and have started with a functional-structural characterisation of six RBPs. Pipelines to produce proteins and different RNAs have been set up and Microscale Thermophoresis (MST) was established to quantify interaction affinities precisely. We have confirmed mobility of candidate RBPs and their initial structures were determined.
Concerning computational and bioinformatic analyses we have designed and built three main computational developments that will support this project. We have built extensive data analysis pipelines for mRNA analysis. These include primary motif searches, binding to cognate protein affinity, GC content, secondary and tertiary structure prediction (in progress), disorder, codon bias, and methylation potential. Current analyses have confirmed previous studies that available tools are not able to identify sequence signals with predictive power that might explain mRNA mobility. More single-cell data becoming available will provide an important new data set with which we can challenge our computational pipelines further.
Looking at mobile RNAs, we have determined the transcript and small RNA complement of Brassica napus plants. We introduced Brassica Wisconsin FastPlants as an alternative to Arabidopsis to detect mobile RNAs. Heterografting experiments have been performed and sequencing data will be evaluated to identify the mobile RNA population. We have also established highly sensitive LAMP PCR assays to detect RNAs from minute sample amounts to enable time-resolved transcript and miRNA analysis from single phloem/tissue droplets from different plant body regions and various time points of growth.
By analysing data on mobile RNAs in Arabidopsis grafts, we found a significant overlap between mobile mRNAs and secondary modified (methylated) mRNAs and have shown that candidate transcript transport is abolished in plants lacking methyltransferase activity. Using non-mobile structurally changed transcript variants we could show that transcript mobility is necessary to induce or complement mutant phenotypes in grafted plants. This approach confirmed that some mobile mRNAs act as a signal changing growth behaviour of receiving tissues. Furthermore, we have established a database of RNAs transported from roots to leaves, stem, and/or flowers and vice versa on the whole tissue and single cell level. This revealed a high number of mobile candidate transcripts that are not expressed in receiving cells/tissues.
Concerning phloem RNA-binding proteins, we have identified additional RBPs from phloem samples and have started with a functional-structural characterisation of six RBPs. Pipelines to produce proteins and different RNAs have been set up and Microscale Thermophoresis (MST) was established to quantify interaction affinities precisely. We have confirmed mobility of candidate RBPs and their initial structures were determined.
Concerning computational and bioinformatic analyses we have designed and built three main computational developments that will support this project. We have built extensive data analysis pipelines for mRNA analysis. These include primary motif searches, binding to cognate protein affinity, GC content, secondary and tertiary structure prediction (in progress), disorder, codon bias, and methylation potential. Current analyses have confirmed previous studies that available tools are not able to identify sequence signals with predictive power that might explain mRNA mobility. More single-cell data becoming available will provide an important new data set with which we can challenge our computational pipelines further.
Several novel methods and platforms for the analysis of mobile RNAs and RBPs, single cell sequencing, functional-structural protein characterisation, computational prediction, data analysis, and modelling were developed in the first reporting period. We will extend and refine these tools as appropriate during the project and apply them for data collection and analysis to gain fundamental new insights in RNA transport and function.
In addition to Arabidopsis, the data obtained from Brassica heterografting experiments will establish the mobile transcript and small RNA population in this important crop species. Stress experiments will identify accumulating translocated RNAs as potential signalling molecules. LAMP PCR will allow time-resolved analysis of the movement of specific RNAs in response to nutrient deficiencies and infection. We will use mathematical modelling to describe this signaling process, using the experimentally determined binding data and RNA populations as parameters.
Concerning RBPs, we expect to significantly increase the number of characterised phloem RBPs until the end of the project. This includes their 3D structures, also complexed with RNAs, and intrinsic RNA binding capacities. In our protein-RNA affinity pipeline, we intend to expand the numbers and types of RNAs available, including base modifications and circular RNAs. Phloem RBP interacting proteins will be identified by BioID and complementary methods. Interacting RNAs will be addressed by newly implemented methods providing additional information about the duration of RNA binding. A few candidate RNAs are expected to be structurally analysed by SAXS and CryoEM permitting to correlate RNA 3D structures to RNA binding and RNA transport signals.
We expect to extend our Bayesian inference and machine learning pipeline to explore different methods for automated feature extraction. We are currently testing CNNs and will trial these further for the identification of mobility triggers. This is currently all sequence-based. Next steps will include the extension to 2D and 3D properties.
We are currently exploring new approaches for sampling 2D and 3D conformations (Nested Sampling and Entropy methods) based on coarse-grained molecular dynamics and thermodynamic descriptions. We will use these methods to predict possible 2D/3D motifs that might be important for recognition by RBPs. Close integration of these methods with the experimental data will be key for elucidating RNA-protein interactions and specificity.
In addition to Arabidopsis, the data obtained from Brassica heterografting experiments will establish the mobile transcript and small RNA population in this important crop species. Stress experiments will identify accumulating translocated RNAs as potential signalling molecules. LAMP PCR will allow time-resolved analysis of the movement of specific RNAs in response to nutrient deficiencies and infection. We will use mathematical modelling to describe this signaling process, using the experimentally determined binding data and RNA populations as parameters.
Concerning RBPs, we expect to significantly increase the number of characterised phloem RBPs until the end of the project. This includes their 3D structures, also complexed with RNAs, and intrinsic RNA binding capacities. In our protein-RNA affinity pipeline, we intend to expand the numbers and types of RNAs available, including base modifications and circular RNAs. Phloem RBP interacting proteins will be identified by BioID and complementary methods. Interacting RNAs will be addressed by newly implemented methods providing additional information about the duration of RNA binding. A few candidate RNAs are expected to be structurally analysed by SAXS and CryoEM permitting to correlate RNA 3D structures to RNA binding and RNA transport signals.
We expect to extend our Bayesian inference and machine learning pipeline to explore different methods for automated feature extraction. We are currently testing CNNs and will trial these further for the identification of mobility triggers. This is currently all sequence-based. Next steps will include the extension to 2D and 3D properties.
We are currently exploring new approaches for sampling 2D and 3D conformations (Nested Sampling and Entropy methods) based on coarse-grained molecular dynamics and thermodynamic descriptions. We will use these methods to predict possible 2D/3D motifs that might be important for recognition by RBPs. Close integration of these methods with the experimental data will be key for elucidating RNA-protein interactions and specificity.