Multi-transcriptomics identifies targets of the endoribonuclease DNE1 and highlights its coordination with decapping.

Decapping is a crucial step in mRNA degradation in eucaryotes and requires the formation of a holoenzyme complex between the decapping enzyme DECAPPING 2 (DCP2) and the decapping enhancer DCP1. In Arabidopsis (Arabidopsis thaliana), DCP1-ASSOCIATED NYN ENDORIBONUCLEASE 1 (DNE1) is a direct protein partner of DCP1. The function of both DNE1 and decapping are necessary to maintain phyllotaxis, the regularity of organ emergence in the apex. In this study, we combined in vivo mRNA editing, RNA degradome sequencing, transcriptomics and small RNA-omics to identify targets of DNE1 and study how DNE1 and DCP2 cooperate in controlling mRNA fate. Our data reveal that DNE1 mainly contacts and cleaves mRNAs in the coding sequence and has sequence cleavage preferences. DNE1 targets are also degraded through decapping, and both RNA degradation pathways influence the production of mRNA-derived small interfering RNAs. Finally, we detected mRNA features enriched in DNE1 targets including RNA G-quadruplexes and translated upstream open reading frames. Combining these four complementary high-throughput sequencing strategies greatly expands the range of DNE1 targets and allowed us to build a conceptual framework describing the influence of DNE1 and decapping on mRNA fate. These data will be crucial to unveil the specificity of DNE1 action and understand its importance for developmental patterning.

Arabidopsis RNA processing body components LSM1 and DCP5 aid in the evasion of translational repression during Cauliflower mosaic virus infection.

Viral infections impose extraordinary RNA stress, triggering cellular RNA surveillance pathways such as RNA decapping, nonsense-mediated decay, and RNA silencing. Viruses need to maneuver among these pathways to establish infection and succeed in producing high amounts of viral proteins. Processing bodies (PBs) are integral to RNA triage in eukaryotic cells, with several distinct RNA quality control pathways converging for selective RNA regulation. In this study, we investigated the role of Arabidopsis thaliana PBs during Cauliflower mosaic virus (CaMV) infection. We found that several PB components are co-opted into viral factories that support virus multiplication. This pro-viral role was not associated with RNA decay pathways but instead, we established that PB components are helpers in viral RNA translation. While CaMV is normally resilient to RNA silencing, dysfunctions in PB components expose the virus to this pathway, which is similar to previous observations for transgenes. Transgenes, however, undergo RNA quality control-dependent RNA degradation and transcriptional silencing, whereas CaMV RNA remains stable but becomes translationally repressed through decreased ribosome association, revealing a unique dependence among PBs, RNA silencing, and translational repression. Together, our study shows that PB components are co-opted by the virus to maintain efficient translation, a mechanism not associated with canonical PB functions.

An extensive survey of phytoviral RNA 3' uridylation identifies extreme variations and virus-specific patterns.

Viral RNAs can be uridylated in eukaryotic hosts. However, our knowledge of uridylation patterns and roles remains rudimentary for phytoviruses. Here, we report global 3' terminal RNA uridylation profiles for representatives of the main families of positive single-stranded RNA phytoviruses. We detected uridylation in all 47 viral RNAs investigated here, revealing its prevalence. Yet, uridylation levels of viral RNAs varied from 0.2% to 90%. Unexpectedly, most poly(A) tails of grapevine fanleaf virus (GFLV) RNAs, including encapsidated tails, were strictly monouridylated, which corresponds to an unidentified type of viral genomic RNA extremity. This monouridylation appears beneficial for GFLV because it became dominant when plants were infected with nonuridylated GFLV transcripts. We found that GFLV RNA monouridylation is independent of the known terminal uridylyltransferases (TUTases) HEN1 SUPPRESSOR 1 (HESO1) and UTP:RNA URIDYLYLTRANSFERASE 1 (URT1) in Arabidopsis (Arabidopsis thaliana). By contrast, both TUTases can uridylate other viral RNAs like turnip crinkle virus (TCV) and turnip mosaic virus (TuMV) RNAs. Interestingly, TCV and TuMV degradation intermediates were differentially uridylated by HESO1 and URT1. Although the lack of both TUTases did not prevent viral infection, we detected degradation intermediates of TCV RNA at higher levels in an Arabidopsis heso1 urt1 mutant, suggesting that uridylation participates in clearing viral RNA. Collectively, our work unveils an extreme diversity of uridylation patterns across phytoviruses and constitutes a valuable resource to further decipher pro- and antiviral roles of uridylation.

A NYN domain protein directly interacts with DECAPPING1 and is required for phyllotactic pattern.

In eukaryotes, general mRNA decay requires the decapping complex. The activity of this complex depends on its catalytic subunit, DECAPPING2 (DCP2), and its interaction with decapping enhancers, including its main partner DECAPPING1 (DCP1). Here, we report that in Arabidopsis thaliana, DCP1 also interacts with a NYN domain endoribonuclease, hence named DCP1-ASSOCIATED NYN ENDORIBONUCLEASE 1 (DNE1). Interestingly, we found DNE1 predominantly associated with DCP1, but not with DCP2, and reciprocally, suggesting the existence of two distinct protein complexes. We also showed that the catalytic residues of DNE1 are required to repress the expression of mRNAs in planta upon transient expression. The overexpression of DNE1 in transgenic lines led to growth defects and a similar gene deregulation signature than inactivation of the decapping complex. Finally, the combination of dne1 and dcp2 mutations revealed a functional redundancy between DNE1 and DCP2 in controlling phyllotactic pattern formation. Our work identifies DNE1, a hitherto unknown DCP1 protein partner highly conserved in the plant kingdom and identifies its importance for developmental robustness.

Ago hook and RNA helicase motifs underpin dual roles for SDE3 in antiviral defense and silencing of nonconserved intergenic regions.

In Arabidopsis thaliana, the putative RNA-helicase SDE3 assists posttranscriptional-gene-silencing (PTGS) amplification by RNA-dependent-RNA-polymerase-6 (RDR6). SDE3 homologs in Drosophila, worm and human contribute to silence viruses, transposons or recently duplicated genes but the underlying mechanisms remain largely unknown. Here, we demonstrate that SDE3 is present with the PTGS effectors AGO1 and AGO2 in higher-order protein complexes owing to a specialized GW-repeat-containing C-terminal domain. We uncover an essential contribution of the RNA-helicase activity and a facilitating role for AGO binding in SDE3 action, which occurs downstream of RDR6. We show that these biochemical properties underpin dual roles for SDE3 in antiviral defense and, unexpectedly, in transposon silencing via a hitherto unanticipated pathway that correlates with DNA methylation, suggesting a continuum of action between PTGS and chromatin-level silencing. We identified endogenous SDE3 targets corresponding to nonconserved intergenic regions, transposons and recently evolved pseudogenes, unraveling striking functional convergences among plant and metazoan SDE3 pathways.

NERD, a plant-specific GW protein, defines an additional RNAi-dependent chromatin-based pathway in Arabidopsis.

In Arabidopsis, transcriptional gene silencing (TGS) can be triggered by 24 nt small-interfering RNAs (siRNAs) through the RNA-directed DNA methylation (RdDM) pathway. By functional analysis of NERD, a GW repeat- and PHD finger-containing protein, we demonstrate that Arabidopsis harbors a second siRNA-dependent DNA methylation pathway targeting a subset of nonconserved genomic loci. The activity of the NERD-dependent pathway differs from RdDM by the fact that it relies both on silencing-related factors previously implicated only in posttranscriptional gene silencing (PTGS), including RNA-DEPENDENT RNA POLYMERASE1/6 and ARGONAUTE2, and most likely on 21 nt siRNAs. A central role for NERD in integrating RNA silencing and chromatin signals in transcriptional silencing is supported by data showing that it binds both to histone H3 and AGO2 proteins and contributes to siRNA accumulation at a NERD-targeted locus. Our results unravel the existence of a conserved chromatin-based RNA silencing pathway encompassing both PTGS and TGS components in plants.

Echocardiographic evidence of left ventricular untwisting-filling interplay.

BACKGROUND: Left ventricular untwisting generates an early diastolic intraventricular pressure gradient (DIVPG) than can be quantified by echocardiography. We sought to confirm the quantitative relationship between peak untwisting rate and peak DIVPG in a large adult population. METHODS: From our echocardiographic database, we retrieved all the echocardiograms with a normal left ventricular ejection fraction, for whom color Doppler M-Mode interrogation of mitral inflow was available, and left ventricular untwisting rate was measurable using speckle tracking. Standard indices of left ventricular early diastolic function were assessed by Doppler (peaks E, e' and Vp) and speckle tracking (peak strain rate Esr). Load dependency of DIVPG and untwisting rate was evaluated using a passive leg raising maneuver. RESULTS: We included 154 subjects, aged between 18 to 77 years old, 63% were male. Test-retest reliability for color Doppler-derived DIVPG measurements was good, the intraclass correlation coefficients were 0.97 [0.91-0.99] and 0.97 [0.67-0.99] for intra- and inter-observer reproducibility, respectively. Peak DIVPG was positively correlated with peak untwisting rate (r = 0.73, P <  0.001). On multivariate analysis, peak DIVPG was the only diastolic parameter that was independently associated with untwisting rate. Age and gender were the clinical predictive factors for peak untwisting rate, whereas only age was independently associated with peak DIVPG. Untwisting rate and DIVPG were both load-dependent, without affecting their relationship. CONCLUSIONS: Color Doppler-derived peak DIVPG was quantitatively and independently associated with peak untwisting rate. It thus provides a reliable flow-based index of early left ventricular diastolic function.

The UPF1 interactome reveals interaction networks between RNA degradation and translation repression factors in Arabidopsis.

The RNA helicase UP-FRAMESHIFT (UPF1) is a key factor of nonsense-mediated decay (NMD), a mRNA decay pathway involved in RNA quality control and in the fine-tuning of gene expression. UPF1 recruits UPF2 and UPF3 to constitute the NMD core complex, which is conserved across eukaryotes. No other components of UPF1-containing ribonucleoproteins (RNPs) are known in plants, despite its key role in regulating gene expression. Here, we report the identification of a large set of proteins that co-purify with the Arabidopsis UPF1, either in an RNA-dependent or RNA-independent manner. We found that like UPF1, several of its co-purifying proteins have a dual localization in the cytosol and in P-bodies, which are dynamic structures formed by the condensation of translationally repressed mRNPs. Interestingly, more than half of the proteins of the UPF1 interactome also co-purify with DCP5, a conserved translation repressor also involved in P-body formation. We identified a terminal nucleotidyltransferase, ribonucleases and several RNA helicases among the most significantly enriched proteins co-purifying with both UPF1 and DCP5. Among these, RNA helicases are the homologs of DDX6/Dhh1, known as translation repressors in humans and yeast, respectively. Overall, this study reports a large set of proteins associated with the Arabidopsis UPF1 and DCP5, two components of P-bodies, and reveals an extensive interaction network between RNA degradation and translation repression factors. Using this resource, we identified five hitherto unknown components of P-bodies in plants, pointing out the value of this dataset for the identification of proteins potentially involved in translation repression and/or RNA degradation.

Trans-species synthetic gene design allows resistance pyramiding and broad-spectrum engineering of virus resistance in plants.

To infect plants, viruses rely heavily on their host's machinery. Plant genetic resistances based on host factor modifications can be found among existing natural variability and are widely used for some but not all crops. While biotechnology can supply for the lack of natural resistance alleles, new strategies need to be developed to increase resistance spectra and durability without impairing plant development. Here, we assess how the targeted allele modification of the Arabidopsis thaliana translation initiation factor eIF4E1 can lead to broad and efficient resistance to the major group of potyviruses. A synthetic Arabidopsis thaliana eIF4E1 allele was designed by introducing multiple amino acid changes associated with resistance to potyvirus in naturally occurring Pisum sativum alleles. This new allele encodes a functional protein while maintaining plant resistance to a potyvirus isolate that usually hijacks eIF4E1. Due to its biological functionality, this synthetic allele allows, at no developmental cost, the pyramiding of resistances to potyviruses that selectively use the two major translation initiation factors, eIF4E1 or its isoform eIFiso4E. Moreover, this combination extends the resistance spectrum to potyvirus isolates for which no efficient resistance has so far been found, including resistance-breaking isolates and an unrelated virus belonging to the Luteoviridae family. This study is a proof-of-concept for the efficiency of gene engineering combined with knowledge of natural variation to generate trans-species virus resistance at no developmental cost to the plant. This has implications for breeding of crops with broad-spectrum and high durability resistance using recent genome editing techniques.

Argonaute quenching and global changes in Dicer homeostasis caused by a pathogen-encoded GW repeat protein.

In plants and invertebrates, viral-derived siRNAs processed by the RNaseIII Dicer guide Argonaute (AGO) proteins as part of antiviral RNA-induced silencing complexes (RISC). As a counterdefense, viruses produce suppressor proteins (VSRs) that inhibit the host silencing machinery, but their mechanisms of action and cellular targets remain largely unknown. Here, we show that the Turnip crinckle virus (TCV) capsid, the P38 protein, acts as a homodimer, or multiples thereof, to mimic host-encoded glycine/tryptophane (GW)-containing proteins normally required for RISC assembly/function in diverse organisms. The P38 GW residues bind directly and specifically to Arabidopsis AGO1, which, in addition to its role in endogenous microRNA-mediated silencing, is identified as a major effector of TCV-derived siRNAs. Point mutations in the P38 GW residues are sufficient to abolish TCV virulence, which is restored in Arabidopsis ago1 hypomorphic mutants, uncovering both physical and genetic interactions between the two proteins. We further show how AGO1 quenching by P38 profoundly impacts the cellular availability of the four Arabidopsis Dicers, uncovering an AGO1-dependent, homeostatic network that functionally connects these factors together. The likely widespread occurrence and expected consequences of GW protein mimicry on host silencing pathways are discussed in the context of innate and adaptive immunity in plants and metazoans.

SIMUS3: An open-source simulator for 3-D ultrasound imaging.

BACKGROUND AND OBJECTIVE: Computational Ultrasound Imaging (CUI) has become increasingly popular in the medical ultrasound community, facilitated by free simulation software. These tools enable the design and exploration of transmit sequences, transducer arrays, and signal processing. We recently introduced SIMUS, a frequency-based ultrasound simulator within the open-source MUST toolbox, which offers numerical advantages and allows easy consideration of frequency-dependent factors. In response to the growing interest in simulating ultrasound imaging with 2-D matrix arrays, we present 3-D versions, PFIELD3 and SIMUS3. METHOD: The linear acoustic equations driving these functions are described, with theoretical assumptions reviewed for user guidance. RESULTS: Comparative analyses with Field II, using a 32×32 element 3-MHz matrix array, highlight the performance of PFIELD3 and SIMUS3 under various transmission conditions. CONCLUSIONS: This work extends the capabilities of existing CUI tools and provides researchers with valuable resources for advanced ultrasound simulations.

Reply to "Comment on "So you think you can DAS? A viewpoint on delay-and-sum beamforming" - Think twice before f-numbering", published by M.F. Schiffner and G. Schmitz.

In a 2021 paper, we delved into the details of delay-sum beamforming (DAS) in high-frame-rate ultrasound for medical imaging [1]. We also proposed a simple and fast method of determining an f-number, which is based on the directivity of the transducer elements. In their comment, Martin F. Schiffner and Georg Schmitz argue that we mistakenly link image quality enhancement to the reduction of measurement noise. They disapprove our proposed f-number, claiming it deteriorates the signal-to-noise ratio (SNR). Based on their previous work [2], they also highlight that the f-number should be derived from the grating lobe angles. In this reply, we explain their error in the SNR argument. We also illustrate the potential drawbacks of exclusively relying on grating lobes to establish an f-number with a DAS, suggesting that alternative approaches might be worthy of consideration.

Efficient Stratified 3-D Scatterer Sampling for Freehand Ultrasound Simulation.

Ultrasound image simulation is a well-explored field with the main objective of generating realistic synthetic images, further used as ground truth for computational imaging algorithms or for radiologists' training. Several ultrasound simulators are already available, most of them consisting in similar steps: 1) generate a collection of tissue mimicking individual scatterers with random spatial positions and random amplitudes; 2) model the ultrasound probe and the emission and reception schemes; and 3) generate the radio frequency (RF) signals resulting from the interaction between the scatterers and the propagating ultrasound waves. This article is focused on the first step. To ensure fully developed speckle, a few tens of scatterers by resolution cell are needed, demanding to handle high amounts of data (especially in 3-D) and resulting into important computational time. The objective of this work is to explore new scatterer spatial distributions, with application to multiple coherent 2-D slice simulations from 3-D volumes. More precisely, lazy evaluation of pseudorandom schemes proves them to be highly computationally efficient compared with uniform random distribution commonly used. We also propose an end-to-end method from the 3-D tissue volume to resulting ultrasound images using coherent and 3-D-aware scatterer generation and usage in a real-time context.

Boosting Cardiac Color Doppler Frame Rates with Deep Learning.

Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughout the cardiac cycle, thereby compromising a comprehensive analysis of ventricular filling. Concurrently, deep learning is demonstrating promising outcomes in post-processing of echocardiographic data for various applications. This work explores the use of deep learning models for intracardiac Doppler velocity estimation from a reduced number of filtered I/Q signals. We used a supervised learning approach by simulating patient-based cardiac color Doppler acquisitions and proposed data augmentation strategies to enlarge the training dataset. We implemented architectures based on convolutional neural networks. In particular, we focused on comparing the U-Net model and the recent ConvNeXt models, alongside assessing real-valued versus complex-valued representations. We found that both models outperformed the state-of-the-art autocorrelator method, effectively mitigating aliasing and noise. We did not observe significant differences between the use of real and complex data. Finally, we validated the models on in vitro and in vivo experiments. All models produced quantitatively comparable results to the baseline and were more robust to noise. ConvNeXt emerged as the sole model to achieve high-quality results on in vivo aliased samples. These results demonstrate the interest of supervised deep learning methods for Doppler velocity estimation from a reduced number of acquisitions.

Heat stress promotes Arabidopsis AGO1 phase separation and association with stress granule components.

In Arabidopsis thaliana, ARGONAUTE1 (AGO1) plays a central role in microRNA (miRNA) and small interfering RNA (siRNA)-mediated silencing. AGO1 associates to the rough endoplasmic reticulum to conduct miRNA-mediated translational repression, mRNA cleavage, and biogenesis of phased siRNAs. Here, we show that a 37°C heat stress (HS) promotes AGO1 protein accumulation in cytosolic condensates where it colocalizes with components of siRNA bodies and of stress granules. AGO1 contains a prion-like domain in its poorly characterized N-terminal Poly-Q domain, which is sufficient to undergo phase separation independently of the presence of SGS3. HS only moderately affects the small RNA repertoire, the loading of AGO1 by miRNAs, and the signatures of target cleavage, suggesting that its localization in condensates protects AGO1 rather than promoting or impairing its activity in reprogramming gene expression during stress. Collectively, our work sheds new light on the impact of high temperature on a main effector of RNA silencing in plants.

Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping.

Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme by utilizing physics-informed neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach. When evaluated on simulated color Doppler images derived from a patient-specific computational fluid dynamics model and in vivo Doppler acquisitions, both approaches demonstrate comparable reconstruction performance to the original iVFM algorithm. The efficiency of PINNs is boosted through dual-stage optimization and pre-optimized weights. On the other hand, the nnU-Net method excels in generalizability and real-time capabilities. Notably, nnU-Net shows superior robustness on sparse and truncated Doppler data while maintaining independence from explicit boundary conditions. Overall, our results highlight the effectiveness of these methods in reconstructing intraventricular vector blood flow. The study also suggests potential applications of PINNs in ultrafast color Doppler imaging and the incorporation of fluid dynamics equations to derive biomarkers for cardiovascular diseases based on blood flow.

Phase Unwrapping of Color Doppler Echocardiography Using Deep Learning.

Color Doppler echocardiography is a widely used noninvasive imaging modality that provides real-time information about intracardiac blood flow. In an apical long-axis view of the left ventricle, color Doppler is subject to phase wrapping, or aliasing, especially during cardiac filling and ejection. When setting up quantitative methods based on color Doppler, it is necessary to correct this wrapping artifact. We developed an unfolded primal-dual network (PDNet) to unwrap (dealias) color Doppler echocardiographic images and compared its effectiveness against two state-of-the-art segmentation approaches based on nnU-Net and transformer models. We trained and evaluated the performance of each method on an in-house dataset and found that the nnU-Net-based method provided the best dealiased results, followed by the primal-dual approach and the transformer-based technique. Noteworthy, the PDNet, which had significantly fewer trainable parameters, performed competitively with respect to the other two methods, demonstrating the high potential of deep unfolding methods. Our results suggest that deep learning (DL)-based methods can effectively remove aliasing artifacts in color Doppler echocardiographic images, outperforming DeAN, a state-of-the-art semiautomatic technique. Overall, our results show that DL-based methods have the potential to effectively preprocess color Doppler images for downstream quantitative analysis.

Ultrafast Cardiac Imaging Using Deep Learning for Speckle-Tracking Echocardiography.

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this article, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle-tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e., high-quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on nonsimulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography.

SIMUS: An open-source simulator for medical ultrasound imaging. Part I: Theory & examples.

BACKGROUND AND OBJECTIVE: Computational ultrasound imaging has become a well-established methodology in the ultrasound community. Simulations of ultrasound sequences and images allow the study of innovative techniques in terms of emission strategy, beamforming, and probe design. There is a wide spectrum of software dedicated to ultrasound imaging, each having its specificities in its applications and the numerical method. METHODS: We describe in this two-part paper a new ultrasound simulator (SIMUS) for MATLAB, which belongs to the MATLAB UltraSound Toolbox (MUST). The SIMUS software simulates acoustic pressure fields and radiofrequency RF signals for uniform linear or convex probes. SIMUS is an open-source software whose features are 1) ease of use, 2) time-harmonic analysis, 3) pedagogy. The main goal was to offer a comprehensive turnkey tool, along with a detailed theory for pedagogical and research purposes. RESULTS: This article describes in detail the underlying linear theory of SIMUS and provides examples of simulated acoustic fields and ultrasound images. The accompanying article (part II) is devoted to the comparison of SIMUS with several software packages: Field II, k-Wave, FOCUS, and the Verasonics simulator. The MATLAB open codes for the simulator SIMUS are distributed under the terms of the GNU Lesser General Public License, and can be downloaded from https://www.biomecardio.com/MUST. CONCLUSIONS: The simulations described in this part and in the accompanying paper (Part II) show that SIMUS can be used for realistic simulations in medical ultrasound imaging.