In vivo screens to identify such factors are challenging and time consuming. In addition, the scored phenotype is usually limited to survival and/or viral load. Transgenic viral replicons are valuable tools to overcome these limitations and screen for novel antiviral factors. Here we describe transgenic Drosophila melanogaster lines encoding a Flock House Virus-derived replicon (FHV∆B2eGFP), expressing GFP as a reporter of viral replication. This replicon is efficiently controlled by the siRNA pathway in most somatic tissues, with GFP fluorescence providing a reliable marker for the activity of antiviral RNAi. Interestingly, in follicular somatic cells (FSC) of ovaries, this replicon is still partially repressed in an siRNA independent manner. We did not detect replicon derived Piwi-interacting RNAs in FSCs and identified 31 differentially expressed genes between restrictive and permissive FSCs. Altogether, our results uncovered a yet unidentified RNAi-independent mechanism controlling FHV replication in FSCs of ovaries and validate the FHV∆B2eGFP replicon as a tool to screen for novel tissue specific antiviral mechanisms.The small interfering RNA (siRNA) pathway is the main and best studied invertebrate antiviral response. Other poorly characterized protein based antiviral mechanisms also contribute to the control of viral replication in insects. In addition, it remains unclear whether tissue specific factors contribute to RNA and protein-based antiviral immunity mechanisms.  Viral infections lead every year to significant losses of human lives, livestock and plant crops, and are responsible for life-threatening pandemics and emerging epidemics, such as influenza, HIV, Ebola or MERS (Middle East Respiratory Syndrome). Viruses are obligatory intracellular pathogens, with a simple RNA- or DNA-based genome sheltered in a small protein capsid, enveloped or not by cellular lipids. They hijack the cellular machinery for all stages of their life-cycle.Drosophila melanogaster is RNA interference (RNAi)  of the viral genetic material, upon perfect matching of the vs.iRNAs with the viral genome or viral transcripts  mobilization  being upregulated upon infection by more than one virus. Other cell stress and signaling pathways, such as autophagy or ubiquitination, control viruses, acting either as restriction or pro-viral factors  (Coleoptera: Scarabaeidae), the alphanodavirus FHV is a model insect virus that has been used to decipher antiviral immunity in Drosophila  from 3-4 days old flies were dissected, collected into Trizol , snap frozen in dry ice and kept at -80° until subsequent RNA preparation. RNA extraction from Trizol stored ovaries was done following manufacturers’ instructions. Quality of extracted RNA was assessed in Agilent Bioanalyzer 2100 , using the Eukaryote Total RNA Nano assay.Libraries for small RNA sequencing were prepared using the Illumina TruSeq Small RNA library preparation kit , according to manufacturers’ protocol, with the following modifications: after ligation of the 3′ adapter, 20 pmol of a terminator oligonucleotide was added to deplete the libraries from 2S ribosomal RNA (rRNA), samplesLibrary quality was assessed in Agilent Bioanalyzer 2100 (Agilent), using the High Sensitivity DNA assay. Libraries were sequenced in Illumina HiSeq 4000 (Illumina), with a single-end 50 bp read-length strategy. Sequencing was performed by the IGBMC Microarray and Sequencing platform, a member of the ‘France Génomique’ consortium (ANR-10-INBS-0009).gypsy were normalized against the total number of reads excluding ribosomal RNA and micro RNA mapping reads. Other normalization strategies yielded qualitatively identical results.After quality trimming and adapter removal using Trimmomatic v0.36 (https://dgrc.bio.indiana.edu/product/View?product=190) without added fly extract. FACS analysis and cell sorting was done on the FACS Aria II or FACS Aria Fusion , configured to detect DAPI, GFP and mCherry according to the manufacturers’ instructions. Alive cells, not incorporating DAPI, were sorted into mCherry-/GFP-, mCherry+/GFP- and mCherry+/GFP+ populations in 0.5 ml of OSS cell medium. Gating was done in real time using FACS Diva . FACS sorting was done at the Flow Cytometry platform at Institut de Génétique et de Biologie Moléculaire et Cellulaire . Cells were centrifuged at 1000 X g for 5 min, resuspended in 0.4 ml of Trizol, snap frozen in dry ice and kept at -80° before subsequent RNA preparation.Dissociation of follicle somatic cells were done according to RNA extraction from Trizol stored cells was done following manufacturers’ instructions. Libraries for total RNA sequencing were then prepared using the NUGEN Ovation Drosophila RNA-seq system , according to the manufacturers’ instructions. cDNA was fragmented using a Covaris S-series System , to obtain a median fragment size of 300-bp. Library quality was assessed in Agilent Bioanalyzer 2100 (Agilent), using the High Sensitivity DNA assay. Libraries were sequenced in Illumina HiSeq 4000 , with a single-end 50 base-pairs (bp) read-length strategy. Sequencing was performed by the IGBMC Microarray and Sequencing platform.p-value < 0.05 were considered differentially expressed.After quality trimming and adapter removal using Trimmomatic, reads were mapped using STAR v2.5.2b , fixed for 20-25 min in a 1:4 solution of 1% methanol-free formaldehyde (ThermoFisher Scientific) in 1X PBTX:Heptane, after dissociation by pipetting up and down with a 1000 ul pipette, and washed three times in PBTX for 5 min.For immunostaining, samples were blocked for 30 min in PBTX+1% BSA, and primary antibodies, diluted in PBTX+1% BSA were incubated overnight at 4°. After washing 3 times with PBTX+0.1% BSA, secondary antibodies, diluted in PBTX+0.1% BSA were incubated for one hour at room temperature. Antibodies and dilutions used in this study are mouse anti-GFP , rabbit anti-FHV-RdRP , mouse monoclonal anti-dsRNA . After washing 3 times with PBTX+0.1% BSA, secondary antibodies from Invitrogen were goat anti-rabbit or anti-mouse with Alexa-564 and Alexa-594. Stained ovaries were mounted in Vectashield with DAPI  on glass slides.in-situ hybridization, Custom Stellaris FISH Probes were designed against the first 2600 nucleotides of the sense strand of FHV-RNA1 by utilizing the Stellaris RNA FISH Probe Designer  available online at www.biosearchtech.com/stellarisdesigner (version 4.2). The samples were hybridized with the FISH Probe set labeled with Quasar 640 , adapting the manufacturer’s instruction for hybridization of Drosophila imaginal discs (available online at www.biosearchtech.com/stellarisprotocols) to a tube format. Briefly, fixed ovaries were incubated with Stellaris Wash Buffer A for 5 min, and hybridized with probes (125 nM) in Stellaris Hybridization Buffer in the dark at 37° overnight. After washing twice with Wash Buffer A at 37° in the dark for 30 min and once with Stellaris Wash Buffer B for 5 min at room temperature, samples were mounted in Vectashield with DAPI on glass slides.For fluorescent Confocal images were taken on a Leica LSM700 or 780 confocal microscope; widefield and stereromicroscope images were taken on a Zeiss Axiovert 200M and a Zeiss Stereo Discovery V12, respectively. Images were processed with ImageJ software.https://doi.org/10.25387/g3.7423376.Strains and reagents (described in File S1) and analysis scripts are available upon request. Supplementary figures S1-5, tables S1-5 and File S1 are available at figshare. Raw sequencing data are available from the European Nucleotide Archives with references PRJEB28601  did not yield viable progeny. The use of tissue specific drivers allowed to test the control of the replicon in different target tissues.The FHV∆B2eGFP replicon was placed under the control of a minimal heat-shock promoter and yeast UASGMR-Gal4 driver did not produce detectable GFP fluorescence, even though the driver efficiently drove expression of a UAS-mCherry∷NLS reporter in this tissue , a strong and homogeneous green fluorescence was observed in the eyes , we observed GFP signal in the whole body in the absence of driver, reflecting baseline leaky expression amplified by the RdRP  led to sterile progeny with rudimentary ovaries (not shown). When testing the replicon with a follicular somatic cell (FSC) specific driver, in a siRNA impaired background , we observed only a partial derepression of GFP expression. Fluorescence was consistently observed in restricted patches  and FHV-RdRP expression by immunostaining. In both cases we detected expression in all cells, ruling out a transcriptional or translational effect  and (-) strands was observed. This sharp peak was not observed in libraries prepared from dcr2 null mutant flies, confirming that it corresponds to virus-derived siRNAs  . In none of these genetic backgrounds however did we observe small RNAs bearing the signature for canonical piRNAs, such as 24-28nt size range or enrichment for U at the 5′ end, although piRNAs targeting TEs, such as gypsy, were present in the libraries , highlighting the importance of the siRNA pathway in the processing of TEs , there was a low number of reads mapping to the replicon, indicative of baseline leaky expression (scripts)  showed t-) and permissive- (GFP+) enriched pools  allowing gypsy to integrate in the genome (i) the observation that an FSC derived cell line (the OSS cell line), produces virus derived piRNAs from FHV and DCV (ii) the proposed role of the piRNA pathway in the control of viruses , whose main effector proteins are the same as the antiviral siRNA pathway (CG13659 and the Drosophila insulin-like peptide 8 (dIlp8), are both expressed at higher levels in the permissive cell fraction. Orthologs of CG13659 are present in most arthropods and in D. melanogaster it is induced upon systemic infection by FHV (flybase.org), but its role in this tissue has not yet been defined. Our results suggest that it could serve as a cytokine issued by permissive cells to induce restriction in neighboring cells. Of note, constitutively expressed restriction factors, regulated at the post-transcriptional level, may also control infection. In this regard, the identified DEGs could be linked to cellular alterations triggered by viral RNA replication (e.g., changes in pathways associated with the mitochondrial compartment (If not RNA-based, a protein-based mechanism may be involved in the restriction of the FHV replicon. The transcriptome data of sorted FSCs reported here will be a useful resource to identify candidate genes involved in this mechanism. These candidates may reflect a response to infection and the induction of an antiviral state. For example, C. elegans, recently identified a novel, RNAi-independent restriction mechanism for RNA viruses based on the 3′uridylation of viral transcripts by terminal uridyl transferases (in vivo screens with injected viruses are time consuming, bypass the natural route of infection for most viruses, and the scored phenotype is usually limited to survival and/or whole-body viral load. Unbiased screens using infectious viruses targeting different tissues, routes of infection and developmental stages present even greater logistic and standardization challenges. The FHV∆B2eGFP lines reported here represent a new addition in the Drosophila toolkit to address the genetic basis of tissue specific antiviral immunity in this model insect.Whatever the mechanism, the transgenic FHV∆B2eGFP lines we describe here provide a useful tool for unbiased genetic screens aimed at characterizing it. Such unbiased genetic screens in another model organism, the nematode


RNA structure often plays a key role in determining the function of non-coding and coding transcripts. Whereas transcript abundance can help to identify transcripts of interest in or under some conditions, the RNA structure can help to explain how the transcript might function. Hence, identifying RNA structures encoded in genomic sequence is of great interest. Since RNA molecules are often folded intensively into structures, the functional important RNA structure is typically conserved and can be more reliably detected by considering the conservation of base pairs rather than the folding of a single sequence . AlthougThe detection of RNA secondary structures throughout multiple organisms can be achieved through different strategies: 1) excising the structure from sequence-based alignments, 2) aligning the predicted structure of individual sequences, or 3) simultaneously optimizing structure and alignment. This is reviewed in reference [In reference , Ivanov Complementing the simultaneous fold and align strategy, which has a relatively high computational cost, using sequence-based alignments combined with a sliding window approach is faster. This strategy is used by Thiel et al. in  who, in Another key challenge in detecting RNA structures is searching for those who change very dramatically in size and thereby structure over evolutionary time. A prime example is telomerase RNA, which is difficult to detect through both sequence and structure homology due to its variation. Waldi et al.  address High-throughput methods for RNA structure determination  provide Structural motifs are key components in the underlying architecture of the RNA structure . In partMany RNA molecules function by binding to proteins. The interactions between the RNA and protein give rise to the specific formation of molecular structures. Even though experimental data can provide the most reliable insight, computational analyses can be sufficient to point out relevant directions and to form hypotheses. In reference , Nithin Finally, some transcripts function through their ability to form circles and tens of thousands of circular RNAs have already been reported . Detecti


Though subjective, poor self-rated health (SRH) has consistently been shown to predict cardiovascular disease (CVD). The underlying mechanism is unclear. This study evaluates the associations of SRH with biomarkers for CVD, aiming to explore potential pathways between poor SRH and CVD.Based on the Malmö Diet and Cancer Cardiovascular Cohort study, a targeted proteomics approach was used to assess the associations of SRH with 88 cardiovascular risk proteins, measured in plasma from 4521 participants without CVD. The false discovery rate (FDR) was controlled using the Benjamini and Hochberg method. Covariates taken into consideration were age, sex, traditional CVD risk factors , comorbidity, life-style and psycho-social factors .p = 0.016) and C–C motif chemokine 20  were significantly associated with SRH.Age and sex-adjusted associations with SRH was found for 34 plasma proteins. Nine of them remained significant after adjustments for traditional CVD risk factors. After further adjustment for comorbidity, life-style and psycho-social factors, only leptin (β = − 0.035, corrected Poor SRH was associated with raised concentrations of many plasma proteins. However, the relationships were largely attenuated by adjustments for CVD risk factors, comorbidity and psycho-social factors. Leptin and CCL20 were associated with poor SRH in the present study and could potentially be involved in the SRH–CVD link. How people rate their own health is a subjective but surprisingly sensitive and reliable assay for evaluation of general well-being. A meta-analysis of 22 prospective, community-based cohort studies has reported that compared to participants with “excellent” self-rated health (SRH), mortality risk among those who rated their health as “poor” has almost doubled even after adjustment for co-morbidity, depression, and cognitive and functional status . The assThe mechanisms underlying the association between poor SRH and CVD remain unclear. SRH is a comprehensive indicator of health status that closely associates with material, behavioral, personality and psycho-social factors , 11. TheIn this study, we aimed to use a recently-developed targeted proteomics approach to explore the change in cardiovascular proteomics associated with SRH. Being highly sensitive and specific, the implement of this proteomics methodology may provide a novel insight into biologically plausible mechanistic pathways between poor SRH and CVD.The Malmö Diet and Cancer (MDC) study is a large prospective cohort study among residents living in the Swedish town of Malmö . BetweenWritten informed consent have been obtained from all participants. The study has been approved by the Regional Ethical Review Board in Lund, Sweden (LU 51/90) and was carried out in accordance with the Helsinki Declaration.Self-rated health was assessed on an ordinal scale ranging from 1 to 7, based on subjects’ answers to a questionnaire item: “How do you feel right now, physically and mentally, with respect to your health and well-being?”. While “1” indicates “feel very bad, could not feel worse”, “7” indicates “feel very well, could not feel better”.® Multiplex CVD I96×96 reagent kit [® Biomark™ HD real-time PCR platform, making the technology of significantly higher specificity and sensitivity than traditional multiplex immunoassays [http://www.olink.com). Four proteins had a call rate < 75%: extracellular newly identified RAGE-binding , beta-nerve growth factor , IL-4 (n = 12), and B-type natriuretic peptide  were removed from main analyses . This resulted in 88 proteins for final analyses. Proteins with levels below the lower limit of detection (LOD) were considered to have a value of LOD/2.Fasting EDTA-plasma samples were stored at − 80 °C immediately after collection until protein analysis. Ninety-two CVD-related proteins were simultaneously measured by the SciLifeLab analysis service  using Proseekgent kit . The reagent kit , 31. Onlnoassays –31. Quannoassays . The mea2). Blood pressure (mmHg) was measured after the participants had rested for 10 min in a supine position. Blood samples were drawn after an overnight fast. LDL concentration (mmol/L) was estimated using Friedewald’s formula [Information on alcohol consumption and smoking, medication (anti-hypertensive and psychiatric medication), comorbidity , physical activity, education level, living alone, average sleep duration, and unemployment were obtained from a health questionnaire completed by the participants and their 7-day personal diary. Smoking was treated as a two-category variable: smokers or non-smokers. Men with alcohol intake > 40 g/day or women with alcohol intake > 30 g/day were considered to have high alcohol consumption. Use of anti-hypertensive and psychiatric drug was identified based on the drugs listed by participants. Anatomical Therapeutic Chemical Classification codes N05 and N06 were used for psychiatric drugs and C02, C03, C07 and C08 were used for anti-hypertensive drugs. Subjects with self-reported diabetes or diabetes treatment or with a fasting venous whole blood glucose higher than 6.1 mmol/L (corresponding to 7.0 mmol/L when using fasting plasma glucose to diagnose diabetes ) were co formula .Incidence of CVD and mortality up to December 31st, 2016, was monitored by data linkage with the Swedish in-patient register and the cause of death register. Incident CVD included new cases of coronary event  or stroke diagnosed according to the International Classification of Diseases 9th or 10th revision .Protein values were Z-score standardized in all analyses. Pearson’s partial correlation tests were conducted between each pair of proteins after adjusting for age and sex. Baseline characteristics for subjects included in this study (n = 4521) are demonstrated for those with SRH higher or lower than the median level (SRH = 5). Continuous variables are all normally distributed and thus presented as mean ± SD, while categorical variables are presented as percentages. Comparisons between the two groups were performed using univariate Chi-square or student t-tests. The hazard ratio of SRH for incident CVD  was calculated using Cox proportional hazard regression models, with time-scale defined as time to follow-up until incident CVD , emigration, death or end of follow-up (2016-12-31). Since only few participants scored their health as 1 or 2 , participants with an SRH value of 1–3 were combined into one group.p values were corrected for false discovery rate using the Benjamini and Hochberg method [p value of < 0.05 was considered as statistically significant. All analyses were performed using the Statistical Analysis System version 9.3 for Windows .Linear regression models were separately conducted for every single protein to investigate its association with SRH (as the independent variable). In primary analyses, only age and sex were adjusted for and forest plot was used to visualize the associations. In a second model, traditional cardiovascular risk factors were included . Finally, comorbidity, life-style and psycho-social risk factors were added to the model . The covariates were examined for multi-collinearity using the Variance Inflation Factor (VIF) and it was found that VIF was < 2 for all independent variables. Since multiple testing was involved in both analyses, g method . For proThe baseline characteristics of the study population according to their SRH values are shown in Table p for trend < 0.0001) and a slightly elevated risk of mortality  compared with those with a SRH score of 7. In total, results from the Cox regression analyses  were found of SRH with 42 of the 88 proteins examined . When comorbidity and psycho-social factors were added to the model, eight plasma proteins were related to SRH with nominal significance; two of them  were significant after correction for multiple testing; Table After further adjustment for traditional cardiovascular risk factors  nine proteins remained significant after correcting for multiple testing , while the association between CCL20 and SRH was modified by smoking (interaction p = 0.006). Results of the corresponding subgroup analyses are presented in Table Interaction tests showed that the adjusted association between leptin and SRH was modified by sex, BMI, alcohol consumption, and systolic blood pressure , while the relationships with poor SRH were strong for e.g. smoking, diabetes and anti-hypertensive medication Table . It is nInflammation has been firmly established as crucial to the development of CVD . The actAfter multivariate adjustment, an association between poor SRH and CCL20 was still observed in our study. CCL20 is a recently discovered CC chemokine which functions together with its selective receptor CCR6 to mediate the chemoattraction of immature dendritic cells and effector and memory T- and B-cells . Whereasp = 0.019). Leptin is an adipose-derived hormone that can control body weight by inhibiting appetite and increasing energy expenditure [2 (interaction p < 0.001). It is therefore speculated that for people with poor SRH, obesity accompanied by elevated leptin concentration and leptin resistance may contribute to some extent the subsequent cardiovascular risk [The coexistence of increased circulating leptin and depression has been previously demonstrated, though in studies with small sample sizes and clinical heterogeneity , 47. In enditure . Howeverenditure . Leptin enditure . In the lar risk . As an olar risk –56. In alar risk  and deprlar risk . Leptin lar risk . Thus, aStrengths of this study included a large sample size and the application of a highly sensitive and specific proteomics approach. However, the reliability of our results is limited by lack of replication samples. The 88 proteins measured in this study only constitute a minor subpopulation of the CVD-related proteins, and their associations with SRH were only cross-sectionally investigated. Since SRH appears to be a summative assessment of various aspects of health, we cannot rule out residual confounding from some aspects that can hardly be measured, or reverse causality. Therefore, the exact biological mechanisms underlying the association between SRH and cardiovascular outcomes cannot be illustrated in the present study. Nevertheless, we supported a biological change associated with subjective measurement, which helps to explain the predictive value of SRH for future outcomes.Poor SRH was associated with raised concentrations of many plasma proteins. However, the relationships were largely attenuated by adjustments for traditional CVD risk factors and factors related to comorbidity, life-style and psycho-social factors. Leptin and CCL20 were associated with poor SRH after multiple adjustments and could potentially be involved in the SRH–CVD link.


A velocity offset error in phase contrast cardiovascular magnetic resonance (CMR) imaging is a known problem in clinical assessment of flow volumes in vessels around the heart. Earlier studies have shown that this offset error is clinically relevant over different systems, and cannot be removed by protocol optimization. Correction methods using phantom measurements are time consuming, and assume reproducibility of the offsets which is not the case for all systems. An alternative previously published solution is to correct the in-vivo data in post-processing, interpolating the velocity offset from stationary tissue within the field-of-view. This study aims to validate this interpolation-based offset correction in-vivo in a multi-vendor, multi-center setup.Data from six 1.5 T CMR systems were evaluated, with two systems from each of the three main vendors. At each system aortic and main pulmonary artery 2D flow studies were acquired during routine clinical or research examinations, with an additional phantom measurement using identical acquisition parameters. To verify the phantom acquisition, a region-of-interest (ROI) at stationary tissue in the thorax wall was placed and compared between in-vivo and phantom measurements. Interpolation-based offset correction was performed on the in-vivo data, after manually excluding regions of spatial wraparound. Correction performance of different spatial orders of interpolation planes was evaluated.A total of 126 flow measurements in 82 subjects were included. At the thorax wall the agreement between in-vivo and phantom was − 0.2 ± 0.6 cm/s. Twenty-eight studies were excluded because of a difference at the thorax wall exceeding 0.6 cm/s from the phantom scan, leaving 98. Before correction, the offset at the vessel as assessed in the phantom was − 0.4 ± 1.5 cm/s, which resulted in a − 5 ± 16% error in cardiac output. The optimal order of the interpolation correction plane was 1st order, except for one system at which a 2nd order plane was required. Application of the interpolation-based correction revealed a remaining offset velocity of 0.1 ± 0.5 cm/s and 0 ± 5% error in cardiac output.This study shows that interpolation-based offset correction reduces the offset with comparable efficacy as phantom measurement phase offset correction, without the time penalty imposed by phantom scans.4865. Registered 19 September 2014. Retrospectively registered.The study was registered in The Netherlands National Trial Register (NTR) under TC  Measurement of blood flow is potentially an unrivalled asset of cardiovascular magnetic resonance (CMR), and able to measure the volume flow in large vessels by pixel wise mapping of the velocities through planes transecting the vessels. This should provide the most accurate measurements available of aortic or pulmonary regurgitation, cardiac output, shunt flow and, indirectly, of mitral and tricuspid regurgitation , 2. The These background phase errors result in offsets in the velocity values, typically in the range up to 4,9 cm/s . In mostAs flow quantification at the aorta and main pulmonary artery are the two main applications for clinical CMR velocity imaging, this study focused on 2D phase contrast imaging of these vessels. It was shown that the velocity offset needs to be below 0.6 cm/s for reliable clinical CMR imaging of the volume flows, typically to obtain an error in cardiac output below 5% . EarlierThe cause of the velocity offset is known. A large source is the concomitant field (i.e. Maxwell) terms of the gradient fields . As thesThe velocity offsets issue led to an initiative backed by the European Association of Cardiovascular Imaging (EACVI) CMR section of the European Society of Cardiology to determine whether these offsets were a general or more site-specific problem. The first study in this initiative showed that these offsets are generic and apparent on systems of all 3 main CMR vendors: General Electric Healthcare, Siemens Healthineers and Philips Healthcare . A seconWhen these offsets are present in the acquisition and are not easily preventable with general protocol guidelines, they should be corrected in post-processing. A fixed correction per CMR system cannot be used as the offsets are dependent on many protocol parameters such as slice orientation, and thus vary per specific acquisition. The most straightforward approach is repeating the identical acquisition on a static phantom to determine the offset, and subtracting the corresponding apparent phantom velocities from those of the clinical acquisition. This approach was performed earlier in several single center studies , 6, 12. Another post- processing approach is the estimation of the offset by using the velocity values of stationary tissue within the field-of-view (FOV); a spatially interpolation-based offset correction. Walker et al. proposed an algorithm to detect stationary tissue on a pixelwise basis within the 2D image using a threshold on the standard deviation of the velocity over the cardiac cycle . Using aThe main objective of this study is to validate the interpolation-based offset correction  were anonymized. To keep balance in the data, we aimed to include 10 studies for each vessel on each CMR system.Each site applied their local protocol for aorta or pulmonary flow acquisitions. For this study, we applied a minimal set of protocol requirements which are known to generally reduce the velocity offset , 11, andenc) was adapted reasonably to the peak velocity Vpeak; meaning (0.8 x Vpeak) < Venc < (2 x Vpeak), such that any velocity aliasing was resolved in post processing. Phase errors due to Maxwell/concomitant gradient terms were corrected within the image reconstruction, as was implemented in all the different included CMR systems [The applied technique for flow assessment was 2D phase contrast velocity quantification with a spoiled gradient echo imaging pulse sequence in cine mode with retrospective or retrogated electrocardiographic (ECG) triggering. Imaging was performed with table shift such that the center of the FOV was positioned in the transaxial iso-center plane (i.e. at z = 0 where z = the head-foot direction) of the scanner, as supported by the vendor’s software in routine clinical use. The encoding velocity ; for Philips systems the default background phase-offset correction (‘LPC filter’) and noise clipping were switched off. Spatial wraparound in the phase encoding direction was limited, with instructions that each center should ensure a remaining ‘air gap’ preventing phase-encode wrapped tissue from overlapping onto other tissue in the image. Both posterior and anterior radiofrequency (RF) receiver coils were enabled in the image FOV. If, for example, only anterior RF surface coils were enabled, the resulting low signal to noise ratio (SNR) posteriorly limited the automatic mask calculation in the correction. Finally for validation purposes we required a FOV set such that stationary tissue in the thorax wall was in view.In order to act as a reference standard for the velocity offsets, measurements in a stationary phantom were applied. After each patient acquisition, a phantom measurement with exactly the same protocol settings was performed within 24 h. A fluid or gelatin filled stationary phantom was imaged, with a size such that it included both the location of the vessel of interest as well as a part of the thorax wall. The gelatin or water phantom was doped to shorten T1 values resulting in a signal to noise ratio in the imaging protocol above muscular tissue. The phantom acquisition was either triggered on a simulated ECG at the same rate as the patient or otherwise at 60 beats/min. For fluid filled phantoms a 5 min waiting time was included after the phantom positioning, to ensure that no residual fluid motion was present during the measurement. This waiting delay was omitted in case of a gelatin filled phantom. For systems (Philips) which showed earlier a dependency of the velocity offset on the gradient system duty cycle of preceding scans , 13, preEarlier studies have shown that due to system heating, temporal stability of the velocity offset is perturbed in some systems , 13. ThiInitial analysis was carried out at each participating site, where analysis of the cardiac output without offset correction was performed. An ROI at the vessel of interest was manually set at each cardiac phase, using the magnitude image. By copying these ROIs to the velocity images, the time-integrated volume flow was assessed. This analysis was performed with the regular flow analysis packages in routine use at each site.Subsequent analysis was performed at the core-lab  as follows.For all in-vivo measurements an interpolation-based offset corrected dataset was obtained. The interpolation-based offset correction method was the method as described by Walker et al. , and earIn support of the second stated objective of this paper, the surface fitting was also performed using not only tilted planar surfaces (“linear fitting”) but also allowing tilted and curved surfaces (“higher order fitting” to 3rd order inclusive) (14).− 6 cm/s were excluded before creating the mask. Second, to reduce sensitivity to noise pixels in air and tissue:air boundaries, the initial threshold was set above the 15%, and the resulting mask was eroded by 1 pixel, such that the final mask fit to the stationarity percentage of 15%.In CMR acquisitions with phase-encode spatial wraparound, within the “overlapping” limit explained above, the areas of infolding tissue were manually traced and excluded from the image, before the 15% threshold on number of pixels was set to obtain the mask. To make the algorithm stable for images from all vendors, the algorithm was slightly adapted, because single pixels in the air and at the edges of the images appeared in the mask of stationary tissue using data from some vendors. First, to exclude zero or constant-filled pixels occurring in some reconstructions (e.g. filling in at FOV-edges after gradient distortion correction “image warping”), pixels with a temporal variance of the velocity over the cardiac cycle below 10Please note that the variation of offset across the field of view is not supported by some commercial correction software, where a single static tissue ROI is placed and used for correction on the strongly inadequate assumption that the background correction is a constant across the field of view . That questionable method should not be confused with the single ROI used for the phantom measurement accuracy check explained later in the Methods and shown in Fig. IB offset correction ROI vessel) was assessed by copying the end-diastolic vessel ROI on the fitted velocity offset field of the offset correction. Subsequently, this ROI was copied to the phantom images, averaging over all simulated cardiac phases, to obtain the phantom estimation of the velocity offset for this flow acquisition (Vphantom ROI vessel). Using the velocity offset value from the phantom measurement, the initial velocity offset error (Voffset pre correction) was determined, as well as the residual offset error after interpolation-based correction (Voffset after IB correction) at the location of the aorta or main pulmonary artery, see Eqs. For each flow acquisition, an ROI was manually drawn at the vessel of interest at the first cardiac phase after the R-wave, at end-diastole Fig. . The velThe velocity sign of the velocity offsets was set such that a positive offset resulted in an overestimation of the cardiac output. The interpolated-based correction was repeated with 2nd and 3rd order surface fits, besides the original linear fit, to test the correction performances of these different spatial orders of interpolation. Using the assessed velocity offsets and the site’s initial cardiac output measurement, the error in the clinically assessed cardiac output was calculated. For the cardiac output analysis, the lowest interpolation order with the required performance (i.e. < 0.6 cm/s difference from the phantom estimate) was chosen, because higher interpolation orders make the correction more sensitive to artifacts and noise.Finally, we assessed the pulmonic flow/systemic flow (Qp/Qs) ratio both with and without interpolation-based correction in a subset of subjects , where both aorta and main pulmonary artery data were obtained and which were not suspected clinically of having shunts.To verify the accuracy of the phantom offset determination, a check was performed in order to make the phantom data a valid reference. From previous studies it was known, that offset assessment by a separate phantom measurement is not a good standard in all cases due to lack of temporal stability , 13. We p value < 0.05 was considered significant.From the Methods section, it should be recalled that the velocity offset is an average over the entire cardiac cycle (since this is retro-gated cine imaging). Two average values of the velocity offset for each of the 6 systems were calculated, pre-correction and post-correction, by averaging over the studies collected on the system. These values are reported as mean ± standard deviation. The error in the cardiac output due to the velocity offset is expressed as a relative error in %. Differences in mean values in offset correction between the phantom and interpolation-based correction were tested with a paired Student’s t-test. Offsets were reported per system both as mean as well as using root mean square (RMS) values. RMS values show a better indication of the offset in an individual subject, whereas the normal average values indicate systematic offsets in a group of subjects. Interpolation order was judged by calculating the root mean square error per system. Values were compared to a target limit of 0.6 cm/s. Differences in variance were tested with an F-test. A A total of 132 studies were sent to the corelab. We excluded 6 studies; 4 showing too much spatial wraparound in the phase encoding direction, 1 with incorrect phantom positioning, and 1 with the vessel not clearly in view. The remaining 126 flow measurements in 82 subjects were included; 46 male, age 43 ± 20 years. Fifty-nine measurements were at the main pulmonary artery, and 67 at the aorta. At 5 systems mostly clinical patient studies were included, whereas in the remaining site patients were included which fitted in another research protocol. Within each image series, areas of spatial wraparound in the phase encoding direction were manually excluded in 34 flow acquisitions.For the ROI placed in stationary tissue in the thorax wall, there was overall  a good agreement between in-vivo and phantom scan of − 0.2 ± 0.6 cm/s Fig. . Howeveroffset pre correction) was − 0.4 ± 1.5 cm/s, and resulted in a − 5 ± 16% error in cardiac output. 40% of the patient studies showed a Voffset pre correction at the vessel smaller than ±0.6 cm/s, with a range of − 4.2 to 3.9 cm/s. Figures Before correction, the offset at the vessel (Voffset after IB correction) decreased, as can be observed in Fig. With interpolation-based correction the offsets (Voffset after IB correction) was 0.1 ± 0.5 cm/s (p = 0.01) and 0 ± 5% error in cardiac output (p = 0.01), significantly smaller than before offset correction. Also the variance in offset over the subjects was significantly less after correction (p < 0.001). 78% of the individual measurements showed remaining error below 0.6 cm/s, with remaining errors were in the range of − 1.2 to 1.4 cm/s post correction. There was no statistical difference between the velocity offset assessed by the phantom or interpolation-based correction (p = 0.3).After application of the interpolation-based correction with spatial orders as described above for each system, the remaining offset velocity (Vp = 0.5), 1.02 ± 0.11 (p = 0.3) for the uncorrected, phantom-based correction, and interpolation-based correction, respectively .The Qp/Qs was determined in a subset of 22 subjects which were not suspected clinically of having shunts and with measurement data in both vessels. The Qp/Qs was 1.1 ± 0.2, 1.03 ± 0.12 . Rigsby et al. also found inconclusive results for change in the Qp/Qs ratio in a single-center study with a similar interpolation-based offset correction [Earlier studies used the more clinically oriented Qp/Qs ratio, because this value is expected to be around 1.05 in patients not suspected of shunts , 6, 19. rrection . ApplyinIn this study we examined the order of spatial interpolation of the offset fit to the mask pixels to the rest of the image including pertinently the location of the vessel of interest. The initial implementation of Walker et al.  applied Finally, these conclusions are for the aorta and main pulmonary artery, which usually are situated reasonably near the center of the FOV, and for every measurement the patient table was adjusted to place the FOV center in the z = 0 plane. To reduce offsets it would be better to put the vessel of interest at the z = 0 plane. However, this would require user input and manufacturers did not implement this. Placing the vessel of interest at the FOV center is not desirable, as this will induce spatial wraparound or requires a large FOV. Placing the FOV center in the z = 0 plane can be easily implemented in the regular workflow. For vessels further away from the center higher order corrections might be required. This is also the situation for velocity quantification in 4D Flow ; velocitAccurate and reliable determination of the mask of stationary pixels is essential to the interpolation-based correction. Here, we set the stationarity percentile to 15% as in the original paper of Walker et al. . In the The algorithm as applied in this study can be implemented in an analysis package for CMR flow post-processing, as is currently already the case for some commercial packages. In principle, the algorithm can run without strict supervision. Manual supervision is still required for the exclusion of phase-encode spatial wrap around before generating the pixel mask. Secondly, the user would also be required to confirm that stationary tissue is included in the mask to both sides of the thorax, and that moving structures of the heart and large vessels  are excluded. Metallic implants are also an increasingly common problem in clinical work with some predictable difficulty in handling nearby pixels. The analysis package could display the stationary pixel mask, so the user could adjust the stationarity percentile as described above. Finally, it may be necessary as in this study that the spatial order of interpolation has to be set per CMR system.A limitation of the interpolation-based offset correction is that it is sensitive to phase-encode FOV wraparound . Ideally this should be prevented in scanning, but this is quite a stringent limitation and tends to increase breath-hold time, unless other sequence parameters are adjusted, with all their consequences. If phase-encode wraparound does not reach as far as overlapping the direct image, or overlaps only small regions, it can be excluded manually before correction. However, any such dependence on intervention can be an obstacle to reliable clinical use. For some protocols it can be helpful to increase the phase-encoding FOV using parallel imaging , becauseA second protocol optimization specific for this interpolation-based correction is to ensure that the posterior RF receiver coils are switched on, even in cases where the vessel of interest, such as the main pulmonary artery is in the anterior part of the thorax. The posterior coils are not of interest for the SNR at the level of the vessel, but they are essential to provide sufficient SNR for the CMR signal of the stationary tissue on the posterior side of the thorax.In this study we considered only 1.5 T whole body superconducting CMR systems. Most CMR studies are still performed at this type of CMR system. However, there is an increased application of 3 T for CMR. The origin of the velocity offset is within the gradient system and its associated errors, and the overall specification of these does not change at 3 T , except that Maxwell terms are smaller, so any imperfections in their software correction might be expected to reduce as well. Therefore, we expect that these results should also be valid at 3 T CMR systems. However, another aspect in which these systems might deviate physically from 1.5 T is mechanical vibration, as the mechanical force for the same gradient performance scales with main field strength. A recent small single-center study at 3 T found similar results as this study considering Qp/Qs ratios .Care should be taken not to interpret the specific offsets found in this study as definitely linked to particular vendor models of CMR systems. In this study we used on purpose as much as possible each site’s customary CMR protocols, thus specific settings such as breath-holding, typical slice orientation and measurement location at the vessel of interest would be likely to give a strong bias on the velocity offset observed using the different CMR systems . The absNotwithstanding the above, we noticed incidentally that the two Philips CMR systems  in this study showed a relatively high uncorrected velocity offset. Besides the issue of the protocol differences between sites as mentioned before, one should realize that some systems may apply in their default protocols a background phase-offset correction filter. On Philips machines this is known as the ‘LPC filter’, developed to reduce the phase offset in CMR contrast angiography, where it is expected to reduce the velocity offset . On the Retrospective ECG gating was used in this study. In case of unstable heart rhythms, prospective ECG gating shows clinically more reliable measurements. In the case of prospective ECG gating it has been shown that the amount of velocity offset varies with the timing after the sequence starts running in each cardiac cycle, and tends to be larger directly after the ECG trigger . The intIn this study, the velocity offset was assessed at the vessel position in the first cardiac phase. In reality the vessel position varies somewhat over the cardiac phases. This effect was neglected, but the phantom data show that the spatial variation of the offset in this motion range is limited.The ‘phantom measurement accuracy check’ was performed with a relatively small ROI. Ideally, we would perform this check on the total mask of stationary tissue. However, this would require a stationary phantom of the size of a large adult thorax at all centers. Because of practical reasons, we were compelled to use a smaller phantom allowing only the vessel and a part of the anterior thorax wall to be covered by the phantom. Instead of the phantom, the possibility of some form of “internal validation”, for example using LV stroke volume from cine stack, or by Qp/Qs, was limited by requiring definitely normal subjects, by other well-known sources of inaccuracy and also by having no permission to make any extra patient acquisitions for this unfunded work.The study protocol was open on the use of breath-hold or non-breath-hold techniques, as well as the precise acquisition parameters and method of positioning the measurement plane in the two vessels. Every site applied the technique in their own regular manner . Therefore, this study shows a realistic variability of protocol settings, but of course this might have introduced also variations between the different sites and systems. We emphasise that the acquisition parameters were checked as identical for each in-vivo scan and its phantom scan, so controllable differences there were not a source.Even in nominally breath-hold scans, phase-encode ghosting artefacts of the bright superficial tissues  are often problematic, and furthermore might sometimes be relatively constant over the cardiac cycle (depending on a few factors not to go into here), and so the impact of artefacts on chest wall phase might get past the temporal variance test of the interpolation-based correction method (which aims to exclude flow ghosting as described in Methods). It is uncertain whether this corrupting effect is usually small  compared to the true pixel brightness at the wall, and is of some possible concern (as are other variations ) becauseFinally, the subset of Qp/Qs studies was acquired at many different centres where the placement of the aortic plane might have varied relative to the coronary ostia, that can require a few % correction in the ‘normal’ Qp/Qs value.In future CMR systems, field camera measurements of real-time effective gradient fields might enable better compensation of phase offsets , 26, butThis study shows that interpolation-based velocity offset correction reduces the offset with comparable efficacy as phantom-based offset correction, without the time penalty imposed by separate phantom scans with their associated concern regarding short-term thermal stability. This method showed to be stable for 2D retrospective ECG triggered phase-contrast velocity quantification in the large vessels around the heart. However, some manual intervention in the largely automated correction of patient scans remained necessary, and the optimum spatial order of interpolation required initial assessment for each type of system, because linear was not always optimal.


Using gene-disrupted strains of the hyperthermophilic archaeon Thermococcus kodakarensis, we studied the in vivo contribution of proteins Nop10 and Gar1 to the dual RNA guide-dependent and RNA-independent activities of Cbf5 on 23S rRNA. The single-null mutants of the cbf5, nop10, and gar1 genes are viable, but display a thermosensitive slow growth phenotype. We also generated a single-null mutant of the gene encoding Pus10, which has redundant activity with Cbf5 for in vitro formation of Ψ55 in tRNA. Analysis of the presence of Ψs within the rRNA peptidyl transferase center (PTC) of the mutants demonstrated that Cbf5 but not Pus10 is required for rRNA modification. Our data reveal that, in contrast to Nop10, Gar1 is crucial for in vivo and in vitro RNA guide-independent formation of Ψ2607 (Ψ2603 in P. abyssi) by Cbf5. Furthermore, our data indicate that pseudouridylation at orphan position 2589 (2585 in P. abyssi), for which no PUS or guide sRNA has been identified so far, relies on RNA- and Gar1-dependent activity of Cbf5.Archaeal RNA:pseudouridine-synthase (PUS) Cbf5 in complex with proteins L7Ae, Nop10 and Gar1, and guide box H/ACA sRNAs forms ribonucleoprotein (RNP) catalysts that insure the conversion of uridines into pseudouridines (Ψs) in ribosomal RNAs (rRNAs). Nonetheless, in the absence of guide RNA, Cbf5 catalyzes the  This modified nucleoside is generated by site-specific C5-ribosyl isomerization of uridine (U) in pre-existing RNA transcripts. Formation of Ψ in RNAs can increase base stacking4 and rigidify the sugar-phosphate backbone6. More specifically, Ψ can stabilize RNA 3D conformation by water bridge formation, as demonstrated in tRNAs8. A cluster of Ψs can modulate the conformation of a large RNA domain as shown for helix 69 – H69 – in rRNA9. It is now firmly established that some evolutionarily conserved Ψs in tRNAs and rRNAs are required for accurate function of these non-coding RNAs (ncRNAs) in translation . Similarly, some of the Ψs in U-rich small nuclear RNAs (UsnRNAs) are important for pre-mRNA splicing22. Moreover, recent transcriptome-wide maps of pseudouridylation detected a large number of Ψs in eukaryotic mRNAs24 but their precise function is not yet firmly established.Pseudouridine (Ψ) is the most abundant RNA modification (for review25). Five families of PUS have been defined based on protein sequence conservation with each of the bacterial TruA, TruB, TruD, RluA, and RsuA proteins (for review25). Comparative analysis of eukaryotic genomes revealed the presence of members of the same five families of PUS in Eukarya. Studies on archaea identified a rather limited number of putative genes encoding PUS, while in addition to PUS homologous to bacterial PUS, a sixth family of PUS was detected in eukaryotes. This sixth family of PUS has an archaeal representative – Pus10 of the archaeon Pyrococcus abyssi27.The conversion of uridine (U) into Ψ is catalyzed by specific RNA:Ψ-synthases (PUS) (for review29). In archaea, box H/ACA sRNPs are formed by association of distinct H/ACA sRNAs with a constant set of four proteins including the TruB-like PUS aCBF5, the ribosomal protein L7Ae, and proteins aNOP10 and aGAR1 31. In absence of sRNA, protein complexes can be formed in which Nop10 and Gar1 interact independently with the catalytic domain of Cbf534. The H/ACA sRNAs act as guide RNAs ensuring the specificity of the RNP enzyme. Each H/ACA sRNA contains at least one hairpin-tail structure with a large internal loop, which forms base-pair interactions with a target RNA sequence. Two duplexes are then formed on each side of the unpaired U to be modified by Cbf5. Cbf5 interacts together with the box H/ACA sRNA and proteins Nop10 and Gar137. The Gar1 protein is dispensable for total modification of substrate RNAs in single-turnover reactions31, but it enhances the kinetics of the reaction39. By modulating the formation of open and closed states of the catalytic center, Gar1 facilitates substrate RNA loading, its efficient placement and the release of the modified RNA42.In archaea and eukaryotes, the catalysts for Ψ synthesis are either stand-alone PUS proteins or ribonucleoprotein particles with PUS activities that are referred to as box H/ACA small RNPs (sRNPs) in archaea and as box H/ACA small nucleolar RNPs (snoRNPs) in eukaryotes  and U2607 (U2585 in P. abyssi) in 23S rRNA. Altogether, our data reveal a general function for Gar1 in the RNA guide-dependent and RNA-independent activities of Cbf5.Here, we describe successful isolation of T. kodakarensis genome56 in the aim of identifying ORFs for the PUS Cbf5 and Pus10, and for protein components L7Ae, Nop10, and Gar1 of the H/ACA RNPs. The protein-protein BLAST program using the amino acid sequences of P. abyssi PUS and H/ACA sRNP protein components as queries identified a single ortholog of Cbf5 (TK1509), Pus10 (TK0903), L7Ae (TK1311), Nop10 (TK1101), and Gar1 (TK2286) in T. kodakarensis genome , which encodes orotidine-5′-monophosphate decarboxylase, and 1 kb of the 5′ and 3′ flanking regions of each target gene were constructed as described in Methods. They were used to transform the T. kodakarensis strain KU216 showing uracil auxotrophy due to a non-functional pyrF locus59. Transformants harboring integration of the plasmid into the chromosome through homologous recombination were selected from the KU216 strain for uracil autotrophy. The intermediary pyrF+ strains were then selected by 5-FOA resistance to obtain gene knockout mutant strains58. Using this strategy, we obtained colonies for the single knockout mutants Δcbf5, Δpus10, Δnop10, and Δgar1, indicating that, taken separately, none of these genes were essential for cell viability. However, this was not the case for the ORF encoding the ribosomal protein L7Ae, for which we did not succeed in isolating a viable disrupted mutant.We performed sequence analysis of the ome Fig.  and S2. T. kodakarensis strains and to exclude potential off-target integration of pyrF gene used in knockout strategy, we performed whole genome sequencing and analysis of generated T. kodakarensis mutants. Fragmented genomic DNA extracted from each mutant was converted to the library and sequenced on Illumina HiSeq1000. Sequencing reads  were aligned to the reference genome and coverage was calculated by each T. kodakarensis ORF. Figure pyrF) is absent in all four strains used. More detailed information on the deletions is given in Supplementary data  can be formed in a RNA guide-independent way55, while Ψ2589 (Ψ2585 in P. abyssi) still have no associated RNA guide and complex Cbf5–Nop10–Gar1 is not capable of forming Ψ2585in vitro55. RNA guide-dependent residue Ψ2592 (Ψ2588 in P. abyssi) was used as control.We next conducted phenotypic analysis of the null mutants by looking for the presence of Ψs in 23S rRNA using the CMCT-RT approach. We focused this more extensive analysis on the Domain V region of 23S archaeal rRNA spanning residues 2588 to 2612 Fig. . This re2589, U2592, and U2607   is present in the T. kodakarensis genome55. The absence of Nop10 or Gar1 has the same effect as the absence of Cbf5, since it completely stopped the formation of Ψ2592 in 23S rRNA  formation.In a previous work, we showed that the in vitro modification of an excess of the 23S rRNA fragment mini–23S–2607  in 23S rRNA.To further pursue our investigation of the role of the auxiliary proteins, we compared single-turnover activities of the free Cbf5 protein, and the Cbf5–Nop10 (CN), Cbf5–Gar1 (CG), and Cbf5–Nop10–Gar1 (CNG) protein mixtures Fig. , for in 607 Fig. . Formatiin vivo by the Pab91 sRNP, which was well characterized in our previous studies65. We verified that this substrate was modified in vitro at the target site by the P. abyssi Pab91 LCN enzyme under single-turnover conditions albeit less efficiently than the LCNG enzyme . We engineered a DNA template allowing transcription of a 143-nucleotide long RNA fragment containing a large portion of the PTC domain Fig. . This suyme Fig. . Howeveryme Fig. . The commed Fig. , lane 4.med Fig. , lane 5,2589(2585) in 23S rRNA remained enigmatic. CMCT-RT analysis of rRNAs extracted from the Δcbf5 mutant indicated that Cbf5 is implicated in the formation of this modification  when total RNAs extracted from T. kodakarensis cells were added to the protein mix. This activity requires the presence of the full set of proteins Cbf5, Nop10, Gar1, and L7Ae. We hypothesize that modification at position U2589 in 23S rRNA is generated by an RNP that is assembled by association of proteins L7Ae, Cbf5, Nop10, and Gar1 with a still unknown guide sRNA present in the T. kodakarensis total RNAs.At this stage of the study, the modification ΨRNA Fig. . IncubatE. coli and S. cerevisiae67. However, archaeal organisms were not included in this thorough-full characterization since targeted gene deletion in most of those organisms is still a rather difficult task. Genetic manipulations have only been used in few archaeal species such as Thermococcales, Sulfolobales, Methanogens, and Halophiles72. In the RNA modification research field, gene deletion has only been performed in halophilic Haloferax volcanii for tRNA modification enzymes73, for 2′-O-methylation guide box C/D RNA74 and for several PUS including Cbf5 and Pus1050. Establishment of the pyrF- KU216 T. kodakarensis strain and the use of pyrF as a marker to select uracil auxotrophic cells enabled the development of a strategy for targeted gene disruption in this sulfur reducing, strictly anaerobic hyperthermophilic organism75. By applying this system to T. kodakarensis, we generated the first disruption mutants of the two PUS enzymes Cbf5 and Pus10, and of the Gar1 and Nop10 components of the catalytic H/ACA RNPs of a hyperthermophilic archaea. These mutants make it possible to analyze the activity of the modification machinery at high temperatures and to study the effect of RNA modifications in these peculiar growth conditions. Up to now, the in vitro specificity and turnover of the H/ACA sRNP enzymes from P. abyssi have been shown to rely on high temperatures39 and in vivo data have not been documented.Genetic manipulation by gene disruption and functional complementation is an extremely valuable tool for the study of metabolic processes in a living cell. Such methods have been widely applied in bacterial or eukaryotic model organisms and have enable the characterization of numerous modification enzymes acting on RNAs in 76, but in agreement with observations made in halophilic archaeon H. volcanii50, the T. kodakarensis gene encoding Cbf5 homolog is dispensable. However, deletion of cbf5 led to a marked decrease in growth rate of T. kodakarensis cells at 90 °C  as a protein stand-alone enzyme acting alone or in complex with auxiliary proteins Nop10 and/or Gar155. We previously demonstrated that in addition to tRNA U55, U2603 in P. abyssi 23S rRNA represents a possible second substrate for the non RNA-guided activity of Cbf5 in P. abyssi55. Here, we found that the modification is conserved in T. kodakarensis 23S rRNA, i.e. at position U2607  an activity linked to its inclusion in H/ACA sRNPs complexes together with L7Ae, Nop10, and Gar1 and one H/ACA sRNA607 Fig. . Modifictes Fig.  that in 2589 in 23S rRNA. Our observation that total RNA fraction from T. kodakarensis restores Ψ2589 formation in vitro supports a possible role for an RNA component likely acting as a guide RNA for the activity of a catalytic RNP enzyme. Analysis of the 23S rRNA extracted from each disrupted mutant strain demonstrated the implication of each of the three cbf5, nop10, and gar1 genes encoding the H/ACA sRNP components for Ψ2592 (used as control) as well as for Ψ2589 formation  formation could correspond to a new type of guide RNA. Several RNA candidates need to be individually tested in in vitro modification assays before generating a null mutant of the gene sequence of the identified RNA for in vivo analysis.The present data also enlarge the catalog of sites modified by Cbf5 to position Uion Fig. . We did in vivo and in vitro, unlike Nop10, Gar1 is absolutely indispensable for the RNA-independent activity of Cbf5 at position 2607(2603) in archaeal 23S  formation. As an extrapolation, we can also suggest that the RNA-independent activity relies on the presence of Gar1 to facilitate the binding of Cbf5 to the RNA and/or to allow catalytic activity of the enzyme. Structural information on the [Cbf5:substrate RNA] complex formed in the presence and absence of Gar1 and comparison with the structure of the [H/ACA RNP:substrate RNA] complex38 might provide further information on the exact roles played by Gar1 in the RNA-dependent and RNA-independent activities of Cbf5.We show that both 23S Fig. . The worT. kodakarensis. We demonstrate that Cbf5 is responsible for Ψ formation at multiple positions in the large subunit rRNA. Cbf5 performs this function as the catalytic subunit of RNPs and modifies at least one position with an RNA guide-independent activity. Pus10 does not appear to be involved in rRNA modification. Gar1 contributes to each of the activities of Cbf5 characterized in vivo.We present here the analysis of several proteins involved in pseudouridylation of ribosomal rRNA in T. kodakarensis strain KU216, lacking pyrF gene (KU216 pyrF-) were cultivated under anaerobic conditions at the optimal growth temperature of 85 °C in a nutrient-rich medium (ASW-YT) containing 0.5% yeast extract, 0.5% tryptone and artificial sea water as described previously59. Other growth temperatures (75 °C and 90 °C) were also used to study the phenotypic effects of the deletions. ASW-CH medium was used for the selection based on uracil autotrophy as described previously57.Cells of the T. kodakarensis putative PUS genes were identified in a homology search with NCBI BLAST80, using the amino acid sequence of Pyrococcus abyssi PUS Pab0356 (cbf5) and Pab2391 (pus10), and H/ACA sRNP components Pab7213 (nop10), Pab3084 (gar1), Pab0460 (l7ae) as query. Sequence analysis of T. kodakarensis was performed using the NCBI genomic database (http://www.ncbi.nlm.nih.gov) and the BAGET sequence exploration tool (http://archaea.u-psud.fr/bin/baget.dll/EXEC). To ascertain the identity of the identified ORFs, sequences of T. kodakarensis proteins were compared with already known and biochemically characterized PUS and H/ACA sRNP proteins from P. abyssi, P. furiosus, or Haloferax volcanii. Only Pus10/Cbf5 showed slight similarity, and Nop10 and L7Ae shared limited similarity with IF2-gamma and L30e, respectively  of 5-fluoroorotic acid (5-FOA) for selection of uracil auxotrophic cells produced by internal chromosomal recombination. The colonies were picked and cultivated in 5 mL ASW-YT. Genomic DNA of each culture was isolated by phenol/chloroform extraction, followed by isopropanol precipitation. The genotypes of isolated strains were validated by whole genome sequencing of each mutant.The knockout strains were obtained with the gene targeting method for T. kodakarensis mutant strains was extracted following the standard procedure81. About 100 ng of DNA in 130 µl of water was fragmented using Covaris M200 AFA ultrasonicator to fragments of ~200 bp. Sonication efficiency was confirmed by capillary electrophoresis using HS DNA chip (Agilent). DNA fragments were converted to sequencing libraries using NEBNext® Ultra™ II DNA Library Prep Kit, according to the manufacturer’s recommendations. Amplified libraries were verified by capillary electrophoresis and quantified by fluorometry using QuBit. Sequencing was performed in SR50 and/or PE2x100 mode, using Illumina HiSeq1000. After trimming of the adapter sequences, clean reads were aligned to the reference genome (GCF_Tkodakarensis_ASM996/ATGC280) using Bowtie2 in–local-sensitive mode. Counting of the reads’ coverage was done using bedtools and the associated gff3 file.Genomic DNA from T. kodakarensis cells was stopped at the end of the exponential phase. Total RNA was isolated as described previously82. The cells were centrifuged and washed in 0.8x artificial sea water. The pellets (~109 cells) were dissolved in extraction buffer  and RNAs extracted by phenol/chloroform/isoamyl alcohol followed by ethanol precipitation, dissolved in H2O and stored at −80 °C. For RNAs extracted from the S100 fraction, the cells were resuspended in sonication buffer , treated by ultra sounds, the concentration of KCl was adjusted to 150 mM before centrifugation at 16,000 × g for 30 min. The S100 extract was obtained after centrifugation at 100,000 × g for 1 hour and stored in 20% glycerol at −80 °C. RNAs from the S100 fraction were extracted with phenol/chloroform/isoamyl alcohol followed by ethanol precipitation, dissolved in H2O and stored at −80 °C.Growth of 82 was adapted from83. Briefly, 10 µg of total RNAs were modified by CMCT, which preferentially reacts with U and Ψ residues. Extended incubation of the CMC-modified RNAs at alkaline pH (10.4) allows the hydrolysis of U–CMC adducts, which are less stable than the Ψ–CMC adducts. The positions of Ψ–CMC adducts were then detected by primer extension using avian myeloblastosis virus (AMV) reverse transcriptase  and 5′–end labeled primer O-2941 (5′-CGTTCCCCTTTAATGGGTGA-3′) complementary to 23S rRNA sequence. The synthesized cDNAs were fractionated by electrophoresis on a 7% sequencing gel which was analyzed by autoradiography.The N-cyclohexyl-N′-(2-morpholinoethyl)-carbodiimide metho-p-toluenesulfonate (CMCT) modification protocolE. coli C41 (DE3) pRare2 as GST-fusion proteins from recombinant pGEX-6P1 plasmids, and purified as described previously39. To ensure removal of contaminating nucleic acids from E. coli, the purification protocol includes incubation with polyethylenimine (PEI)39. Circular dichroism (CD) spectroscopy was used to control the absence positive Δε values in the spectral region above 240 nm, which is specific of CD spectra measured for RNAs35 . For the other fragments, the transcription reactions were carried out as previously described32P]CTP. The substrates mini–23S–rRNA–2589 and mini–23S–rRNA–2607 used to measure Cbf5 activity were radiolabeled during transcription in presence of [α-32P]CTP and [α-32P]GTP, respectively. Radiolabeling of the phosphodiester linkage 3′ to the targeted U2685 in the substrate RNA 23S–143nt was obtained by splinted ligation of two in vitro transcribed RNA fragments of the 23S rRNA84. Briefly, 10,000 cps of the 5′ end radiolabeled donor RNA spanning 23S rRNA positions 2686 to 2160 and containing the CUGA sequence connecting position 2717 with position 2143 were mixed with 100 pmol of the acceptor RNA spanning positions 2603 to 2685, in the presence of 50 pmol of a DNA single-stranded splint, which was perfectly complementary to the sequence of the donor and acceptor RNAs, in 50 mM Tris-HCl pH 7.4 containing 10 mM of MgCl2, 2 nmol of ATP, and 2 nmol of DTT. Hybridization of the two RNAs with the DNA splint was obtained by incubation at 75 °C for 4 min and a slow cooling to room temperature. The ligation reaction was achieved with 75 U of T4 DNA ligase (Fermentas) at 4 °C overnight. The DNA splint was digested by adding 3 U of DNase RQ1 (Promega) at 37 °C for 30 min. The radiolabeled ligation products were purified on a 8 M urea-PAGE (6%).The substrate RNA 22–U used for Pab91 sRNP activity measurements was radiolabeled during transcription in presence of [α-65. Protein Gar1 was added to this mix to assemble the LCNG RNP enzyme.LCN Pab91 RNP enzyme assembly was obtained by mixing the sRNA Pab91 and the proteins L7Ae, Cbf5, and Nop10 as previously described39. The radioactivity present in the CII’ complex or in the unbound fraction was revealed using a Phosphorimager .Complex CII’ was obtained by association of the sRNA present in the sRNP containing proteins L7Ae, Cbf5, and Nop10 (LCN) with radiolabeled substrate RNA, as described previously65. Conditions for time course analysis of single-turnover reactions by reconstituted Pab91 sRNP particles LCN and LCNG were previously detailed48. Briefly, ~4 pmol of unlabeled Pab91 sRNA and ~150 fmol of labeled 22–U substrate RNA were mixed with protein combination L7Ae–Cbf5–Nop10 or L7Ae–Cbf5–Nop10–Gar1 (200 nM each) at 65 °C. In these conditions, the concentration for the reconstituted sRNP was estimated to be of ~0.5 µM. For multiple turnover reactions, an excess of unlabeled substrate RNA 23S–143nt, ranging from 1 to 10 μM, was added to the reaction mix.To test for the RNA:Ψ-synthase activity, we used an approach based on the nearest-neighbor analysisThe guide RNA was omitted to perform single turnover reactions for the measurement of the RNA guide-independent activity of Cbf5. The radiolabeled substrates mini–23S–rRNA–2589 and mini–23S–rRNA–2607 (~150 fmol) were incubated with different combination of the recombinant proteins Cbf5, Nop10, Gar1, and L7Ae (200 nM each).85. For the reactions performed on the RNA 22–U, one dimension chromatography was sufficient to fractionate the labeled nucleotides whereas two-dimensional TLC is needed for activity measurement with mini–23S–rRNA–2589 or mini–23S–rRNA–2607. The radioactivity of the 3′-monophosphate nucleotides was quantified with the Phosphorimager and quantified using the ImageQuant software Version 5.2 (Molecular Dynamics). The quantities of Ψs formed were determined by taking into account the total number of U nucleotides in the substrate RNA. The obtained values in moles of Ψ per mole (mol.mol−1) of substrate RNA were analyzed with Prism software Version 5.04 (GraphPad).For all these assays, aliquots were collected at several time points and the reaction was stopped by phenol/chloroform extraction followed by ethanol precipitation. The recovered RNAs were digested with ~0.4 U of RNase T2 to generate 3′-monophosphate nucleotides that were fractionated by thin layer chromatography (TLC)Dataset 1


Stevia rebaudiana (S. rebaudiana) is a herbaceous and perennial plant belonging to Asteraceae family. The genus stevia is well known as a natural producer of sweetener comprising non-caloric and non-carcinogenic steviol glycosides. In recent years, the capability in producing natural sweetner has increased the demand for S. rebaudiana as substitute of processed sugars. Flowering phase of S. rebaudiana has shown to affect the content of steviol glycosides in the leaves. Steviol glycosides level is the highest at the time of flower bud formation and lowest at time preceding and following flower bud formation. Therefore, sequencing and analysing the genes that are involved in flowering phase will provide platform for gene manipulation in increasing steviol glycosides content. The Stevia transcriptome data that include two stages of growth (before flowering and after flowering), were obtained using Illumina RNA-seq technology and can be accessed at NCBI Sequence Read Archive under Accession No. SRX6362785 and SRX6362784. The plant was grown at Glasshouse and Nursery Complex in IIUM Kuantan Campus. The bam files that obtained from transcriptome libraries have been deposited to NCBI SRA database with accession number SRX6362784 and SRX6362785. The statistic of RNA-seq generated and overview of the number of transcripts are described in The dataset contains raw sequencing data obtained through the transcriptome sequencing of leaf samples of 22.1S. rebaudiana accession MS007 was cultivated through shoot cutting method, in order to grow sufficient number of plants required. A healthy S. rebaudiana accession MS007 was obtained from Glasshouse and Nursery Complex in IIUM, Kuantan Campus as a mother plant. Healthy shoot with at least 2 leaves were selected for shoot propagation [pagation . No addi2.2RNA was isolated from the samples using Plant Total RNA Mini Kit (Geneaid). Transcriptome data were generated from the total RNA extracted from these two samples collected at two different developmental stages. NanoDrop ND-1000 Spectrophotometer  and Bioanalyzer RNA 6000 chips system  were used to determine the quality and integrity of total RNA.2.3S. rebaudiana samples were trimmed using Solexa QA++ with Phred score Q20. By using FastQ file, the FastQC was ran at default parameter. De novo assembly of S. rebaudiana data was done using Trinity RNA-Seq 2.0 with default settings [The raw reads generated from settings . The det


RNA–protein interactions are crucial for most biological processes in all organisms. However, it appears that the complexity of RNA-based regulation increases with the complexity of the organism, creating additional regulatory circuits, the scope of which is only now being revealed. It is becoming apparent that previously unappreciated features, such as disordered structural regions in proteins or non-coding regions in DNA leading to higher plasticity and pliability in RNA–protein complexes, are in fact essential for complex, precise and fine-tuned regulation. This review addresses the issue of the role of RNA–protein interactions in generating eukaryotic complexity, focusing on the newly characterized disordered RNA-binding motifs, moonlighting of metabolic enzymes, RNA-binding proteins interactions with different RNA species and their participation in regulatory networks of higher order. These proteins are necessary for RNA synthesis, maturation, transport, storage, regulation of stability and translatability. The RNA molecule from synthesis to degradation is constantly supervised and chaperoned by protein complexes, and their ever-changing protein content contributes to the regulation of the fate of the bound RNA. The scope of this regulation is crucial for the complexity of the organism. In bacterial cells, the regulation is relatively simple, with polycistronic operons controlling entire pathways. The discovery of CRISPR–Cas9 RNA-guided interference shows that bacteria also have at their disposal more sophisticated mechanisms involving RNA regulation employed in anti-phage defence . HoweverRecent efforts to identify new RNA-binding proteins (RBPs) in a screening technique called RNA interactome capture revealed that there might be about 1500–1900 RBPs in human cells , many moAnother contribution to eukaryotic complexity is provided by dual- or multi-function proteins, which, under certain circumstances, may act as either metabolic enzymes or regulatory RBPs, or possess additional biological roles. This additional layer of regulation enables RNA molecules to directly affect metabolic pathways.The current perception of RNA–protein interactions is strongly biased towards a protein-centric approach, in which proteins regulate the expression and activity of RNA, not the other way around. Since only about 1–2% of the human genome encodes proteins and as much as 83–85% is transcribed  repeats. Motifs rich in arginine (R) and glycine (G) consist of at least three RG/RGG repeats, separated by 10 amino acid residues. This motif constitutes the second most common RBD in the human genome. RNA-binding properties of this motif are poorly defined; it is known to have a broad, degenerate specificity in RNA recognition. However, the binding can be quite specific: for example, the RGG/RG domain of FMRP binds tightly to the RNA-containing G-quadruplex structure, acting as a specificity determinant [erminant . RG[G]-cerminant .RS/RG-rich. RS-rich, RG-rich and other basic sequences can mediate both specific and non-specific interactions with RNA. Disordered, arginine and serine (RS) repeat-containing regions occur in a number of human proteins referred to as SR proteins and SR-like proteins. The most important group of SR proteins are pre-mRNA splicing factors which bind to exonic splicing enhancers and stimulate the excision of adjacent introns. These proteins contain one or two RBDs at the N-terminus and a C-terminal arginine–serine-rich domain which also could directly bind RNA. Splicing efficiency depends on the length of the RS repeat and could be modified by phosphorylation of RS, which promotes a transition from intrinsically disordered to an arch-like structure, influencing RNA-binding properties. This phenomenon can be observed in serine/arginine-rich splicing factor 1 (SRSF1) and the RNA-helicase DDX23 [se DDX23 ,36. ExceK/R basic patches. This motif is composed of four to eight lysines (K) or arginines (R), which form a highly positive and exposed interface. K/R patches are highly abundant among non-canonical RBPs and can group at multiple positions within RBPs, frequently flanking globular domains and probably cooperating with them in RNA binding [ binding ,32.Molecular recognition features. Most MoRFs are up to 25 amino acid residues long, but some of them are 50 or more residues long [ues long . An impoues long . MoRFs aues long ,39. ThisLow-complexity sequences. Low-complexity (LC) sequences contain up to 100 amino acids, and are composed of many repeats of the same amino acid or several amino acids [no acids . Signifino acids . LC sequno acids . With in2.2.2.The majority of RBPs (as well as RNAs) are localized in the nucleus. Export of the RNA to the cytoplasm is tightly controlled, requires many steps and concerns mostly mRNAs, rRNAs, tRNAs and miRNAs. There are also examples of long non-coding RNA (lncRNA) present in the cytoplasm, which is consistent with many of them being capped, polyadenylated and undergoing splicing, thus fulfilling the criteria for efficient export. Nevertheless, compared with mRNAs, lncRNAs are significantly enriched in the nucleus . NuclearSpliceosome (nucleus). Approximately half of the combined sequence of abundant spliceosomal proteins is predicted to be disordered. About 80% of spliceosomal proteins were predicted to contain at least one IDR, in contrast with the calculated fraction of about 35% for the entire human proteome [proteome . It has proteome . Additioproteome .Ribosome (cytoplasm). Intrinsic disorder is not only abundant in ribosomal proteins, but it is also necessary for their function and, as such, is highly conserved. According to the criteria set out by Gunasekaran et al. [n et al. , almost n et al. . Ribosomn et al. . Anothern et al. .RNA granules. The presence of disordered RBPs has been linked to the formation of membraneless cellular ultrastructures, rich with condensed proteins and RNA. The formation of these granules requires liquid–liquid phase separation (LLPS [on (LLPS ). LLPS pon  and frontotemporal lobar degeneration (FTLD). Several mutations in genes encoding such IDPs as FUS, hnRNPA1, TARDBP (TDP-43) and TIA-1 have been linked to these age-related neurodegenerative diseases. Many of the identified mutations result in a replacement of prion-inhibiting charged amino acids with neutral or prion-promoting ones . Indeed, in vitro and in vivo studies showed that mutated RBPs show a higher propensity for transition from the liquid and reversible hydrogel state to irreversible fibrillar assemblies. This process is usually mediated by a mutated LC domain, but changes in the other parts of the protein can also indirectly increase its ability to undergo phase transition [ansition ,53. The ansition .3.Metabolic enzymes constitute a class of proteins with well-defined structural features, crucial for enzymatic activity and—accordingly—proteins with the least percentage of disordered regions. There has been a general opinion that they perform only one, highly specific function, but in some cases, described below, they have another function, which is often related to RNA binding. Therefore, these enzymes may ‘moonlight’, performing some tasks physiologically distinct from their role in basic metabolism. Thus, moonlighting of metabolic enzymes provides yet another explanation for the small number of genes in the human genome and adds to the complexity of eukaryotic regulation. Owing to their promiscuous binding, IDPs were initially hypothesized to be good candidates for moonlighting , but, toA well-known example of dual-function protein moonlighting as RBP is aconitase, an enzyme that catalyses the interconversion of citrate to isocitrate. Aconitase is an iron–sulfur protein and, in normal conditions (unchanged iron level), it functions as an enzyme. Its moonlighting as an iron regulatory protein 1 (IRP1) has been described many times –59, and Another well-known example of a metabolic enzyme acting as an RBP is glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a glycolytic enzyme with a plethora of extra-glycolytic functions, which include its RNA-binding properties ,61. GAPDAside from aconitase and GAPDH, RNA binding by metabolic enzymes has been reported many times (reviewed by Ciesla ) and incIdentification of such a relatively large group of bi- or multi-functional enzymes, with RNA-binding properties, has led to the formulation of the RNA–enzyme–metabolite (REM) hypothesis, according to which moonlighting metabolic enzymes mediate the connections and, possibly, cross-talk between cellular metabolism and gene expression .4.Regulation at the RNA level was traditionally understood in terms of RBP-mediated control over transcript quality/stability, so only coding sequences were considered and only in the context of regulation by proteins. However, non-coding RNA constitutes the prevailing part of eukaryotic transcriptomes, especially in more complex species. It is becoming more and more obvious that this huge transcriptional effort has a function and these RNAs are not junk or molecular fossils, but functional elements of sophisticated regulatory circuits.There are different types of non-coding RNAs, including well-known RNAs with specific functions, such as rRNAs or tRNAs in translation, snRNAs involved in splicing, nucleolar snoRNAs, which guide chemical modifications of other RNAs, and, finally, the two big classes of regulatory ncRNAs: small non-coding RNAs  and lncRNA (more than 200 nt) with a growing list of different functions, including the regulation of chromatin accessibility and transcription, miRNA sponging, pathway regulation or direct regulation of specific proteins. The non-coding part also includes introns, which constitute most of the eukaryotic pre-mRNA, and their functionality is still enigmatic.As demonstrated in Thus, in this part of the review, we would like to address the changing perspective on RBP targets, their genomic distribution, newly assigned functions and their role in building eukaryotic regulatory networks.4.1.Analysis of the genomic distribution of protein-binding RNA motifs revealed that a large proportion of these motifs is localized within non-coding parts of the genome. The most popular regions include introns and the 3′UTRs of mRNA ,68. This4.1.1.Intronless genes constitute only a small and, surprisingly, quite evolutionarily recent part of eukaryotic genomes . IntronsDepending on the function of RBPs, the proportion of intron binding may vary from around 10–20% for proteins involved in mRNA transport and translation or mRNA degradation  to more than 80% in splicing, polyadenylation and RNA processing-associated proteins . ExampleProtein HuR, which is usually perceived to be involved in post-transcriptional regulation, was also shown to bind abundantly to intronic sequences , suggestXenopus oocytes [While most intronic sequences are considered to degrade shortly after incision, some persist either in a form of sequences retained in mRNA or as a stable intronic sequence RNA (sisRNA), a new class of non-coding RNA identified in  oocytes . Functio oocytes .4.1.2.In addition to conventional control executed by RBPs on RNA metabolism , there is some new evidence showing that they have an impact on microRNA-regulated decay. While their participation in miRNA processing is obvious, including maturation controlled by microprocessor, export, cleavage by Dicer and RISC-assisted silencing, there are some reports pointing to their role in the regulation itself, not only as molecular machines that execute the programme but also as a part of the decisive switch.It has been known for a long time that mRNA stability can be controlled by RBP binding to the 3'UTR of the transcripts –83. SomeSince 3'UTRs turned out to be the primary target of microRNA-mediated regulation, it is only logical to assume that there might be some competition (or cooperation) between classical RBPs and miRNAs. While only a comprehensive and exhaustive analysis of coincidence in binding of RBPs and miRNAs to specific 3'UTR targets can decisively prove the scope of these interactions, there is some evidence to support its existence. It has been shown, using RIP-chip and PAR-CLIP, that the well-characterized RBP HuR binds to many of the same targets (75%) as protein Ago (RISC complex component) , suggestRecently, another potential role of the 3′UTR in eukaryotic regulation has emerged. In prokaryotes, the assembly of the most protein complexes occurs co-translationally by sequential translation of the polycistronic mRNA. It has been shown that, in eukaryotic organisms, the assembly of many protein complexes also occurs co-translationally . The aut4.1.3.LncRNAs are probably the most promising source of eukaryotic complexity, since, to date, only a small fraction of them have an assigned function and their regulatory abilities seem to be vastly underappreciated. The ENCODE project has annotated about 16 000 lncRNA genes (28 000 distinct transcripts) in humans . NONCODETo date, functions have been assigned to many lncRNAs, but specific RNA–protein interaction has not been described in all cases. Some of these confirmed interactions are listed in One of the most interesting examples of lncRNA regulation is polycomb repressive complex 2 (PRC2), a histone methyltransferase active in epigenetic silencing during development and carcinogenesis ,101. It 4.2.The existence of regulatory units of higher order in eukaryotic cells was proposed many years ago . Recent ex vivo data, the Tartaglia group was able to probe the expression patterns of about 1000 human RBPs and their mRNA targets, revealing that, for the predicted mRNA–RBP pairs, the expression patterns were strongly correlated or anti-correlated [et al. [First predictions of RNA–protein interactions were based on amino acid sequence similarities and the presence of the known RBDs (e.g. RNApred ). In 201rrelated . Interes [et al.  performe [et al. . RNA–RBP [et al. .It was observed that proteins which interact with multiple RBPs are frequently RBPs themselves . Thus, tThese new computational approaches should allow further characterization of the RNA–RBP regulatory networks, which control whole processes and pathways in an intricate and largely unknown manner.5.Considering the emerging scope of the regulatory effort in complex Eukaryota, it appears that what we currently perceive is only the tip of the iceberg. With the advent of reliable high-throughput techniques, enabling analysis of whole transcriptomes and proteomes, it transpires that the number of RBPs is much higher than previously estimated and that their RNA-binding modes may differ substantially—from RBPs with clearly defined, structured RBDs, moonlighting proteins with dual specificity, to unstructured, multitasking IDPs. Of particular interest is the situation in which the protein does not possess any known RNA-binding motifs, but still binds a large number of RNAs with low affinity. As can be seen in the example of polycomb, such a binding mode may have profound regulatory consequences. Specific binding with low affinity is characteristic of RNA-binding IDPs, and their role in RNA regulation is increasingly being recognized. In the past few decades, we have observed an important paradigm shift—from the static image, in which only well-folded proteins with defined domains were able to interact and function—to a dynamic, far more complex picture, where conformational plasticity is required for full functionality. Intrinsic disorder is crucial for this plasticity and, as it appears, for the formation of RNA–protein functional units, in the form of either macromolecular complexes or RNA granules.Another change of perspective considers the role of RNA in ribonucleocomplexes. From Thomas Cech's discovery of catalytic RNAs , it is kOverall, as stressed in this review, formerly unappreciated features such as structural disorder or non-coding properties play a crucial role in shaping eukaryotic regulatory networks and, as a result, eukaryotic complexity.