Yet, encrusting sponges dominated reef biomass (35 ± 18%). Based on our quantification of exposed and cryptic reef communities using different metrics, we suggest adjustments to current monitoring approaches and highlight ramifications for evaluating the ecological contributions of different taxa to overall reef function. To this end, our metric conversions can complement other benthic assessments to generate non-invasive estimates of the biovolume, biomass, and elemental composition  of Caribbean coral reef communities.A paramount challenge in coral reef ecology is to estimate the abundance and composition of the communities residing in such complex ecosystems. Traditional 2D projected surface cover estimates neglect the 3D structure of reefs and reef organisms, overlook communities residing in cryptic reef habitats , and thus may fail to represent biomass estimates needed to assess trophic ecology and reef function. Here, we surveyed the 3D surface cover, biovolume, and biomass  of all major benthic taxa on 12 coral reef stations on the island of Curaçao (Southern Caribbean) using structure-from-motion photogrammetry, coral point counts, in situ measurements, and elemental analysis. We then compared our 3D benthic community estimates to corresponding estimates of traditional 2D projected surface cover to explore the differences in benthic community composition using different metrics. Overall, 2D cover was dominated  by non-calcifying phototrophs , but their contribution to total reef biomass was minor (3.2 ± 0.6%). In contrast, coral cover (32 ± 2%) more closely resembled coral biomass (27 ± 6%). The relative contribution of erect organisms, such as gorgonians and massive sponges, to 2D cover was twofold and 11-fold lower, respectively, than their contribution to reef biomass. Cryptic surface area (3.3 ± 0.2 mThe online version contains supplementary material available at 10.1007/s00338-021-02118-6. Ecological models offer insights into complex community dynamics and biogeochemical cycling within ecosystems, but depend on accurate abundance estimates  of taxa comprising communities , we aimed to determine a baseline for the benthos on the leeward reef slopes of Curaçao, with potential implications for our overall understanding of the ecosystem functioning of coral reefs.At present, we do not know to what extent 2D-projections of 3D organisms and the exclusion of cryptic communities in benthic reef surveys influence estimates of the relative abundances and biomass distributions of benthic reef organisms. Therefore, we created an overall census of all major sessile benthic taxa  in both exposed and cryptic habitats along the leeward fringing reef on the Caribbean island of Curaçao. First, we determined exposed and cryptic surface areas per m2 m−2planar reef),3D reconstructions of cryptic and exposed reef surface areas  of the benthic groups occurring on exposed horizontal and vertical, and cryptic reef surfaces,3 m−2planar reef),volumes of all erect organisms (gorgonians and massive sponges) . Sites were chosen to cover a representative range of reef types  along the southwest coast of Curaçao (WAITT-Institute −2 or g cm−3) of 52 species (Online resource 2). All measures were combined to estimate site-specific  and island-wide  relative abundances of benthic reef organisms in terms of 3D surface area, biovolume, and biomass.Additional measurements included tissue thicknesses (in cm) of 30 species and various biomass measures  can be interpreted as measurements of reef rugosity in 3D, although scale differences need to be considered when comparing rugosities determined by different methods. The 3D reconstructions were based on 200–350 overlapping (> 60%) images of all photographable surfaces within each quadrat—excluding small cryptic crevices or deep holes, which were quantified in situ (see below)—using a GoPro HERO6 Black camera  and three GoBe 800-lm video lights to improve uniform illumination of all visible surfaces. We applied a structure-from-motion method for image acquisition and 3D-model generation that was previously established for structural complexity and surface analysis of coral reef 3D reconstructions . Pre-processed images were exported as JPEG files at maximum compression quality and uploaded in Agisoft Photoscan . This program was used to create 3D representations of all visible surfaces in reef quadrats using the following configurations: (1) align photographs: generic image pre-selection, 20,000 key point limit and 8000 tie point limit, (2) dense reconstruction: mild depth filtering, medium density, (3) mesh reconstruction: arbitrary 3D surface, 450,000 faces, and (4) build texture: generic mapping mode, mosaic blend, single texture (4096 × 1). The marker and rule tools in Photoscan were used to calibrate all 3D models using the Falcon tubes and the spirit levels for scale. Models were aligned horizontally in reference to the spirit levels and cropped to a 1 by 1 m planar footprint  using the rectangular cropping tool in Photoscan and the four Falcon tubes as reference points. Finally, quadrat models  were saved as Wavefront OBJ files and imported into MeshLab  images were resized to 2500 × 1875 pixels using the Lanczos resampling algorithm, (2) color adjusted to compensate for site-specific differences in lighting, and (3) an automatic contrast enhancement step was added to facilitate detection and identification of organisms and reef structures. All pre-processing of images was done in XNConvert (θ) of each mesh element toward the water surface was computed as the perpendicular vectors of the element . Third, each element’s exposure to external observers was estimated based on an ambient occlusion algorithm , was extracted in MeshLab. First, 3D quadrat models were resampled by applying a clustering decimation filter using a cell size of 5 mm to ensure resolution consistency across all reconstructions, after which a two-step Gaussian smoothing filter was applied to remove noise . Filter parameters used were: (1) directional bias: 0.5, (2) requested views: 128, (3) lightning direction:  (Nadir pointing Sunlight source), and (4) cone amplitude: 60°. Second, the angle (θ < 45°) and rather vertical (45° ≤ θ < 135°) surfaces to account for the fact that communities on these substrates differ  or at the base of stalking corals , these corals were photographed from several angles . The images were scaled in ImageJ (version 1.X)  and bases of stalking corals , which we then applied to our 3D surface area estimates for these corals to calculate the cryptic surface underneath these corals. Total cryptic surface area was estimated by adding these additional cryptic surfaces to our 3D reconstructed cryptic surfaces. Since smaller and highly inaccessible cavities and crevices could not be accessed, we acknowledge that our estimates of cryptic surface areas must be considered underestimations and are therefore conservative.Small crevices and deep holes inaccessible for our camera did not appear in the photogrammetric 3D reconstructions, so their surfaces and volumes (Online resource 3) were measured by hand in situ and approximated based on formulas of closest known geometrical shapes . To account for the lack of fine-scale substrate rugosity associated with geometrical approximations, the ratio of image-based area over hand-measured area was calculated in three accessible caves. The average ratio  was then used to correct for the underestimation of surfaces from geometric-based cave measurements compared to photogrammetric 3D reconstructions. To quantify cryptic surfaces existing on the underside of sheeting . Sponges were identified to the lowest taxonomic level possible based on photographs and field surveys. The species were further aggregated into seven major benthic groups: gorgonians, massive sponges, scleractinian corals, encrusting sponges, non-calcifying phototrophs, calcifying algae, and others . An INON S-2000 strobe  was used to ensure proper lighting and avoid shadows. Subsequently, for each quadrat, three side-view images of haphazardly determined vertical reef surfaces were made with the same camera equipment to estimate vertical cover  per image). Whenever possible, we chose one surface parallel to the reef slope, and two surfaces perpendicular to the reef slope: one up- and one down-current to the long-term mean current from SE to NW. Lastly, three cryptic surfaces per quadrat were photographed using an Olympus Tough TG-5 underwater compact camera  to estimate cryptic cover . Specifically, for these cryptic surfaces we chose, where possible due to space limitations, one ‘roof’ surface, one ‘side’ surface, and one ‘back’ surface per quadrat. Benthic community composition in each image was analyzed using point-count analyses (80 stratified random points per picture) in Coral Point Count with Excel extension (CPCe)  underwater camera was used to first obtain one 1 mSome substrates were covered by communities through which the community on the underlying substrate could still be seen . In those cases, only the overlying turf algae was counted in 2D cover estimates, while for all 3D estimates (see below), the percentage bottom cover of both overlying and underlying communities was estimated separately so that total cover estimates exceeded 100% in some cases (see Online resource 5).i for benthic group i) was first calculated for horizontal and vertical exposed and cryptic surfaces separately. Average substrate surface areas  obtained from the 3D reconstruction of the reef were multiplied by the average relative 2D cover (Ci) of a benthic group across all 16 quadrats (or 15 for site Jeremi) per site. For example, the 3D surface area of benthic group i on horizontal surfaces (denoted by the subscript ho) was calculated as:i):δ refers to standard error (SE). Estimates for horizontal and vertical exposed and cryptic surfaces were summed up to obtain total 3D surface area per site for benthic group i , Online resource 6). The standard error of the total surface area was obtained by propagating the standard errors from Eq.  to calculate overall biovolume in dm3 m−2planar reef.Biovolumes of erect organisms  were estimated by dividing each individual into one or more geometrical forms  that were measured in situ using a measuring tape or ruler, taking into account accessible empty space . This was done because individuals assigned to these groups often moved (in the current) and as a consequence could not be reconstructed in 3D through structure-from-motion techniques. To ensure that all erect organisms were measured in a similar manner, we also hand-measured individuals that did not move in the current. The cuboid geometrical form was used to estimate the biovolume of all non-erect organisms by multiplying their 3D surface area to their average thickness. The thickness of organic tissues for each non-erect species was derived from side-view photographs of broken colonies in ImageJ. Since we considered all calcified framework as substrate , the coral tissue itself represents a thin layer overlying such structures (Edmunds and Gates Dictyota spp. and Lobophora spp.) were difficult to discern on CPCe images and therefore assessed as a single benthic group. Further, these algae grow in loose bundles of leaves that can be more or less dense. This means the empirical relationships between canopy height and tissue volume for a given surface area may not be linear, rendering the cuboid calculation potentially erroneous. We therefore first calculated the empirical regression between canopy height and tissue volume for a total of 9 tissue samples of each species covering 25–160 cm2 of substrate with varying canopy height . For each sample, thallus surface area was estimated by laying out all thalli on a white sheet and taking scaled images to be analyzed for total surface area in ImageJ. The tissue volume of these samples was calculated by multiplying thallus surface area with average thallus thicknesses of 70 µm for Dictyota and 40 µm for Lobophora, respectively  into these empirical regressions to calculate conversion factors  from 3D surface area to volume for that quadrat and surface type (see Online resource 1 for specific equations). Empirical regressions differed between Dictyota spp. and Lobophora spp., and the average conversion was used to calculate the biovolume of macroalgae. Total and exposed biovolumes for all benthic groups were obtained by summing estimates of volumes on horizontal and vertical surfaces with and without cryptic surfaces, respectively, as described above for 3D surface area.The dominant macroalgae  were collected for representative species of all benthic groups (n = 3–8 samples per species)  across the surveyed area using different approaches specific to each group (see below). Tissue samples were collected to estimate tissue thickness, ash-free dry weight (AFDW), organic carbon (Corg), and organic nitrogen (Norg). Samples rich in calcium carbonate  were collected by chiseling out fragments containing the entire tissue layer  and subsequently stored in individual 50-mL Falcon tubes. Samples of organisms with embedded skeletal structures  were collected by carefully scraping or cutting off a tissue area or volume of known size, using a sharp blade. Sampling of epibionts was avoided and samples were stored in 15-mL falcon tubes. Non-calcifying phototrophs and soft coelobites  were carefully collected using tweezers and stored in zip lock bags. All samples were photographed in situ and transported to the lab for processing within 2 h, where they were photographed again next to a scale. Samples were quickly rinsed in deionized and distilled water  to remove excess salts, and subsequently freeze-dried . Sample dry weight was determined on a precision scale (± 0.01 mg), after which samples were homogenized  for 8 min, at a relative centrifugal force of 22×g, and divided equally into three aliquots. One aliquot was weighed, combusted at 450 °C for 4 h, and weighed again to determine AFDW. The remaining two aliquots were both acidified to remove inorganic C using 4 mol L−1 hydrochloric acid until effervescence ceased  on a carbon, hydrogen, nitrogen, sulfur elemental analyzer . H and S contents were not extracted from the output file.Tissue samples of various sizes (0.7–20 cmAcropora cervicornis (factor = 0.95), Agaricia spp., Mycetophyllia spp., and Pseudodiploria strigosa. The latter three species were converted using the ratio determined for the morphologically most similar species Montipora spp. . Conversion factors to CT-scanned surface area estimates , biomass was estimated by surface-to-biomass conversion factors (Table S2) multiplied by their 3D surface areas (Online resource 8). The same calculations (see Online resource 1) were used to estimate standing stocks of organic carbon (Online resource 9) and nitrogen (Online resource 10), but using weight estimates for Corg and Norg, respectively , rather than AFDW.To calculate the biomass of non-calcifying phototrophs, we used the same approach as described above for the biovolume of macroalgae, converting 3D surface area to biomass using the empirical regression between canopy height and AFDW for a given area . For all erect organisms, whose volumes were measured directly in situ (Online resource 7), biomass was estimated by volume-to-biomass conversion factors (Table S1) multiplied by their in situ-measured biovolumes. For encrusting sponges, corals, crustose coralline algae, and other coelobites  for the seven major benthic groups Table  were genSince relief is commonly used as proxy for structural complexity on reefs, we tested whether relief predicts our measured surface areas . All data were log transformed prior to analysis to obtain normal distributions . Simple linear regression was used with relief as predictor variable. Dependent variables were either cryptic, exposed, or total surface area per quadrat. Results were then back-transformed for plotting. A roughly uniform spread of residuals verified homoscedasticity of the underlying data.We characterized the composition of reef communities between 9 and 14 m water depths along the leeward shore of Curaçao in terms of relative 2D and 3D benthic cover, biovolume, and biomass. We found that surveys taking into account the three-dimensional nature of coral reefs and reef organisms, and using biomass rather than cover as an abundance metric, greatly altered the relative contribution of dominant benthic groups to overall community composition.2 m−2planar reef) per site ranged from 5.2 ± 0.4  to 7.6 ± 1 . Approximately half of the total reef surface area consisted of cryptic surfaces (island-wide average: 3.3 ± 0.2 m2 m−2planar reef). The maximum reef surface observed within a quadrat was located at Jeremi and reached 17.4 m2 m−2planar reef, of which 82% was cryptic surface. Relief as a proxy for reef flatness correlated significantly with total surface area, but explained only 16% of the variation . This correlation was stronger for cryptic substrates  Fig. .Fig. 2SiAccording to a traditional assessment, using the percent relative cover estimated by 2D projection of exposed reef surfaces, the most abundant benthic groups were non-calcifying phototrophs (52 ± 2%) and scleractinian corals 32 ± 2%)  reef surface areas, non-calcifying phototrophs were still the largest benthic group  and dominated the exposed reef surface together with scleractinian corals 11 ± 2%)  and gorgonians 6 ± 1%) became profoundly more dominant on the exposed reef surface , whereas encrusting sponges dominate the cryptic habitat  and can represent a staggering 35% of the total biomass in benthic reef communities  sponges and the overall ecological role of sponges under changing ocean conditions are still under debate . Miller et al. , turf algae , and all non-calcifying phototrophs  fall closely within these ranges  surface cover Fig. . This war et al.  comparedes Table , which aThe abundance metrics portrayed here have different strengths and weaknesses. Organic biomass and element analysis  estimates—though limited in scaling opportunity—provide the most comprehensive insights into the standing stocks and biogeochemical cycling of energy and (in)organic nutrients in both aquatic and terrestrial ecosystems , they should also be incorporated into standing stock assessments, along with motile invertebrates, to improve our understanding of how benthic-pelagic interactions shape marine ecosystems and their response to environmental change.There are some important limitations regarding our biomass estimates. First, they must be considered conservative estimations, since despite our best efforts to quantify all existing benthic organisms, some biota residing within inaccessible cavities and endolithic taxa, such as bioeroding sponges or other burrowing organisms, were not included due to logistical constraints and to avoid destructive practices associated with such measurements  lead to markedly different perspectives on benthic reef community composition. These different abundance metrics serve different ecological questions. For example, conventional 2D approaches may provide the best balance between accuracy, scale, and required resources if the goal is to indicate general reef health based on the relative proportions of, for example, scleractinian corals and macroalgae. But the same conventional approach structurally overestimates the biomass contribution of conspicuous primary producers , while underestimating the contributions of reef taxa with erect morphologies  that have important functions in nutrient cycling, biodiversity, and reef productivity (Ferrier-Pagès and Gattuso


The rapidly growing popularity of RNA structure probing methods is leading to increasingly large amounts of available RNA structure information. This demands the development of efficient tools for the identification of RNAs sharing regions of structural similarity by direct comparison of their reactivity profiles, hence enabling the discovery of conserved structural features. We here introduce SHAPEwarp, a largely sequence-agnostic SHAPE-guided algorithm for the identification of structurally-similar regions in RNA molecules. Analysis of Dengue, Zika and coronavirus genomes recapitulates known regulatory RNA structures and identifies novel highly-conserved structural elements. This work represents a preliminary step towards the model-free search and identification of shared and conserved RNA structural features within transcriptomes. SHAPEwarp is a method that allows identifying structurally-similar RNAs by direct comparison of reactivity profiles derived from chemical probing experiments. Its application to viral genomes identified conserved RNA structure elements. Infernal relies on probabilistic models, called covariance models2 (CMs), which describe both the consensus sequence and secondary structure of a family of related RNAs, and it uses a dynamic programming algorithm to identify putative structurally-homologous RNAs that can be optimally aligned to the CMs. While this method is extremely accurate and powerful, it depends on the availability of both a high-quality alignment of related RNA sequences and an accurate secondary structure model for the RNA family of interest. Although determining the structure of an RNA is a nontrivial task, the introduction over the last decade of a wide range of methods, mostly based on the coupling of RNA structure probing and high-throughput sequencing, has significantly improved our ability to interrogate the structure of thousands of RNA molecules in a single experiment3. Among these, methods based on chemical probing are probably the most popular. Chemical probing allows to rapidly query RNA structures in a variety of contexts, including the complex cellular environment, by taking advantage of specific chemical probes that can selectively react with RNA nucleotides, in a way that is directly correlated with their structural context4. Dimethyl sulfate5 (DMS), a nucleobase-specific probe for unpaired A and C bases, and Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) reagents8, which react with the 2′-OH of the ribose moiety on structurally-flexible residues, are widely employed probes as they can readily permeate cell membranes, allowing to study RNA structures in their physiological context. The readout of these experiments is a reactivity profile that informs on the reactivity of each nucleotide in the RNA to the employed chemical probe. Such reactivity profiles can be used in conjunction with free energy minimization algorithms to derive an experimentally-informed structure model of the RNA of interest. This is typically done by converting the experimentally-determined reactivities into pseudo-free-energy contributions, that are used to adjust the thermodynamics parameters (known as nearest-neighbor model9) in order to constrain the RNA structure prediction and yield a structure model that better agrees with the observed reactivity profile10, although many alternative approaches have been also proposed14. However, the accuracy of the inferred structure models largely relies on a multitude of factors, including the employed chemical probe, the approach used to incorporate the experimentally-determined reactivities into the structure prediction, and a number of method-specific analysis parameters. As a consequence, deriving high-quality RNA secondary structure models is a nontrivial task, as minimal changes in the analysis protocol might significantly affect the reliability of the inferred RNA structure model. Furthermore, the growing interest in RNA as a target for small molecule drugs underscores the importance of this challenge, as well as the need for tools able to evaluate if a candidate RNA structure can be selectively targeted, or, in other words, whether a candidate target is sufficiently unique within the transcriptome15. To fill this gap, we here introduce SHAPEwarp, a model-free and sequence-agnostic method for the identification of structurally-similar RNA regions in a database of chemical probing-derived reactivity profiles. By applying SHAPEwarp to a number of SHAPE datasets for viral RNA genomes we demonstrate that, not only SHAPEwarp is able to recover known functional RNA structures, but most importantly that it can be used to drive the identification of previously unidentified conserved RNA structure elements.A key need in RNA biology is the ability to identify structurally-homologous  RNAs. One of the most widely used methods for RNA structure homology search is Infernal16 and it builds on top of two widely used methods for similarity search in time series data: Mueen’s Algorithm for Similarity Search (MASS)17 and dynamic time warping (DTW). In a first step  in just O(n2). Additionally, the percent GC content of the kmer and its matches are evaluated and matches having GC content deviating by more than a certain percentage from that of the query kmer, are discarded. Indeed, analysis of multiple sequence alignments for known RNA families from RFAM suggests that the local percent GC content of structurally-related RNAs tends to show only limited variation  , a powerful SHAPE reagent we recently developed7, and validated on the 16S rRNA. E. coli 16S rRNA was first split into 200 nucleotides-long windows, with 100 nucleotides overlap, and used to query B. subtilis rRNAs  with respect to the distribution of E-values for false matches  , each time by using one serotype as the query dataset and the other serotype as the database. Once again, SHAPEwarp showed a remarkably low FDR . In all comparisons but one , SHAPEwarp matched, to different extents, the 5′ and 3′ UTRs of the genomes, known to carry several key RNA regulatory elements  , as it respectively matched ~75% and ~70.5% of the scanned ZIKV and DENV genomes, versus 0% and ~1.5% respectively matched by the RFAM model  comparing the Asian and African lineages of Zika virus (ZIKV) by in vivo probing with NAI6 and we further generated two new datasets by probing the structure of both SARS and SARS-CoV-2 coronaviruses (CoVs) with 2A3. SHAPEwarp identified extensive structural homology between the compared genomes for both ZIKV  and CoVs . The identified structurally-homologous regions encompassed known structural elements . Besides known structures in the UTRs, application of our combined SHAPEwarp+cm-builder pipeline resulted in the identification of eight novel conserved RNA elements, of which five in ZIKV and three in CoVs , all eight structures but one (ZIKV Motif #4) showed significant in vivo support by direct RNA–RNA interaction capture via COMRADES32 in infected host cells, further underscoring their functional significance. Lack of significant support by COMRADES for ZIKV Motif #4, is most likely the consequence of its limited size. In a recent study we introduced DRACO25, a method for the deconvolution RNA structure ensembles from DMS-MaPseq33 data, and used it to identify multiple regions within the SARS-CoV-2 genome folding into two alternative structures. Comparison of SARS-CoV-2 structurally-heterogeneous regions with the three structurally-conserved structure elements here identified showed that CoV Motif #3 partially overlaps with one of the regions identified by DRACO to form two alternative conformations. The deconvolved reactivity profile for the major conformation (59.0 ± 1.5%) well agrees with the structure here identified by our SHAPEwarp + cm-builder pipeline. Importantly, analysis of the minor conformation (41.0 ± 1.5%) with cm-builder revealed that weaker but significant covariation and COMRADES support exist also for this structure  Fig. . We perfWe further evaluated the performances of SHAPEwarp with queries of different lengths  and compared the results to those previously obtained with 200 nucleotide-long queries. To this end, we calculated the fraction of correctly matched 200 nucleotide-long windows , that overlapped with at least one correctly matched window at each of the evaluated query lengths, imposing a minimum overlap of 25% . For SHAPE only, this resulted into overlaps of 54%, 86% and 97.2% respectively for 100, 150, and 250 nucleotide-long queries. For SHAPE + sequence, the overlap was way more extensive, with 92.1%, 99.3%, and 99.5% respectively for 100, 150, and 250 nucleotide-long queries. The marked difference in overlap with shorter queries observed between SHAPE only and SHAPE + sequence searches can be easily explained by the very low probability of finding by chance a match having both similar SHAPE reactivity and nucleotide sequence. Thus, the difference between the score of a true match and the distribution of alignment scores from the shuffled database tends to be way more significant when sequence identity is rewarded as well. This difference can be mitigated by increasing the E-value cutoff for shorter queries. Indeed, by simply increasing the E-value cutoff from 0.01 to 0.05, the overlap for 100 nt-long queries increases from 54% to 76%, while keeping the FDR below 5%.In summary, we have here introduced SHAPEwarp, a SHAPE-driven sequence-agnostic method for the identification of structurally-similar RNA regions. We can anticipate that SHAPEwarp will greatly facilitate the discovery of shared and conserved RNA structural features within transcriptomes.The SHAPEwarp algorithm is implemented in Perl and Rust. SHAPEwarp comes with three components: SHAPEwarp, swKmerLookup and swBuildDb. The SHAPEwarp module allows searching one or more queries in a database of SHAPE reactivities (or base-pairing probabilities). The swKmerLookup module (invoked by SHAPEwarp) is responsible of performing both the kmer lookup and kmer grouping into HSGs. Finally, the swBuildDb module allows generating SHAPEwarp-compliant databases of SHAPE reactivities (or base-pairing probabilities). A complete description of the algorithm is provided in Supplementary Note E. coli and B. subtilis rRNAs, DENV and SARS-CoV-2 (probed with NAI) were obtained from the Gene Expression Omnibus (GEO) database . Pre-normalized icSHAPE data for ZIKV was obtained from Li et al.29. COMRADES datasets for ZIKV and SARS-CoV-2 virus in living infected host cells were respectively obtained from ArrayExpress (E-MTAB-6427) and GEO (GSE154662).SHAPE-MaP data for 34. All tools referenced in the following paragraphs are distributed as part of the RNA Framework suite (https://github.com/dincarnato/RNAFramework). The following parameters were used: rf-map , rf-count (parameters: -m -rd), and rf-norm (parameters: -sm 3 -nm 3 -mm 1 -n 1000). For the analysis of DENV SHAPE-MaP data, the -n parameter of rf-norm was lowered to 500, to ensure the inclusion of genome boundaries. Only replicate 1 of each experiment was used, given the exceptionally high inter-sample correlation.All the analysis steps, from reads alignment to data normalization, were performed using RNA Framework7 and 1.1 and 0.0 for NAI23. For NAI-N3, a reference structure including the known 5′ and 3′ UTR elements of ZIKV was built for the Asian strain (PRVABC59) and optimal values 2.4 and −0.6 were identified by grid search using the rf-jackknife tool (parameters: -rp “-md 600 -nlp” -x) and ViennaRNA v2.4.1438, and by selecting the prediction achieving the highest sensitivity.For base-pair probability calculation and structure modeling, the following previously determined slope and intercept pairs were used: 1.0 and −0.4 for 2A339. Briefly, Vero E6 cells were cultured in DMEM , supplemented with 8% FCS (Bodinco), 2 mM L-glutamine, 100 U/mL of penicillin and 100 µg/mL of streptomycin . Cells were infected with either SARS-CoV or SARS-CoV-2 in EMEM  supplemented with 25 mM HEPES, 2% FCS, 2 mM L-glutamine, and penicillin/streptomycin. 16 h after the infection, infected cells were harvested by trypsinization, followed by resuspension in EMEM supplemented with 2% FCS, washed with 50 mL 1X PBS, and then resuspended in 1 mL of QIAzol Lysis Reagent . All experiments with infectious SARS-CoV/SARS-CoV-2 were performed in a biosafety level 3 facility at the LUMC.Infection with SARS-CoV and SARS-CoV-2 was conducted as previously describedg (4 °C). The upper aqueous phase was collected in a clean 2 mL tube, supplemented with 1 mL (~2 volumes) of 100% ethanol, and then loaded on a Monarch® RNA Cleanup Kit column . In vitro folding was carried out as previously described40. Briefly, ~5 μg of total RNA from infected cells were first depleted of ribosomal RNAs using the RiboMinus™ Eukaryote System v2 , as per manufacturer instructions. rRNA-depled RNA in a volume of 39 μl was denatured at 95 °C for 2 min, then transferred to ice for 1 min. 10 μl of ice-cold 5X RNA Folding Buffer  supplemented with 20 U of SUPERase•In™ RNase Inhibitor  were added. RNA was then incubated for 15 min at 37 °C to allow secondary structure formation. Subsequently, 1 μl of 500 mM MgCl2 (pre-warmed at 37 °C) was added and RNA was further incubated for 15 min at 37 °C to allow tertiary structure formation.To 1 mL of infected cells in QIAzol Lysis Reagent, 200 μl of chloroform were added. The sample was vigorously vortexed for 15 sec and then incubated for 2 min at room temperature, after which it was centrifuged for 15 min at 12,500 × ® RNA Cleanup Kit columns.For probing of RNA, 2A3 was added to a final concentration of 100 mM (assuming a stock concentration of 1 M). An equal volume of DMSO was added to the control samples. Samples were then incubated at 37 °C for 5 min. Reactions were quenched by the addition of 1 volume DTT 1 M and then purified on Monarch24, with minor changes. Probed RNA was first fragmented to a median size of 150 nt by incubation at 94 °C for 8 min in RNA Fragmentation Buffer , then purified on Monarch® RNA Cleanup Kit columns and eluted in 8 μl NF H2O. Eluted RNA was end repaired by treatment with 1 U of rSAP  at 37 °C for 30 min, plus 5 min at 70 °C to heat-inactivate the enzyme, followed by treatment with 20 U of T4 PNK , in the presence of 1 mM ATP, at 37 °C for 1 h. 50 ng of end-repaired RNA were first ligated to 10 pmol of a pre-adenylated 3′ adapter (rApp-AGATCGGAAGAGCACACGTCT-SpC3) using 200 U of T4 RNA Ligase 2 truncated KQ , in the presence of 12.5% PEG-8000, for 1 h at 25 °C. 10 pmol of a 5′ RNA adapter (CUACACGACGCUCUUCCGAUCU) were then ligated using 30 U of T4 RNA Ligase 1 , in the presence of 1 mM ATP and 8% PEG-8000, at 25 °C for 1 h. The adapter-ligated RNA was then supplemented with 10 pmol of RT primer (AGACGTGTGCTCTTCCGATCT), incubated at 70 °C for 5 min and immediately transferred to ice for 1 min. Reverse transcription reactions were conducted in a final volume of 10 μl. Reactions were supplemented with 2 μl 5X RT Buffer , 1 μl DTT 0.1 M, 0.5 μl dNTPs 10 mM, 0.5 μl MnCl2 120 mM, 20 U SUPERase•In™ RNase Inhibitor and 100 U SuperScript II . Reactions were incubated at 42 °C for 2 h. RNA was degraded by addition of 1 μl NaOH 5 N, followed by incubation at 95 °C for 3 min. After purification, barcodes were introduced by PCR, using NEBNext® High-Fidelity 2X PCR Master Mix  and NEBNext® Multiplex Oligos for Illumina® Dual-Index primers .SHAPE-MaP library preparation was conducted as previously describedE. coli and B. subtilis 23S rRNAs we previously generated7 and used a structurally-informed alignment of the 23S rRNAs from the two species  as a reference. For the calibration of kmer lookup and kmer grouping parameters, a 200 nt-long window was slid along the E. coli 23S rRNA, with an offset of 100 nt, and used to query the B. subtilis 23S rRNA, using any possible combination of the following paramters: maxKmerDist ; kmerLen ; minKmers ; kmerMinComplexity ; maxReactivity ; kmerMaxMatchEveryNt . A query was considered to represent a true positive match if at least one HSG overlapped by at least 1 nt the expected position in the reference alignment. Any other match was regarded as false positive. The set of parameters having the highest ratio of true positive matches to the average number of false positives per window, was picked. For the calibration of alignment parameters, we proceeded in two steps. First, by using a subset of 15 true positive HSGs to seed the alignment of 15 × 200 nt-long queries, alignment extension was performed using any possible combination of the following parameters: alignMatchScore ; alignMismatchScore ; maxReactivity . During the first step, the following parameters were set to a fixed value: alignGapOpenPenal (−12); alignGapExtPenal (−5); alignMaxDropOffRate (0.7); alignMaxDropOffBases (5). After having computed the score of the true alignments, the reactivity values around each HSG in the database were randomly shuffled 100 times and aligned to the original query. The null distribution built by using the scores of the alignments to the shuffled database was then used to calculate the p values of the true alignments, by using the extreme value distribution ; alignGapExtPenal ; alignMaxDropOffRate ; alignMaxDropOffBases . Once again, the p value of each alignment was calculated using the aforementioned procedure, and the set of parameters resulting in the lowest average p value for the searched queries was selected. This procedure resulted in the following parameter sets: SHAPE reactivities  and base-pairing probabilities .To optimize SHAPEwarp parameters, we took advantage of in vivo 2A3 probing data for Databases were generated using the swBuildDb module (parameters: --chunkSize 1000), starting from RNA Framework’s normalized SHAPE reactivity XML files. For bacterial rRNAs, a single database was built including both the 16S and 23S rRNAs. For DENV, ZIKV and SARS-CoV-2 genomes, a separate database was built for each strain. Database shuffling was performed in 10 nt blocks. This approach allows generating more realistic profiles, as it is likely that nearby bases will reside within a similar structural context , hence preserving the structural features of the profile. 100 shuffles were performed for each database entry and a chunk of a maximum size of 1000 nt was extracted from each shuffled entry and used to build the shuffled database. This random sampling step does not introduce any bias (provided that the searched query is shorter than the chosen chunk size) while reducing the computation time.p value < 0.05 and a base-pair support  of at least 0.75, were retained for covariation analysis. For FDR estimation, matches falling at the same relative genomic position (with a tolerance of ±2%) in the query and database genomes were considered to represent true matches, while any other match was assumed to represent a false match. When searching NAI-derived profiles in a database of 2A3-derived profiles, the exact genomic position was required to match (0% tolerance).Query transcripts were first split into 200 nt-long windows, with 100 nt overlap. Each query window was searched against the database using the previously optimized search parameters. When accounting for sequence in the alignment (SHAPE + sequence), the following additional parameters were set: --alignScoreSeq --alignSeqMatchScore 0.5 --alignSeqMismatchScore −2. Consecutive matching windows were then merged and alignments were evaluated for the presence of conserved structure elements by analyzing the SHAPEwarp-generated alignment with RNAalifold v2.4.14. SHAPEwarp-generated alignments were then randomly shuffled 100 times, and the resulting alignments were analyzed with RNAalifold. Shuffling was performed in 3 column blocks, followed by random shuffling of the columns within each block. A null distribution was then built using the free energy of the structures inferred from the shuffled alignments. The free energy of the true alignment was then converted into a Z-score and the corresponding probability was determined by using the normal distribution. The above-described analysis steps are integrated into SHAPEwarp . Structures having a https://github.com/dincarnato/labtools), an automated pipeline we have previously introduced25, built on top of Infernal v1.1.3. Briefly, a first covariance model (CM) was built from the structure inferred by RNAalifold, using the cmbuild module. For structures <100 nt, a 100 nt window centered on the structure was extracted and used to build the CM. After calibrating the CMs using the cmcalibrate module, it was used to search for RNA homologs in a database composed of either all the non-redundant Orthocoronavirinae or Flaviviridae complete genome sequences from the ViPR database41, using the cmsearch module. Only matches from the sense strand were kept and a relaxed E-value threshold of 10 was used at this stage to select potential homologs. Matches were required to fall at the same relative position within their respective genomes, with a tolerance of 3.5% for coronavirus genomes  and 2% for flavivirus genomes . Furthermore, matches retaining less than 55% of the canonical base pairs from the original structure and truncated hits covering <50% of the structure were discarded. The whole procedure was repeated a maximum of 3 times. As compared to the original cm-builder approach, we further introduced an additional evaluation step. Briefly, at each iteration of the algorithm, the resulting Stockholm alignment was analyzed with R-scape v1.4.030 and APC-corrected G-test statistics to evaluate the presence of significantly covarying base pairs, using a relaxed E-value of 0.1. If the detected number of significant covariations was lower than that detected at the previous iteration, the alignment from the previous iteration was reported. Furthermore, the search was aborted if no significant covariation was detected after 2 iterations. Retained alignments were then manually inspected and only complex structures  having covariation support for multiple helices were retained. The selected best alignment (the one with the most significant covarying base pairs) was then refactored by removing non-structured regions at either sides and used to build the final CM.To evaluate the conservation of the predicted structure elements, we further improved cm-builder , and reads were aligned to the reference using the same . Prior to parsing, replicates were merged. Chimera support for the base pairs inferred from RNAalifold analysis of SHAPEwarp alignments was calculated using the rf-duplex tool (parameters: -st -mh 5 -xr 1e9 -eo). Briefly, alignments were filtered to discard reads having more than one gap, as well as ungapped reads and reads aligning to the human transcriptome, and the total number of reads per experiment was calculated (Ctot and Stot). To assess whether a candidate base-pair i–j was significantly enriched in the COMRADES sample with respect to the control sample, we calculated the number of chimeras for which one side of the chimera encompassed base i and the other side encompassed base j, for both samples (Ci–j and Si–j). Significance of the enrichment was then assessed using a one-tailed binomial test, with parameters k = Si–j, n = Stot, and p = Ci–j/Ctot. Base pairs having p value < 0.05 were considered to have in vivo support.Each COMRADES dataset consisted of two to three biological replicates, each one composed of a control (C) and the actual COMRADES sample (S). A reference was built on all human transcripts from refGene, plus the sequence of either the ZIKV or SARS-CoV-2 genomes, using STAR v2.7.1aFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Adenosine-to-inosine (A-to-I) RNA editing, catalyzed by ADAR enzymes, is an essential post-transcriptional modification. Although hundreds of thousands of RNA editing sites have been reported in mammals, brain-wide analysis of the RNA editing in the mammalian brain remains rare. Here, a genome-wide RNA-editing investigation is performed in 119 samples, representing 30 anatomically defined subregions in the pig brain. We identify a total of 682,037 A-to-I RNA editing sites of which 97% are not identified before. Within the pig brain, cerebellum and olfactory bulb are regions with most edited transcripts. The editing level of sites residing in protein-coding regions are similar across brain regions, whereas region-distinct editing is observed in repetitive sequences. Highly edited conserved recoding events in pig and human brain are found in neurotransmitter receptors, demonstrating the evolutionary importance of RNA editing in neurotransmission functions. Although potential data biases caused by age, sex or health status are not considered, this study provides a rich resource to better understand the evolutionary importance of post-transcriptional RNA editing. Huang et al performed a genome-wide RNA editing investigation in the porcine brain in which they found over 680,000 A-to-I RNA editing sites. They identified conserved recoding events between pig and human brains thus providing an extensive resource to aid our understanding of the evolutionary importance of post-transcriptional RNA editing. Apart from transcription, posttranscriptional RNA modifications (PTMs) contribute to expanding function and diversity of transcripts. One of these PTMs is RNA editing, which increases biologically relevant diversity of transcripts or protein isoforms2. The most common type of RNA editing in mammals is adenosine-to-inosine (A–I) editing, which is catalyzed by adenosine deaminase acting on RNA (ADAR) enzymes4. RNA-editing events modulate brain physiology within mammalian central nervous system, especially functions related to neurotransmission5. Dysregulation of RNA-editing process has been reported to be associated with several human neurological disorders, such as amyotrophic lateral sclerosis (ALS), autism spectrum disorder, and schizophrenia8. Some RNA-editing sites have evolved for indispensable PTM in mammalian development. Early postnatal death is observed in the Adar2-null mice unable to edit an mRNA transcribed from the Gria2 gene encoding an AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) glutamate receptor subunit9.The brain is the most complex organ in mammals. Recently, we investigated the brain-wide transcriptomics among human, pig, and mouse brainSus scrofa) shared a common ancestor with human about 79–97 million years ago10. Compared to rodents, pigs are more similar to humans in respect of anatomy, genetics, and physiology11. Our previous reported transcriptomics analysis revealed that protein-coding gene expression profiles in pig and human brain are highly conserved1. In addition, pig models of human diseases can well recapitulate the pathophysiology and symptoms in humans12.The domestic pig  in combination with whole-genome DNA sequencing (WGS). The regional differences and cross-species (pig–human) similarities in RNA editing were investigated across brain regions.A recent study analyzed RNA editing using samples from the Genotype-Tissue Expression (GTEx) project, representing 13 subregions in the human brain1. An average of 173 million uniquely mapped RNA reads were selected for RNA-editing analysis. Further, WGS was performed on individual pig with an average depth of 104×  were used for brain-wide RNA-editing investigation. We performed ribosomal RNA-depleted RNA-seq of 119 samples from 30 anatomically defined subregions within pig brain  within UTRs, introns, and intragenic regions. In agreement with these findings, up to 94% of edited sites identified in the pig brain were within repetitive elements. Further characterization of editing sites in repetitive elements revealed that 82.79% of all A–I editing sites were located in glutamic acid transfer RNA-derived SINEs (SINE/tRNA) , followed by 8.47% in the LINE1 elements and 2.74% in other repetitive elements  editing sites appeared in the PRESDB brain dataset. Hence, >97% of the editing sites identified in this study are previously unknown, including 1687 sites located in CDS and 11,281 sites in 3′-UTRs, respectively. The editing sites identified in this study largely extended the current knowledge of editing landscape in pigs.We compared A–I RNA-editing sites identified in this study with those of the PRESDB database, a database of porcine RNA-editing sites from 11 pig organs20 and mouse16, reported that the density of RNA editing in genes is not random. To investigate the clustering tendency, editing sites were considered as sites in a cluster if more than three sites occurred in a 100 bp sliding window, as described in Li et al.20. Although the editing sites located in 5′-UTRs and CDS were similar in number, CDS-residing sites showed a low clustering tendency  in the 5 bp regions flanking the editing sites. As compared to randomly selected “A” sites from the genome, there is a significant depletion of G in the −1 base position and enrichment of G in the +1 base position   and 3 were glutamic acid to glycine (E-to-G). Many recoding editing events are associated with altered protein function26. Gene set enrichment analysis further revealed that the genes with at least one recoding site were functionally enriched in neurotransmission-related processes, such as glutamate-gated ion channel activity  in 3′-UTRs altered candidate miRNA-binding sites and 2138 editing sites (12%) might create novel miRNA-binding sites . The number of editing sites shared by all 12 regions was 100,831. It is believed that the number of edited sites identified could increase as more sequencing data are generated8. Hence, the number of A–I edited sites were normalized by the uniquely mapped reads of each sample. We found that the normalized number were higher in the cerebellum and olfactory bulb, as compared with other regions . The number of A–I editing sites identified across 12 brain regions ranged from 240,564  to 562,002   based on the sites covered by at least 10 reads in all 30 subregions. The regions from the same brain structure, such as the cerebrum, were clustered together, suggesting that brain regions that are biologically and anatomically similar have closer RNA-editing profile Fig. . PCA anaP = 0.0011 and P = 0.003, respectively) .The overall editing levels of CDS-residing edited sites across regions were generally similar, except for corpus callosum Fig. . Howeverly) Fig. . The ove1. We focused on the RNA-editing sites located in genes with low variability of expression (coefficient of variation < 1) and normalized expression ≥ 10 in all main regions. In addition, these sites were required to be covered by at least ten reads in two-third of the main regions. Out of 271,651 editing sites analyzed, 22,275 (about 8.2%) region-specific edited events were identified, including 6359 (2.3%) region-enriched sites, 455 (0.2%) group-enriched sites, and 15,461 (5.7%) region-enhanced sites, respectively . The majority of edited events were classified as having low region specificity. The cerebellum harbored the largest number of region-specific edited sites  in each brain region. GO analysis revealed a number of biological processes and molecular functions specific or shared across brain regions  or lowly edited (<25%) sites. We found that the expression of genes with highly edited CDS-residing sites tended to be lower than that of genes with lowly edited CDS-residing sites  analyzed were required to be covered by at least 10 reads in all main regions. The median correlation coefficients were close to zero, suggesting that there was weak linear relationship for most of RNA-editing sites and genes analyzed , as well as between ADAR2 and repetitive sites (P < 2.22 × 10−16), relative to that between ADARs and coding sites , whereas ADAR2 explained 27% of the variation (P = 1 × 10−9)  and ADAR2  were broadly expressed across brain regions, with lowest expression in the corpus callosum Fig. . Furthertes Fig. . The exp28. We extended our analysis with another mRNA expression data set29, which also supported that the expression of mRNA encoding ADAR1 and ADAR2 is higher in neurons than in non-neuronal cells   is reported to be a negative regulator of active ADARs (ADAR1 and ADAR2)27. Here we conducted a survey of RNA-editing events in several major classes of neurotransmitter receptors, including adrenergic, cholinergic, dopamine, GABA, glutamate, glycine, histamine, opioid, and serotonin receptors. The editing sites located in neurotransmitter receptors were mainly found in glutamate receptors  type (GRIN3B), and metabotropic type (GRM4)  recoding site had a 21% increase in olfactory bulb over the mean editing level. The GRIN3B (Q208R) editing event was exclusively detected in the spinal cord  show low permeability to Ca2+, whereas those lacking edited GluA2 subunit show high Ca2+ permeability33. In a recent mouse model, it has been found that mice with reduced GluA2 Q/R site RNA editing exhibit signs of impairment of several neurological processes, including loss of dendritic spines, hippocampal CA1-neuron loss, learning and memory impairments, and NMDA receptor-independent seizure vulnerability34. In the pig brain, we observed that the GRIA2 transcript contained two recoding sites (Q607R and R764G) and two synonymous sites (Q608Q and L763L)  Fig. . The edi3L) Fig. , corrobo35. The function of the other recoding site GABRA3 (N327D) (9–22% edited) remains to be characterized in future studies  and HTR2C (T35A) were only found in pig brain, but not in human brain.RNA editing can also regulate the surface expression of some neurotransmitter receptors. One example is type A receptors of GABA (γ-aminobutyric acid), which is the major inhibitory neurotransmitter in the mammalian brain. Two recoding sites, GABRA3 (I342M) and GABRA3 (N327D), were observed in the pig brain Fig. . GABRA3 ies Fig. . Anothertes Fig. . Five reV1.1), which is encoded by the KCNA1 gene and is essential for neuronal excitability. The channel KV1.1 with I400V edited recovers faster from inactivation27. A higher (fourfold) editing level of KCNA1 (I400V) was observed in the entorhinal cortex of chronic epileptic rat than in wild type38. In the pig brain, editing level of KCNA1 (I400V) varied from 2% to 40% , which is encoded by the CACNA1D gene and essential for Ca2+ homeostasis. Two recoding sites, CACNA1D (I1660M) (4–31% edited) and CACNA1D (Y1663C) (2–11% edited), were seen in the pig brain  binding protein 7, a member of the soluble proteins that bind IGFs and modulate IGF binding to its receptors41. The IGF system is evolutionarily conserved and plays key roles in nervous system function. Elevated editing levels of K95R and R78G sites in IGFBP7 were observed in the neurons , ion channel-related genes (KCNMA1), ion transport-related genes (UNC80 and TMEM63B), and other genes , harbored conserved recoding sites solely. The genes with non-conserved recoding sites were mainly in categories related to the nucleus, extracellular exosome, and RNA binding  and IGFBP7 (K95R), were observed in the cerebral cortex, hippocampal formation, hypothalamus, cerebellum, and spinal cord  were taken into account. Differential gene expression analysis between pig and human was performed across six brain regions, including the cerebral cortex, amygdala, hippocampal formation, hypothalamus, cerebellum, and spinal cord. The pig-biased genes were defined as genes showing fourfold higher expression in ≥5 regions in pig than in human. No significant difference in recoding editing was observed between pig-biased and unbiased genes  were included in the study. Future studies with larger sample sizes will increase the statistical power of data analysis. Second, the human subjects aged 20–69 years were included in this study, while the pigs were about 1 year old. Life span varies among species. It is still challenging to determine whether the subjects from different species are at the same age/stage of development. RNA editing and gene expression are believed to be developmentally regulated in mammals44. When cross-species RNA editing or gene expression comparisons were performed, we did not consider potential data biases caused by differences in age, sex, or health status.There are some limitations in this study. First, only a limited number of pig samples  and GRIA4 (R/G), are not described in this database. This may be due to insufficient coverage and sequencing depth of the transcriptome in previous studies. Here we performed a genome-wide RNA-editing investigation across 30 subregions within the pig brain. The combination of RNA-seq and WGS enabled us to de novo identify RNA-editing sites accurately. Consistent with earlier observations in other mammals, A–I editing was the prevalent type of RNA editing in pig. Most of the edited sites identified in this study were previously unknown. Importantly, 1687 novel editing sites located in CDS and 11,281 sites in 3′-UTRs were identified. Besides GRIA2 (R764G) and GRIA4 (R765G), many previously undescribed but potentially important recoding events, such as GRIK1 (Q621R), KCNA1 (I400V), and CACNA1D (I1660M) were described in our PBRe Portal. The CDS-residing sites were small in number and the vast majority of editing sites was located in non-coding regions, such as intron and untranslated regions. We predicted that 29% of editing sites in 3′-UTRs potentially interrupt or create miRNA target sites. Indeed, this study largely expands the existing dataset of porcine brain RNA editing.Only 33,779 porcine brain A–I editing sites, which were revealed by RNA-seq data solely, are described in the PRESDB database17, B1 in mouse16, and PRE in pig , indicating that RNA editing is spatially regulated in the mammalian brain. Similar to observations in human, the overall editing levels of CDS-residing sites tended to be similar across pig brain regions, whereas editing levels of those sites in repetitive region were more likely to be different. This suggested that there were different factors contributing to RNA editing in CDS-residing and repetitive sites. The spatiotemporal expression of ADAR enzymes is a known trans-regulatory mechanism of A–I editing. We observed that the overall correlation between expression of ADAR1 and editing level of repetitive sites was higher than that between ADAR1 and coding sites in the pig brain, similar to earlier observations in human13. Unexpectedly, there was no high correlation between the expression of ADAR2 and editing level of coding sites in the pig brain, contrasting the result in human, which is drawn based on thousands of samples from whole body13. Considering the small sample sizes used in this study, the differences might be due to lack of enough statistical power. More samples are needed to better address this in future studies.Regional variations in RNA editing have been observed in the human13. In the mammalian brain, the highest density of neurons (>80%) is seen in the cerebellum47. The expression of ADAR1 and ADAR2 is considered to be higher in neurons than in other cell types29. The distinct editing signature in the cerebellum might be associated with the high proportion of neurons. In-depth investigations, such as more samples and single-cell RNA-seq, will be able to test this hypothesis.The expression of ADAR2 was remarkably higher in the pig cerebellum and the editing profile in cerebellum was distinct from those in other brain regions. The overall editing analysis revealed that repetitive sites in the cerebellum were edited more, in line with earlier observations in human14, there are 107,094 A–I editing sites (including 224 recoding sites) in mouse, which is much less than that in human or pig. Only a small number of conserved editing sites were found between human and mouse23, or between pig and human (this study). One similarity among human, mouse, and pig is the fact that many conserved recoding events occur in neuronal genes.ADAR enzymes are highly conserved in mammals. The number of known A–I editing sites varies among species. For example, according to REDIportal (V2.0)2+ permeability26. GRIA2 (R764G), GRIA3 (R775G), and GRIA4 (R765G) are involved in receptor desensitization26. GABRA3 (I342M) is involved in receptor trafficking26. This again indicated the biological importance of RNA editing in the central nervous system. On the other hand, more human-biased sites were observed in some regions, such as the cerebral cortex. An earlier study reported that conserved sites were often edited more in human than in mouse brain13. These observations are in line with the fact that the human brain has greater complexity than the brain of other mammals.Cross-species editing analysis of pig and human brain revealed that most of the conserved editing sites displayed an unbiased pattern. Of particular note, many physiologically important editing events were unbiasedly edited. For example, out of unbiased edited events found in the cerebral cortex, GRIA2 (Q607R) and GRIK2 (Q572R) are involved in regulating Ca48. Deficient RNA editing in the AMPA glutamate receptor gene GRIA2 has been found to be associated with the development of ALS in human7. To investigate the underlying mechanisms of human disease, mouse is one of the primary model organisms. For example, the functional importance of Q/R site in Gria2 has been demonstrated in the Adar2-null mouse9. However, anatomical differences between mouse and human brain are needed to be considered. For example, mouse lacks gyri and sulci in the cerebrum, unlike human or pig. In addition, many genes related to neurotransmission are subject to RNA editing. A higher overall correlation between pig and human brain for expression of genes related to neurotransmission was observed, as compared with that between mouse and human brain1. As stated previously, we failed to reveal high correlation between ADAR2 expression and editing of coding sites in the pig brain. However, most of conserved CDS-residing sites, including many physiologically important editing events, were unbiasedly edited between pig and human brain. Based on these observations, pig may be an alternative choice for studying dysregulated RNA editing associated with human neurological disorders.It is known that aberrant RNA editing caused by, e.g., altered ADAR activity is associated with many human diseases, such as cancers, metabolic diseases, autoimmune disorders, and neurological disordersTaken together, our study provides a valuable resource to understand the complexity of the mammalian brain, as well as broaden the application of pigs in biomedical research.1. Sample collection and handling of animals were carried out in accordance with national guidance for large experimental animals and under permission of the local ethical committee . Four Chinese Bama minipigs , were provided by the Pearl Lab Animal Sci & Tech Co., Ltd, where the animals were housed in a special pathogen-free stable facility under standard condition.The samples were collected from 30 subregions within pig brain 51. For comparisons between samples, the gene counts were normalized by normalization factor 52 using the normalize.deseq function in metaseqR package53.Clean RNA-seq reads were mapped to Sscrofa11 genome (ensembl release-92) with parameter “-q --no-mixed --no-discordant -p 3” by HISAT2 (version 2.1.0)To compare the results from REDItools and RES-Scanner, the same set of initially pre-aligned RNA and DNA BAM files were used as input for both tools. Only sites that met the criteria were used for comparisons: (I) homozygous for gDNA and with a coverage of at least ten DNA reads; (II) a coverage of at least ten RNA reads; and (III) at least three edited reads and editing level ≥ 5%.54. To retrieve these reads, the RNA reads discarded by initial alignment were transformed (As to Gs), re-aligned to transformed reference genome (As to Gs) by HISAT2, and then recovered as described in Porath et al.54. The recovered aligned RNA reads in BAM format were used for the subsequent RNA-editing analysis.Recent studies have reported that numerous RNA reads were missed by regular alignment due to hyper-editingde novo RNA-editing identification, was performed on each sample to obtain a list of editing sites by RES-Scanner15. Together with pre-aligned DNA-seq reads, the initially pre-aligned RNA reads and recovered pre-aligned RNA reads in BAM format of each sample were used as inputs for RES-Scanner. The potential PCR duplications of DNA or RNA reads were discarded using SAMtools (0.1.19)55. An average of 173 million uniquely mapped RNA reads were obtained for RNA-editing analysis of each sample. Strict criteria were used as follows: (I) a candidate site was required to be homozygous for gDNA and with a coverage of at least ten DNA reads; (II) any RNA reads with quality score < 30 for the site were discarded. The first and last six bases of each aligned RNA read were clipped. A candidate site should be supported by at least three edited RNA reads with editing level ≥ 5%; and (III) in order to avoid misalignment to paralogous regions, the uniquely mapped RNA reads were re-aligned to the genome by BLAT56. The sites with a proportion of qualifying reads to total BLAT-realigned reads < 50% were discarded. (IV) A candidate site was required to be supported by at least two samples. The default parameters of RES-Scanner were used if it is not mentioned specifically. The qualifying sites identified in each sample from different regions were combined to generate a comprehensive list of A–I editing sites in the first round.Two rounds of A–I RNA-editing identification were performed in this study. The first round, also called Due to the stringent criteria used in the first round, some true-positive editing sites were missed in some samples. For example, the site with low editing level (<5%) or low edited coverage (<3 edited reads supporting) were discarded. Hence, a second round of RNA-editing sites identification were performed to retrieve such missed editing sites. A more liberal criterion was used: each candidate site was required with editing level > 0 and ≥1 edited RNA read supporting editing. However, at least ten total RNA reads supporting edited and non-edited were required for each site, allowing us to quantify editing levels accurately. Finally, a total of 682,037 A–I RNA-editing sites were identified and an average of 179,927 A–I editing sites were obtained for each sample.The editing level of each site was calculated as the number of Gs divided by the total number of As + Gs. The As or Gs of different samples from the same subregion or region were combined to calculate the editing level of each site at the subregion or region level. The overall editing level of each sample was calculated as the number of Gs divided by the total number of As + Gs at all editing sites.S. scrofa) were downloaded from miRBase database (release 22)57. A seed (7 bp) was defined as positions 2–7 of a mature miRNA. To investigate the effect of RNA editing on miRNA targeting, we compared the flanking 13 bp-long sequence of the editing site (6 bp each sides) and the seed of known miRNA. A site is regarded as a candidate binding site if any 7 bp sequence can be completely complementary to a known miRNA seed, as described in Peng et al.58.A total of 457 known mature miRNA (https://presdb.deepomics.org)19.RNA-editing sites of previous study in pig were downloaded from PRESDB database  as compared to any other region. Region-enhanced sites were defined as those with an editing level at least 20% higher in a particular region as compared to the average level in all regions.29, were obtained from http://oldhamlab.ctec.ucsf.edu/. The significance of differences between expression scores of ADAR1 or ADAR2 in neurons and non-neuronal cells was assessed by Wilcoxon’s test.The mRNA expression scores of neurons, microglia, astrocytes, and oligodendrocytes from 20 human brain regions, which are based on co-expression analysisn = 3) and neurons (n = 9) were downloaded from Gene Expression Omnibus using accession number GSE14649440. The SRA data format was converted to fastq by fastq-dump in SraToolkit (2.8.2). Then paired-end RNA-seq reads were mapped to Sscrofa11 genome (ensembl release-92) with parameter “-q --no-mixed --no-discordant -p 3” by HISAT2 (2.1.0)50. The potential PCR duplications of RNA reads were discarded using SAMtools (0.1.19)55.The RNA-seq data (including Sequence Read Archive (SRA) data and Transcript per Million (TPM) data) of fibroblasts 59. To compare editing sites between pig and human, the conserved sites were obtained from the pig and human genome. The coordinates of human editing sites were converted to coordinates on the pig reference genome using the liftOver tool and the “hg19ToSusScr11.over.chain.gz” file from UCSC (http://genome.ucsc.edu). The total number of RNA reads supporting edited and non-edited status in a given conserved editing site were combined from samples corresponding to the same brain region. The differential editing sites at the same brain region between the two species were identified using Fisher’s exact test, with difference of editing level ≥20% and P-value < 0.01.A–I RNA-editing sites in a human brain atlas, including 332 samples, were downloaded from REDIportal . Differential expression analysis between species was performed by DESeq252. Only the pig-to-human one-to-one orthologous genes  were included for analysis. Species-specific expressed genes were defined as genes with fold change ≥ 4 and false discovery rate <0.01.The RNA-seq read counts of 122 samples (Supplementary Data prcomp function in R language.The editing sites with at least ten RNA reads coverage in all subregions within the pig brain were used for PCA analysis. PCA was performed using the 60. GO terms were defined as significant with Fisher’s exact test P-value < 0.05.We performed GO enrichment analysis using DAVID online ResourcesFisher’s exact test was used to assess the significance of GO enrichment or differential editing analysis. Wilcoxon’s test was used to assess the significance of median differences between two groups. The editing sites identified in samples from the same subregion/region were pooled for further investigations.Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Supplementary FilesSupplementary DataReporting Summary


RNA localization is involved in multiple biological processes. Recent advances in subcellular fractionation-based sequencing approaches uncovered localization pattern on a global scale. Most of existing methods adopt relative localization ratios , however, absolute ratios may yield different results on the preference to different cellular compartment. Experimentally, adding external Spike-in RNAs to different fractionation can be used to obtain absolute ratios. In addition, a spike-in independent computational approach based on multiple linear regression model can also be used. However, currently, no custom tool is available. To solve this problem, we developed a method called subcellular fraction abundance estimator to correctly estimate relative RNA abundances of different subcellular fractionations. The ratios estimated by our method were consistent with existing reports. By applying the estimated ratios for different fractions, we explored the RNA localization pattern in cell lines and also predicted RBP motifs that were associated with different localization patterns. In addition, we showed that different isoforms of same genes could exhibit distinct localization patterns. To conclude, we believed our tool will facilitate future subcellular fractionation-related sequencing study to explore the function of RNA localization in various biological problems. RNA localization plays important functions in diverse biological processes in the cells. Proper subcellular distribution of RNAs not only maintains the correct organization of cell structure but also is crucial to control multiple biological processes . Cis-elements encoded on RNAs are recognized by RBP and guide the RNA to the proper cellular localization of RNAs . There are 11 cell lines with Cytosol, Nucleus and whole cell (WC) fractions PolyA RNA-seq, 7 cell lines with RNA-seq from three corresponding non-PolyA fractions *TPMnuc. The cost function we used to minimize the differences isThe inputs of subcellular fraction abundance estimator (SFAE) method were the filtered normalized expression vectors: genes whose TPM level in WC did not fall within the range between nuclear and cytosol TPM levels were removed. To estimate the single parameter CR, we aimed to identify CR value that minimized the difference between the predicted WC TPM vector with observed TPM vector, where TPMWe’ve tested in multiple datasets and such cost function would normally yield a bell shape in the prediction error measurement . When de2. HeLa cells were treated with 5 µg/ml actinomycin D  to inhibit transcription , 1% streptomycin/penicillin and 0.1% gentamycin(Life Technologies). Cell were incubated at 37°C and 5% COFor separation of nuclear and cytoplasmic fractions, the extraction kit  was used and followed the same procedures as previous study  across multiple cell lines for each gene in three genesets from ensemble reference database . Then we used FIMO to scan each gene’s cDNA sequence for occurrences of the annotated motifs from CISBP-RNA and Ray Cyto/NumberNuc) more than 0 in both MIN6 cells and liver cells . Similarly, nuclear genes (Nuc-genes) are genes whose log2 (NumberCyto/NumberNuc) less than 0 in both MIN6 and liver cells.For genesets of mouse, we directly used the genesets (Cyto-gene and Nuc-gene) defined in their study  in two isoforms were remained.iθ (ranges from 0 to 1) to represent the ratios of total RNA abundances among individual cellular subfractions. When cells are fractioned into nuclear and cytosolic portions, we used cytoθ to represent the CR. For each fraction as well as total cell extract, we have a vector of transcripts per million (TPM) values  to represent the expression vectors. We estimated optimal Cytoθ by minimizing the sum of log ratios of the observed and predicted expression matrix . Such strategies can be also generalized to cases with more than two subcellular fractionations of cells, such as data from CeFra-Seq with four fractions.We used We applied this method to quantify the relative cytosolic and nuclear portion for polyadenylated and nonpolyadenylated RNA in the ENCODE datasets . CRs of vs cytosol-enriched genes (283) upon perturbation  in cytosolic RNA ratios upon HNRNPK knockdown in two cell lines . The decurbation .Unfortunately, such dataset is quite limited. Since we did not have a positive-control treatment that is known to alter the cytosolic RNA ratios, we resorted to ActD treatment, which inhibits transcription. Originally, we suspected that such cytosolic ratios (CRs) might increase as nuclear transcription was shut down. Both ActD inhibition  and RNA Therefore, both examples above serve as a proof-of-principle that global cytosol ratios can be altered; further studies are needed to characterize how common this global CR is regulated under other conditions and biological processes.vs nucleus. There were three major gene clusters based on K-means clustering algorithm, and the cluster in the bottom showed clear preference for nuclear localization pattern. As expected, nonprotein coding genes, such as lncRNA and antisense RNAs, were significantly enriched  in this nuclear localized cluster . Interestingly, compared with other two groups, genes in nucleus group only have a much smaller number of enriched RBP motifs  for all genes in nuclear and cytosol subcellular fractions. Indeed, such ratios in bivalent genes were significantly higher compared with other two types for nuclear RNAs in nonpolyadenylated RNAs . Cyto/Nuc Ratios in polyadenylated RNAs predicted by our method showed clear imbalance between cytosolic and nuclear RNAs: there are consistently more RNAs resided in the cytoplasm compared with nuclear for all cell lines and conditions . In addiWe also showed that the localization pattern for each gene transcript could be dynamically regulated. For the genes exhibited large variations, we identified alternative isoforms that may exhibit distinct localization patterns. Therefore, by altering the relative abundance of different isoforms, the localization pattern can be switched. This is consistent with previous report that during neuronal differentiation, isoform switching occurs to regulate mRNA localization . In addicis-elements sequences on lncRNA, lead to opposite localization patterns between human and mouse embryonic stem cells  and significance of such regulated RNA localization in multiple physiological processes and disease conditions.https://ngdc.cncb.ac.cn/gsa.The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive , China NSFAE method is available as a standalone Python pipeline (github.com/bioliyezhang/SFAE).G3 online.jkab371_Supplementary_FiguresClick here for additional data file.jkab371_Supplementary_TableS1Click here for additional data file.jkab371_Supplementary_TableS2Click here for additional data file.jkab371_Supplementary_TableS3Click here for additional data file.jkab371_Supplementary_TableS4Click here for additional data file.jkab371_Supplementary_TableS5Click here for additional data file.


Lateral lymph node (LLN) metastasis is one of the leading causes of local recurrence in patients with lower rectal cancer. Unfortunately, no diagnostic biomarkers are currently available that can predict LLN metastasis preoperatively. Accordingly, we investigated the relationship between the middle rectal artery (MRA) identified by contrast-enhanced magnetic resonance imaging (ceMRI) and LLN metastases.Data from 102 patients with lower rectal cancer who underwent surgery, and were evaluated by preoperative ceMRI, between 2008 and 2016 were reviewed retrospectively. Two expert radiologists evaluated the MRA findings. The diagnostic performance of MRA for LLN metastasis was evaluated by a multivariate analysis with conventional clinicopathological factors.P = 0.045) and clinical LLN status (P = 0.001) were independent predictive factors for LLN metastasis. Furthermore, the sensitivity and negative predictive value of MRA for LLN metastasis were 95% and 97.1%, respectively.The MRA was detected in 67 patients (65.7%), including 32 (31.4%) with bilateral MRA and 35 (34.3%) with unilateral MRA. The tumor size, presence of the MRA, and clinical LLN status were significantly correlated with LLN metastasis. A multivariate analysis demonstrated that the presence of MRA (We successfully demonstrated that MRAs could be clearly detected by ceMRI, and the presence of MRA robustly predicted LLN metastasis in patients with lower rectal cancer, highlighting its clinical significance in the selection of more appropriate treatment strategies.Trial registration number: retrospectively registered 2126Trial registration date of registration: August 23, 2019 Local recurrence of rectal cancer remains an important clinical problem associated with poor survival, severe morbidity, and low likelihood if salvage. Lateral lymph node (LLN) metastasis, which occurs in approximately 15% of patients, is a leading cause of local recurrence in patients with lower rectal cancer –8. Both However, the pre-treatment diagnosis of the presence of LLN metastasis remains clinically challenging. Currently, the identification of enlarged LLNs evaluated on computed tomography (CT) and/or magnetic resonance imaging (MRI) is reportedly a risk factor for LLN metastasis –16; howeLLN metastasis has been considered associated with lateral lymphatic drainage along the middle rectal artery (MRA) , 18. TraRecently, advances in diagnostic imaging have enabled the visualization of the detailed vascular anatomy. Bilhim indicated that the prevalence and anatomical findings of the MRA could be detected by CT angiography/digital subtraction angiography for male patients with lower urinary tract symptoms . HoweverIn the present exploratory study, for the first time, we investigated the frequency of the MRA detected by contrast-enhanced MRI (ceMRI) and successfully confirmed the utility of MRI-detectable MRAs for the identification of LLN metastasis, thus highlighting the clinical significance of this biomarker for the management of patients with lower rectal cancer.n = 50), insufficient contrast imaging conditions (n = 8), and poor images caused by body movements (n = 4). Ultimately, 102 patients were included in this study  guidelines . The preThis study was approved by the ethics committee of the Nara Medical University (No. 2126). All patients gave their informed consent for the use of their anonymized data via an opt-out method. Patients’ consent to participate was obtained through an opt-out method.We have routinely used a 1.5-T MRI scanner  or 3-T MRI scanner  for the diagnosis of the depth of the primary rectal tumor and LLN swelling. Patients underwent MRI examinations in the supine position without preparation. T1-weighted gradient-echo sequencing was performed for enhanced MRI. For contrast-enhanced MRI, Gd-DTPA, Gd-DOTA, or Gd-BT-DO3A (0.1 mmol/kg) was infused into the vein. The slice thickness of all sequences was 1 mm and with a mean total time of 30 min.Kiyomatsu et al. reviewed and classified MRAs into the following three branches: (i) antero-lateral (AL) type, which branches from the prostatic artery, inferior vesical artery, or uterine artery; (ii) lateral (L) type, which runs into the mesorectum via the lateral ligament; and (iii) postero-lateral (PL) type, which runs into the rectosacral fascia consisted of posterior rectal wall Fig.  27]. In. In27]. In this study, we defined as cases as having a positive clinical LLN status when the LLN short axis exceeded 7 mm on preoperative pelvic MRI, with reference to previously reports , 15. LLNP<0.05. Multivariate logistic regression analysis was employed to evaluate clinicopathological variables and the presence of MRA that were significant on a univariate analysis for the detection of LLN metastasis.Statistical analyses were performed using the SPSS software program, version 25.0 . Categorical variables were expressed as numbers and percentages. In order to evaluate the relationship between the presence of the MRA and LLN metastasis, the sensitivity, specificity, positive predictive value, and negative predictive value were calculated. Fisher’s exact test and the chi-squared test were used as appropriate to analyze the significantly different clinical factors between LLN metastasis-positive and metastasis-negative patients. Statistical analyses of all variables were considered significant at Patients’ clinicopathological characteristics are shown in Table Using the ceMRI examinations, MRAs were detected in 67 (65.7%) patients, and the numbers of AL/L/PL MRAs on the right side were 28 (27.5%)/35 (34.3%)/22 (21.6%), while that on the left side were 16 (15.7%)/25 (24.5%)/18 (17.6%), respectively. There were 32 (31.4%) patients with bilateral MRAs and 35 (34.3%) with unilateral MRAs. Thirty-five (34.3%) patients had no detectable MRAs . Importantly, regardless of the tumor side in rectum, LLN metastasis frequently developed on the same side with MRAs . On the other hand, there were few instances of LLN metastasis on the without side .The incidence of LLN metastasis in the patients with MRA was higher than in those without MRA (P=0.045) and clinical lateral lymph node status (P=0.001) emerged as significantly independent predictive factors for LLN metastasis.The correlation between the clinical factors and LLN metastasis is shown in Table In this study, the diagnostic performance of the presence of MRAs detected by ceMRI is an important predictive factor for LLN metastasis. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the presence of MRAs detected by MRI for LLN metastasis were 95% (19/20), 41.5% (34/82), 61.8% (63/102), 28.4% (19/67), and 97.1% (34/35), respectively (Table In order to advance cancer treatment into a new era of precision medicine, the development of individualized treatment strategies for cancer patients is essential. In this exploratory study, we investigated for the first time the presence of MRAs using ceMRI and identified a significant correlation between MRAs and LLN metastasis. MRAs have been investigated anatomically using cadavers in several previous studies; however, clinical and radiological studies have not been conducted. The cadaveric studies mainly discussed the frequency, origin, and/or trajectory of MRAs. These anatomical studies have shown a wide range of detection rates because of variation in the definition of MRA. Kiyomatsu et al. reviewed the frequency and trajectory of MRA in detail and described three types of MRAs . We therFor the preoperative evaluation of lower rectal cancer, high-resolution T2-weighted turbo spin-echo images are generally used for assessing the depth of tumor invasion, circumferential resection margin, and lymph node metastasis , 29. In In the present study, the prevalence of LLN metastasis was 19.6%, which is consistent with previous reports , 32, 33.The gender, tumor location, proximal lymph node metastasis, tumor depth of invasion, lymphatic or venous invasion, histological tumor differentiation, and tumor size have been reported as risk factors for LLN metastasis in previous reports , 3. BaseSeveral limitations associated with the present study warrant mention. First, this study was a single-center, retrospective study. Second, we analyzed MRAs in a moderately sized clinical cohort. Third, no all MRAs are always going to be detected by ceMRI, although most of them may be visualized on 1-mm slice pelvic images. Finally, we need to prove the reproducibility of MRAs, since this is the first study. Further prospective studies are therefore needed in order to verify the detectability of MRAs by MRI via an anatomical approach and its validity as a significant predictor of LLN metastasis.In conclusion, we successfully showed that MRAs could be clearly detected by ceMRI, and MRAs robustly predict LLN metastasis in patients with lower rectal cancer, highlighting their clinical significance in the selection of more appropriate treatment strategies. Furthermore, our study provides a framework for developing individualized treatment strategies for LLN and designing future clinical trials for patients with lower rectal cancer.