However, architecture dependency has not been sufficiently investigated, especially for pseudoknotted secondary structures. In this study, we propose a novel algorithm for directly inferring base-pairing probabilities with neural networks that do not depend on the architecture of RNA secondary structures, and then implement this approach using two maximum expected accuracy (MEA)-based decoding algorithms: Nussinov-style decoding for pseudoknot-free structures and IPknot-style decoding for pseudoknotted structures. To train the neural networks connected to each base pair, we adopt a max-margin framework, called structured support vector machines (SSVM), as the output layer. Our benchmarks for predicting RNA secondary structures with and without pseudoknots show that our algorithm outperforms existing methods in prediction accuracy.Existing approaches to predicting RNA secondary structures depend on how the secondary structure is decomposed into substructures, that is, the  The roles of functional non-coding RNAs (ncRNAs) in regulating transcription and guiding post-transcriptional modification have been recently shown to be critical in various biological processes, ranging from development and cell differentiation in healthy individuals to disease pathogenesis . The welYet, methods for experimentally determining RNA tertiary structures utilizing X-ray crystal structure analysis and nuclear magnetic resonance (NMR) are costly and labor-intensive, thus restricting their application. Accordingly, researchers often carry out computational prediction of RNA secondary structures based on the analysis of base pairs comprising nucleotides joined by hydrogen bonds.Computational approaches to RNA secondary structure prediction often utilize thermodynamic models  that As an alternative to experimental approaches, machine learning can be utilized to train scoring parameters based on the substructures constituting reference structures. This type of approach, as implemented in CONTRAfold ,8, SimfoAnother important aspect of RNA secondary structure prediction is the choice of the decoding algorithm used to find the optimal secondary structure from among all possible secondary structures. Two classic decoding algorithms are the minimum free energy (MFE) algorithm, which is used in thermodynamic approaches, and the maximum likelihood estimation (MLE) algorithm, which is used in machine-learning-based approaches. These algorithms find a secondary structure that minimizes the free energy and maximizes the probability or scoring function, respectively. Another option is a posterior decoding algorithm based on the maximum expected accuracy (MEA) principle, which is known to be an effective approach for many high-dimensional combinatorial optimization problems . As resePseudoknots, an important structural element in RNA secondary structures, occur when at least two hydrogen bonds cross each other, and are typically drawn as two crossing arcs above a primary sequence .Many RNAs, including rRNAs, tmRNAs and viral RNAs, form pseudoknotted secondary structures . PseudokHowever, all of the aforementioned algorithms cannot consider pseudoknotted secondary structures owing to computational complexity. It has been proven that the problem of finding MFE structures including arbitrary pseudoknots is NP-hard ,25. TherWe previously developed IPknot, which enables fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming ,37. IPknarchitecture , to defed to in ,28 and ted to in  for limik-mer contexts around both bases in a pair. Our benchmarks for predicting RNA secondary structures with and without pseudoknots show that the prediction accuracy of our algorithm is superior to that of existing methods.Here, we propose a novel algorithm to directly infer base-pairing probabilities with neural networks instead of the McCaskill algorithm or the inside–outside algorithm, which both depend on the architecture of RNA secondary structures. Then, we employ the inferred base-pairing probabilities as part of a MEA-based scoring function for the two decoding algorithms: Nussinov-style decoding for pseudoknot-free structures, and IPknot-style decoding for pseudoknotted structures. To train the neural networks connected to each base pair, we adopt a max-margin framework, called structured support vector machines (SSVMs), as the output layer. We implement two types of neural networks connected to each base pair: bidirectional recursive neural networks (BiRNN) over tree structures and multilayer feedforward neural networks (FNN) with The major advantages of our work are summarized as follows: (i) our algorithm enables us to accurately predict RNA secondary structures with and without pseudoknots; (ii) our algorithm assumes no prior knowledge of the architecture that defines the decomposition of RNA secondary structures and thus the corresponding parameter space.x, referred to as the length of x. Let x. A secondary structure The RNA sequence structure is modeled following the setup used by Akiyama et al. . First, We employ the maximum expected accuracy (MEA)-based scoring function originally used for IPknot ,37.level p. For each RNA secondary structure m such that y is decomposable into m substructures without one or more pseudoknots . ThrougFirst, to construct an MEA-based scoring function, we define a gain function of Here, The objective is to identify a secondary structure function  under a Here, stimator  has beenAccordingly, the expected gain function  can be ap base pairs and C is a constant that is independent of Here, erial of ). The baIt should be noted that IPknot can be considered an extension of CentroidFold . For thefunction  of IPknoFor the prediction of pseudoknot-free secondary structures, we find ted gain  with m=1The constraint defined by Equation  means thEquation  disallowThis integer programming (IP) problem can be solved by dynamic programming as follows, similar to the Nussinov algorithm ,(7)Mi,jMaximization of the approximate expected gain  can be sp. The constraint defined by Equation  over tree structures as shown in k-mers around the i-th and j-th bases as shown in The second architecture employs a simple multilayer feedforward neural network (FNN). To calculate the base-pairing probability k-mer around k-mer around k-mer context length default  . For a tEquation  for NussEquation  for IPknfunctionL(λ)=∑; 1:initialize 2:repeat3:for alldo  4:    5:for all do    6:      7:end for    8:end for  9:until all the parameters convergeTo minimize the objective function , stochasfunction  using thhttps://github.com/keio-bioinformatics/neuralfold/, (accessed on 27 September 2022).Our algorithm is implemented as the program Neuralfold, which is short for the neural network-based RNA folding algorithm. We employ Chainer  for the We evaluated our algorithm with the Nussinov-style decoding algorithm for predicting pseudoknot-free RNA secondary structures using four datasets, TrainSetA, TestSetA, TrainSetB and TestSetB, which were established by .TrainSetA and TestSetA are literature-based datasets ,10,41,46TrainSetB and TestSetB, which contain 22 families with 3D structures , were asWe also evaluated our algorithm with the IPknot-style decoding algorithm for predicting pseudoknotted RNA secondary structures on two datasets. The first dataset is called the pk168 dataset , which wThe second dataset is called RS-pk388, originally established by . This daWe evaluated the accuracy of RNA secondary structure predictions based on sensitivity , which can be regarded as base-pairing probability matrices, they do not directly address the constraints that the RNA secondary structure must satisfy  for forN×N mquations –(11) thaIt has been pointed out that RNA secondary structure prediction based on machine learning and deep learning is prone to overfitting due to bias in the training data ,45. Sevek-mers around each base pair as input to infer its base-pairing probability, where k is the context length to model the length of loops and the contexts around the openings and closings of helices. As can be seen in k-mer context lengths affect the prediction of pseudoknotted secondary structures. For example, consider the input bases when calculating the base-pairing probability of the blue-highlighted base pair (AU) using the FNN model. The FNN model with the context length k = 11 takes as input five bases in both the upstream and downstream directions from bases i and j. As seen in not completely located within the context length k = 11 around the base pair AU. On the other hand, for the FNN model with context length k = 41, all the bases of Stem 2 are completely located within the context around the base pair AU. This leads the FNN model to correctly predict the base pair AU, suggesting that a longer context length enables consideration of the dependency between stems in pseudoknotted substructures.The FNN model takes two We propose a novel algorithm for directly inferring base-pairing probabilities with neural networks that enables us to accurately predict RNA secondary structures with pseudoknots. By combining IPknot-style decoding with the max-margin framework, our algorithm trains the model in the end-to-end manner to compute base-pairing probabilities under the constraints that RNA secondary structures, including pseudoknots, must satisfy. HotKnots 2.0 , on the


The combination of molecular tools, standard surveying techniques, and long-term monitoring programs are relevant to understanding environmental and ecological changes in coral reef communities. Here we studied temporal variability in cryptobenthic coral reef communities across the continental shelf in the central Red Sea spanning 6 years (three sampling periods: 2013–2019) and including the 2015 mass bleaching event. We used a combination of molecular tools (barcoding and metabarcoding) to assess communities on Autonomous Reef Monitoring Structures (ARMS) as a standardized sampling approach. Community composition associated with ARMS for both methodologies (barcoding and metabarcoding) was statistically different across reefs (shelf position) and time periods. The partition of beta diversity showed a higher turnover and lower nestedness between pre-bleaching and post-bleaching samples than between the two post-bleaching periods, revealing a community shift from the bleaching event. However, a slight return to the pre-bleaching community composition was observed in 2019 suggesting a recovery trajectory. Given the predictions of decreasing time between bleaching events, it is concerning that cryptobenthic communities may not fully recover and communities with new characteristics will emerge. We observed a high turnover among reefs for all time periods, implying a homogenization of the cryptobiome did not occur across the cross shelf following the 2015 bleaching event. It is possible that dispersal limitations and the distinct environmental and benthic structures present across the shelf maintained the heterogeneity in communities among reefs. This study has to the best of our knowledge presented for the first time a temporal aspect into the analysis of ARMS cryptobenthic coral reef communities and encompasses a bleaching event. We show that these structures can detect cryptic changes associated with reef degradation and provides support for these being used as long-term monitoring tools. However, it is estimated that 85–99% of coral reef species are still to be described; a large proportion is expected to be discovered in the small inconspicuous organisms such as cryptobenthic fishes3 and invertebrates4, which inhabit cracks and crevices provided by the reef framework5. These organisms constitute the reef cryptobiome5 and due to their characteristics  have regularly been overlooked in reef surveys. However, the highly specious cryptobiome is crucial for coral reef dynamics. They are an essential food source for predators7 and play an important role cycling nutrients in the reef food web8.Coral reefs harbor approximate 35% of the total marine biodiversity9. This is particularly relevant in areas where comparatively limited research has been conducted, such as the Red Sea10. In addition, a shortage of taxonomists is creating a bottleneck in the description of new species11. Recent advances in molecular techniques have improved our ability to separate organisms’ identity based on regions in their DNA code into “operational taxonomic units”, fostering more comprehensive assessments over space and time in species-rich areas12. DNA-based techniques have an additional advantage in extracting the identifications of larvae or immature organisms, an even greater challenge for morphological-based identifications13. Therefore, studies using DNA-based techniques are becoming increasingly used in coral reef ecosystems19. Recent studies using eDNA in the Red Sea and elsewhere have detect conspicuous and cryptobenthic reef species21, proved suitable for monitoring multitrophic community variations22 and estimate levels of anthropogenic pressure23.To establish strategies to halt or slow down the current trend in species loss, first we need to catalog biodiversity through collections and descriptions of these organisms24, negatively affecting human well-being. Coral reefs, in particular, are known to support the livelihoods of more than 500 million people in the world and generating US$35.8 billion dollars annually25. A sound understanding of how species change in space and time, and the underlying processes driving those changes is critical for elucidating the relationship between habitat degradation and biodiversity26. Global pressures, such as increasing sea water temperature as a result of elevated CO2 levels, have been linked to a higher intensity and frequency of bleaching events worldwide27, resulting in dramatic coral loss, and potentially a relative dominance of rubble28. Scleractinian corals along with crustose coralline algae contribute to reef building, with a critical role in maintaining reef structure and consequently reef biodiversity30. The shift to reefs dominated by non-calcifying organisms and an increase in non-framework building corals34 conveys an immediate decline in species with obligate relationships with corals, followed by an alteration in the community of fishes and invertebrates36. Maintaining biodiversity is crucial for securing the stability of ecosystems’ functioning38 and, consequently, the conservation of coral reefs and their associated biodiversity, which is a fundamental step to ensure the delivery of critical goods and services39.Recent reports have linked biodiversity loss with impairment of the ocean’s ability to deliver goods  and services 41. Time series provide fundamental information about natural variability and are key to set critical thresholds above which significant and undesirable changes may be detected. Ultimately, these thresholds will be fundamental in the early detection of ecosystem degradation. Regrettably, funding for sustaining long-term monitoring projects is decreasing over recent years41. Marine ecosystems are subject to natural variability in physico-chemical  and biological forcing factors , driving the spatio-temporal dynamics of biological communities44. It is important to understand the natural temporal variances in biological communities and their driving factors, so that the effects of human induced disturbances can be detected, assessed, and modeling of future trajectories predicted46. To the best of our knowledge, efforts in documenting biodiversity patterns of the cryptobiome and its ecological drivers following standardized approaches across a temporal time scale are lacking in the Red Sea and are very limited worldwide.Long-term studies provide unique information regarding environmental and ecological changes over time14. One example is the Autonomous Reef Monitoring Structures (ARMS) to investigate changes in the cryptobiome. In response to the need of finding standardized tools to investigate patterns in space and time of this overlooked biological reef component, during the Census of Marine Life (CoML), a team of scientists developed the Autonomous Reef Monitoring Structures. Each ARMS unit consists of nine layers of square PVC plates (22.5 cm by 22.5 cm), mimicking the structural complexity of the reef environment with multiple ecological niches represented through different levels of exposure to light and water flow. They are usually deployed in a coral reef between one and three years to allow a mature community of the cryptobiome to settle47. The removal and re-deployment allows for sustained long-term observations of biodiversity to be achieved. ARMS have been attracting the attention of the scientific community, with hundreds of deployments across the Indo-Pacific, Red Sea, Atlantic, Mediterranean, Black and Baltic Seas in different benthic habitats54. The relevance and potential of this tool to scaling up spatial and temporal trends has even resulted in the establishment of a molecular research network in Europe55.The application of standardize methods to address a specific question is paramount to be able to scale up findings from local to global scales16 and these patterns will be maintained through time. Considering that the effects of bleaching tend to attenuate with distance from shore28, we also hypothesize that temporal turnover will be higher in nearshore than offshore reefs in response to a bleaching event, as more changes are expected in the nearshore benthic coral reef structure.In this study, we investigate patterns of cryptobenthic coral reef communities at three time periods  across a shelf gradient in the central Saudi Arabian Red Sea. During this 6-year period (2013: first deployment of ARMS), reefs in this region experienced the global mass bleaching event in 2015/2016, giving us the opportunity to investigate not only the spatio-temporal changes in cryptobenthic biodiversity across a shelf gradient but also to analyze the potential response of the cryptobiome to this bleaching event. We hypothesize that shelf position will be a driver of biodiversity patternsp = 0.04) and the overall abundance  was significantly different between sampling times  and 1,723 specimens were identified from the > 2000 µm size range of the 33 ARMS units. Most OTUs were present at a single time or reef. However, we were unable to retrieve ARMS from Abu Shootaf in 2019. Over the three sampling periods only 38 OTUs (14%) were identified in all three periods. The highest number of OTUs  were shared between 2017 and 2019. In contrast, 2015 and 2019 shared the fewest number of OTUs . This pattern between sampling times was observed for most reefs, except Al Fahal Fig. . Only 34es Table . Averagees Table . The Shaes Table .Figure 1In the 106–2000 µm metabarcoding fraction 33,832 amplicon sequence variants (ASV) were obtained from the 33 ARMS units. Most of the ASVs were present in a single sampling period  or reef  Fig. . Only 5%p = 0.02) but not between reefs . Unlike the observed for the barcoded fraction, the average number of ASVs per ARMS was lowest in 2017 (1618) and highest in 2019 (2139), close to the number of ASVs observed in 2015 (2072).The number of ASVs in the metabarcoding fraction (106–2000 µm) was significantly different between sampling times  p < 0.01; Fig. p = 0.02; Fig. p < 0.01; nestedness p = 0.03; Fig. p < 0.01; Fig. For both the barcoding and metabarcoding datasets, turnover was a higher component of beta diversity than nestedness. For the barcoding dataset, different patterns were observed for turnover and nestedness temporally at the reef level. At the offshore reef Abu Madafi, turnover  and Al Fahal (p < 0.01), this pattern was not observed in Abu Madafi where the 2015–2017 comparison was significantly lower (p < 0.01). Comparisons amongst reefs showed that in 2017 and 2019 all reefs had a significantly different turnover . In terms of nestedness the only significant difference was observed in 2019 between Abu Madafi and Abu Shoosha (p = 0.01).In the metabarcoding dataset the only significant temporal differences in turnover—observed in all reefs occurred between 2017 and 2019 , two squat lobsters , two decapods (Decapoda OTU_53 and OTU_102), one gastropod (Duprella cornus OTU_74), one blenny (Cirripectes stigmaticus OTU_118), and one sea urchin (Eucidaris metularia OTU_11) were indicator taxa for the reef factor. Seven indicator OTUs  had an inshore to offshore gradient in abundance with higher abundance in the inshore reef Abu Shoosha , one brittle star (Ophiocoma OTU_35), two gastropods (Fissurellidae OTU_37 and Gastropoda OTU_78), one decapod (Decapoda OTU_53), one blenny (Cirripectes stigmaticus OTU_118), and one hermit crab  were indicator taxa for the factor time. Seven indicator OTUs  gained abundance in 2017, and, OTU_78 in 2019. OTU_68 showed the opposite pattern with highest abundance in 2015. When looking at the abundance of the barcoding fraction, the factor reef and time had a significant association with the abundance of the indicator taxa for the reef and time factor , three arthropods , two chordates (Chordata ASV_12 and Amblyglyphidodon ASV_11), one cnidarian (Leptothecata ASV_47), one echinoderm (Echinodermata ASV_105), one sponge , and seven identified only at kingdom level  were indicator taxa for the reef factor. Nine indicator ASVs  presented an inshore to offshore gradient with higher abundance in the inshore reef Abu Shoosha and eleven indicator ASVs  showed the opposite pattern with higher abundance in the offshore reef Abu Madafi. One annelid (Polychaeta ASV_37), one Arthropoda (Arthropoda ASV_111), one cnidarian (Sarcophyton ASV_9), one echinoderm (Echinodermata ASV_105), one mollusk (Gastropoda ASV_86), and five ASVs identified to kingdom level  indicator taxa for the time factor. Eight indicator ASVs increased in abundance in 2017  in 2017. And two (ASV_9 and ASV_11) in 2019.We identified 21 ASVs with a significant contribution to the differences in spatial patterns and 10 ASVs in spatial patterns Fig. . Five ana concentrations (Chla) was associated with the nearshore reef Abu Shoosha in 2015 and 2019.For the > 2000 µm fraction the first axis of the distance-based redundancy analysis (dbRDA) explained 11.8% of the constrained variation with the cross-shelf gradient being evident along this axis especially for the 2017 and 2019 samples  fraction in the dbRDA is observable in the first axis of the dbRDA that explains 8.5% of the constrained variation Fig. B. All th57 and in the Red Sea58. Only a few studies focused on the largest fraction of the reef biodiversity (the cryptobenthos)60. While ARMS have previously been used to investigate differences in the cryptobiome across spatial gradients  may bias the reaction time of reef-associated communities. Short lived species like those targeted by ARMS can, therefore, provide faster responses to disturbance events and be more informative in environmental impact assessment studies7. Despite signs of recovery from the effects of the 2015 bleaching event, full recovery may not happen quickly enough, if ever occur, if bleaching events become more frequent as expected27.Our data covers the period immediately before the global mass bleaching event of 2015/2016 (June 2015) and four years after allowing the investigation of the effects of the bleaching event on the cryptobiome. While the central region of the Red Sea was not affected as strongly by the 2015/2016 bleaching event as it was by previous bleaching events66, however, in coral reefs most of species prefer areas not covered by living corals67. Regarding cryptobenthic fish, Froehlich, et al.59 observed a clear decline in all Gobiodon goby species in response to changes in their Acropora coral hosts after a sequence of cyclones followed by a bleaching event, with 78% of colonies uninhabited. Moreover, Bellwood et al.60 also reported that cryptobenthic fish communities may not recover to pre-bleaching characteristics even in cases of coral cover recovery, as shifts in coral communities may also persists beyond its cover. The close associations between cryptobenthic organisms and their coral hosts may magnify the shifts in community structure even if biodiversity increases68. Indeed, increase in biodiversity and density may not indicate better reef health as previous studies have found that number of species and abundance of cryptobenthic species tend to increase in coral rubble compared to live coral67. Also, regarding ocean warming, these small-sized organisms may have an advantage as increasing temperature may result in increasing growth rates, fostering successful settling and future survival68. In the smaller size fraction, there was a clear reduction in richness, measured as the number of ASVs, from 2015 to 2017 followed by an increase to levels similar to the pre-bleaching time. For the larger fraction we observed a steady increase in richness, measured as number of OTUs. Indeed, the shorter life span and higher sampled area to body size ratio of the smaller organisms allows us higher sensitivity to detect changes in reef communities.Annelida increased in relative abundance and percentage of number of reads after 2015 in the barcoding and metabarcoding fractions respectively, while Chordata decreased from 2015 to 2019 when assessed by metabarcoding. Annelids inhabit corals and non-living substrates16. Pearman et al.16 described the offshore reefs in the Red Sea to account for the most unique OTUs, and observed distinct cryptobiome communities across the shelf. This gradient appears to be consistent across the temporal period studied here. This gradient is also consistent in other taxonomic groups including sessile benthic organisms and fish communities70. Thus, it appears that the bleaching event in 2015 did not cause a homogenization of the cryptobiome at the level that the shelf signature is lost. Individual coral reefs are physically isolated by kilometers of deeper, non-reef habitat, and often prevailing currents limits dispersal between reefs71, leading to dispersal limitation amongst the reefs72. Which could keep cryptobenthic communities between reefs distinct even when perturbed by extensive bleaching events or the associated increased in temperature. A high turnover in the cryptobiome amongst reefs is present in all time periods which was previously observed prior to the coral bleaching16 and is typical of other environments including soft bottom communities in the central Red Sea73 and in tropical coastal habitats75.Previous studies showed a cross-shelf gradient in both community composition and structure across the reefs analyzed here76 and Chust et al.77 who found that dispersal capabilities of species has a negative relationship with beta diversity. Indeed it has been shown that dispersal limitation had a greater effect on the cryptobiome in a pan-regional study compared to environmental conditions53. However, it should be noted that two different processing methodologies, as well as taxonomic level used in the comparisons (ASV and OTU), were undertaken for the different size fractions in this study and thus it cannot be ruled out that methodological biases could account for this finding. Future studies could involve the metabarcoding of the larger size fraction and the use of the same sequencing grouping methodology to minimize this bias as well as including other taxonomic groups such as bacteria and fungi.The cross-shelf distinction in coral reefs communities was clearer for smaller organisms. This is consistent with Soininen, et al.78 with the cryptobiome being shown to be affected by environmental differentiations79. Indeed, temperature, fishing pressure, nutrient levels, sedimentation, and pollution has been hypothesized to influence the communities of coral reefs81. The offshore reef, Abu Madafi, was associated with hard coral and algae, which were negatively correlated with SST while the other reefs were positively correlated with sea surface temperature. This suggests that the cryptobenthic community in the offshore reef is particularly vulnerable to future increases in temperature in agreement with Chaidez, et al.82. Indeed, Abu Madafi showed the largest reduction in species richness between 2015 and 2017 before partially recovering to 2015 levels in 2019.Beta diversity is also driven by variations in local environmental conditions28. The type of community composition of the benthos indeed affects cryptobenthic communities54. Coker et al.15 reported that the branching corals Acropora, Pocillopora, and Stylophora influence community composition in the Cryptobenthos of the Red Sea. In our results, hard coral cover was related to samples from the offshore reef Abu Madafi and soft coral cover with samples from the inshore reef Abu Shoosha.The turnover increased exclusively after the bleaching event in the inshore reef Abu Shoosha, supporting our hypothesis. Indeed, the inshore reefs in the central Red Sea were strongly bleached compared to the midshelf and offshore reefs, particularly branching corals83 and showing what is expected for a community affected by environmental variation as seen in other coral reefs84. Even after the bleaching event, the nearshore to offshore gradient in cryptobenthic communities was still apparent, confirming out first hypothesis. Yet, responses were not always consistent through time across the shelf gradient.Our results demonstrate highly dissimilar communities in the same reef complex refuting the neutral theory of biodiversity68.The lack of sound knowledge on the natural variability of cryptobiota in the Red Sea hampers us from fully disentangling the effects of bleaching on the composition of these assemblages. Our results suggest that cryptobenthic communities will respond to bleaching events and that responses might be size-dependent. While for the largest organisms the number of OTUs increased after the bleaching event not returning to pre-bleaching, for the smaller fraction showed a clear decrease in the number of ASVs two years after the bleaching, with numbers recovering to pre-bleaching levels in 2019. Changes in community composition were also apparent between the response of organisms in both size fractions, reinforcing the importance of more comprehensive assessments of biodiversity patterns when investigating coral bleaching impacts. The partitioning of beta-diversity supported our hypothesis that temporal turnover would be higher in nearshore reefs, particularly in the larger size fraction. This study lays the foundation for future studies in spatio-temporal variability patterns of this critical component of the reef biodiversity using standardized quantifications, and in particular for comparisons with other regions more severely affected by the 2015/2016 global bleaching event, as the south-central Red Sea. However, a more in-depth analysis of shifts in coral species is also necessary as cryptic species often develop symbiotic or mutualistic relationships with specific corals and these might be disrupted even if the overall cover is maintained by other coral species. Directing our attention to the general changes in coral cover lacking deeper taxonomic resolution and disregarding the highly diverse cryptobiome, one might be overlooking relevant effects of bleaching on coral reef ecosystems processes and functioning. This is particularly alarming given the scarce knowledge we have about the ecological roles, life histories and distribution preferences of the members of the cryptobiome, limiting our ability to predict the resilience of coral reefs to climate change16. Given the previously observed cross-shelf patterns, four reefs in the central Red Sea were selected under the framework of a long-term monitoring program in the Red Sea . This framework allowed for the investigation of the components (turnover and nestedness) of beta diversity85. Turnover informs about the replacement of species between pairs of sites and nestedness about the prevalence of species86. It is important to know the contribution of turnover and nestedness to beta diversity in order to understand the ecological and historical processes that shaped the community studied85.Previous studies conducted in the Red Sea identified well-defined reef biodiversity cross-shelf patterns (nearshore to offshore reefs) of the cryptobiome16. However, in this study we reanalyze the 2015 data together with new data from 2017 and 2019 to assess temporal changes. Due to a logistical restriction, the retrieval in 2019 collected two reefs in June  and one in November (Abu Madafi). Locations, deployment and retrieval dates are provided in Table 16. The first sampling date in 2015 occurred prior to the bleaching event in the central Red Sea, which was recorded from September 2015, with nearshore reefs suffered a higher percentage of bleached corals than midshore and offshore reefs28.A total of 33 ARMS units were deployed and retrieved from three sampling periods. Triplicate ARMS were placed in four reefs located across the Saudi Arabian coastal shelf  in the central Red Sea starting in 2013 Fig. , and repa concentrations, (Chla) and percentage cover of ecologically meaningful groups (see "87. Variables with significant (p < 0.01) results greater than 0.5 or less than − 0.5 were assessed and only the environmental variables rubble, hard coral, soft corals and gorgonians, and Chla were retained.Sea surface temperature (SST), chlorophyll-https://oceandata.sci.gsfc.nasa.gov/opendap/catalog.xml on 25th December 2020 choosing MODIS Aqua with 4 km resolution. The time interval of the deployment period was retrieved for each sample unit.Eight-day averages of nighttime SST and daily averages of Chla were downloaded from NASA Oceancolor website 47. The ecological groups and the major benthic components  were identified and percent cover estimated in Coral Point Count with Excel extensions for 48 randomly distributed points as described in Kohler and Gill88.Coral reef benthic communities were assessed based on triplicate (separated by 5 m) 20 m length by 1 m wide photo-transects conducted at each sampling location during the recovery of the ARMS unit between 8 and 10 m depth. Photo quadrats of 1 × 1 m were taken every two meters as in Pearman et al.ARMS were deployed by SCUBA at approximately 10 m depth on the hard reef framework. SCUBA was used to collect the ARMS units; a collection container with 106 µm mesh drainage holes was placed over each unit to retain any mobile organisms inhabiting the units. Once on board the boat, units were placed in plastic containers and submerged with filtered (106 µm) seawater from the site.To disassemble the ARMS, each plate was brushed inside the filtered sea water (106 µm), to remove mobile organisms, then scraped to remove the incrusting and sessile organisms. Sea water was sieved through 106 µm, 500 µm, and 2000 µm mesh to divide the mobile organisms into three fractions: (i) 106–500 µm, (ii) 500–2000 µm, and (iii) > 2000 µm. Each fraction was then homogenized in a blender and preserved in 96% ethanol.The two smallest fractions and the scraped sessile fraction were processed following a metabarcoding technique. The organisms of the larger > 2000 µm were individually segregated and morphologically identified to its lowest possible taxonomical group before DNA barcoding.This research followed the guidelines for sampling recognized at King Abdullah University of Science and Technology (KAUST) at the start of the study. The research permits for sampling in Saudi Arabian territorial seas were procured from the Saudi Arabian coastguard. This study did not target protected or endangered species; therefore, it did not require special authorization. At the time of sampling, no governing body of ethics for animal research was established in KAUST or Saudi Arabia. Consequently, we could not attain ethics approval or waiver.3 instead of weighing each individual tissue sample. Smaller specimens, which did not have the required tissue size, were placed in the lysis buffer whole. A region of 658 bp of the mitochondrial cytochrome oxidase I (COI) gene was amplified using the primer combination jgLCO1490 (TITCIACIAAYCAYAARGAYATTGG) and jgHCO2198 (TAIACYTCIGGRTGICCRAARAAYCA)89. A polymerase chain reaction (PCR) was done using 10 μl of GoTaq G2 Hot Start Master Mix (Promega), 0.6 μl of each primer at 10 μM, 0.2 μl of 20 mg mL-1 bovine serum albumin (BSA), and 1 μl of extracted DNA for a total of 19 μl of reaction for the samples of retrieved in 2015 and 2017. The thermocycling profile of the 2015 and 2017 samples consisted of an initial denaturation step at 95 °C for 5 min followed by 4 cycles of 94 °C-30 s, 50 °C-45 s, and 72 °C-1 min, and by 34 cycles of 94 °C-30 s, 45 °C-45 s and 72 °C-1 min, and a final 8 min elongation phase at 72 °C. Samples from 2019 were amplified in the same region with the same primers using 12.5 μl of QIAGEN Multiplex PCR master mix, 2.5 μl primer at 0.2 μM, 9 μl of RNase-free water, and 1 μl of extracted DNA for a total of 25 μl of reaction. Following the thermocycling profile consisting of an initial denaturation step at 95 °C for 15 min followed by 4 cycles of 94 °C-30 s, 50 °C-1 min 30 s, and 72 °C-1 min, and by 34 cycles of 94 °C-30 s, 45 °C-1 min 30 s and 72 °C-1 min, and a final 10 min elongation phase at 72 °C. PCR products were inspected on 1.5% agarose gels stained with 4 μl of SYBR™ Safe DNA gel stain per 100 mL. Successful PCR products were purified applying 2 μl of Illustra™ ExoProStar™ 1-step from GE Healthcare to 8 μl of PCR product. The PCR product was sequenced in Sanger ABI 3730 capillary platform using 5 μl of primer at 20 pmol and 10 μl of purified PCR product at the King Abdullah University of Science and Technology (KAUST) Bioscience Core Laboratory (BCL).A tissue sample was taken from organisms of the > 2000 µm fraction and DNA extracted using the Qiagen DNeasy DNA extraction kit, following manufacturers protocol. To accelerate the DNA extraction process of the specimens, tissue was sampled at an approximate size of 1 mm89). For amplification, the PCR conditions were an initial 3 min denaturation step at 98 °C, followed by 27 cycles at 98 °C for 10 s, 46 °C for 45 s and, 72 °C for 45 s, with a final extension of 5 min at 72 °C. All PCR reactions were done in triplicate, using 0.4 μl of 10 μM primers, 10 μl of Phusion High Fidelity Mix (2X), 7.2 μl of water and 2 μl (~ 10 ng) of DNA. PCR triplicates were combined and 20 μl of the combined PCR products were cleaned and normalized using SequelPrep Normalization plates (ThermoFisher Scientific) resulting in a final concentration of ~ 1 ng/μl. To add Illumina Nextera tags, a second round of PCR amplification of 8 cycles using KAPA 2 × HiFi Hot Start ReadyMix was undertaken following the manufacturer’s recommendations. The SequelPrep Normalization plates (ThermoFisher Scientific) were used to undertake a second round of cleaning and normalization. Sequencing (2 × 300 bp) was done on an Illumina MiSeq sequencing platform (v3 chemistry) at the King Abdullah University of Science and Technology (KAUST) Bioscience Core Laboratory (BCL), using a spike of 10% PhiX. Raw reads were deposited at the NCBI Short Red Archive under the project accession (To be deposited).For the two smaller size fractions of the bulk mobile organisms 10 g of material was used as an input for the Powermax Soil DNA kit (MO BIO). Extractions were undertaken as per the manufacturer’s instructions with the exception of the bead-beating step. This step was replaced by shaking incubation overnight at 56 °C with the addition of Proteinase K (0.4 mg/mL). DNA was amplified using a versatile primer set amplifying a 313 bp fragment of the COI gene . Consensus sequences of the assemblies were aligned and primer sequences were removed. A Bayesian clustering algorithm with lower 3 and upper 4 variance interval was used to define OTUs with a minBoot threshold of 5191 within R92 was used for the processing of the reads. Briefly reads were truncated 165 and 160 bp for forward and reverse reads respectively) and filtered with a maximum allowable number of “expected errors” (maxEE) of four (forward reads) and six (reverse reads). Sequences were dereplicated and sequence variants were inferred based on a parametric error matrix constructed from the first 108 bp of the sequences. Singletons were discarded and the remaining paired-end reads merged with a minimum overlap of 10 bp and no mismatches in the overlap allowed. Sequences that were shorter than 312 bp or longer than 314 bp were removed from analysis before chimeric sequences were removed using the removeBimeraDenovo script within DADA2. Pseudogenes were detected and removed using Multiple Alignment of Coding Sequences (MACSE93) against the MIDORI database94 as described in Leray and Knowlton Leray and Knowlton54. Firstly, sequences were translated and aligned using the invertebrate code 5 and then the vertebrate code 2. Any sequences containing a stop codon or possessing greater than two frame shifts were considered as pseudogenes and removed from further analysis. Samples were subsampled to evenly for downstream comparison. Taxonomy was assigned against both the NCBI and BOLD databases using RDP classifier algorithm in dada2 with a minBoot threshold of 5195. The two smaller mobile fractions were combined and used in further analysis for simplification. The sessile fraction is not used in this publication.Raw sequences were automatically demultiplexed on the MiSeq machine. Primers were trimmed from the sequences using cutadapt with a maximum of one mismatch allowed (parameters: -e 0.05 –discard-untrimmed). The DADA2 package96 and figures edited in Graphic (Picta). Access to sites in Saudi Arabian waters are subject to approvals from government authorities; such approvals may change on short notice due to government activities on the nearby coastal areas. In 2019, we were not able to access Abu Shootaf because of permitting issues. Therefore, the reef Abu Shootaf was removed from the univariate analysis, given the lack of 2019 samples from this reef. However, in the Venn diagram categorized by time we kept Abu Shootaf. Composition plots for each reef and time were plotted at order level using phyloseq in R and ggplot298.Alpha diversity  was assessed using the non-parametric test Kruskal–Wallis, due to the skewed nature of the data, for the factors reef and time. The number of unique and shared species among reefs and time was visualized in Venn diagrams plotted in VennDiagram package of R99. PostHoc test with Bonferroni adjustment of probability was conducted for the Bray Curtis and Jaccard dissimilarity index distance matrix for reef and time factors using RVAideMemoire program in R100. Bar plots were done in phyloseq and ggplot2 to visualize composition in the order level for each sample98.A permutational multivariate analysis of variance (PERMANOVA) was done using the Bray Curtis and Jaccard dissimilarity index distance matrix for the factors reef and time with an interaction term as well using vegan package in R101. We used 299 iterations of the indval value and retained the taxa that were consistently selected (> 90% of the times). We used 999 permutations in each iteration and restrict the permutation within the year for the reefs and within the reef for the years. Kruskal Wallis test was performed on the abundances of the barcoding fraction for the selected indicator taxa. Kruskal Wallis was chosen over ANOVA given that our data did not meet the ANOVA assumption of homogeneity of variance. No statistical analysis was done in the number of reads of the selected taxa for the metabarcoding fraction, because the error in measuring abundance in a metabarcoding protocol would be higher than the barcoding and a Kruskal Wallis test would provide low statistical power.The indicator taxa were found using the command multipatt of indicspecies in R102. Biological data was transformed using the Hellinger method in the vegan package to give greater importance to the OTUs which were not dominant5.A distance-based redundancy analysis (dbRDA) was performed to visualize the direction and extent of the associations between environmental factors and community structure. The dbRDA was performed for Bray–Curtis and Jaccard dissimilarity distance matrices103 using the Jaccard dissimilarity metric. The nestedness and turnover was assessed between pairs of ARMS of different sampling times for each reef and between reefs for each sampling time using the Baselga Jaccard method in the adespatial package of R104.The nestedness and turnover comparisons were visualized in boxplots. Differences in nestedness and turnover amongst sampling time and reefs were tested with a one-way ANOVA for those that were normally distributed and had homogeneity of variance. Otherwise, a Kruskal–Wallis test was used.A principal coordinate analysis (PCO) was used to visualized the differences in the composition of the cryptobiome between ARMS. The PCO was done in the package labdvs of RSupplementary Information 1.Supplementary Information 2.


N6‐methyladenosine (m6A) has been identified as the most common internal modification of messenger RNA (mRNA). Although m6A modifications were first discovered in the 1970s, advances in technology have revived interest in this field, driving an abundance of research into the role of RNA modifications in various biological processes, including cancer. As analogs to epigenetic modifications, RNA modifications also play an important role in carcinogenesis by regulating gene expression post‐transcriptionally. A growing body of evidence suggests that carcinogens can modulate RNA modifications to alter the expression of oncogenes or tumor suppressors during cellular transformation. Additionally, the expression and activity of the enzymes that regulate RNA modifications can be dysregulated and contribute to carcinogenesis, making these enzymes promising targets of drug discovery. Here we summarize the roles of RNA modifications during carcinogenesis induced by exposure to various environmental carcinogens, with a main focus on the roles of the most widely studied m6A mRNA methylation.The field of epitranscriptomics encompasses the study of post‐transcriptional RNA modifications and their regulatory enzymes. Among the numerous RNA modifications,  We focus primarily on carcinogens, which are classified as carcinogenic to humans (Group 1), as well as agents that are classified as probably (Group 2A) or possibly carcinogenic (Group 2B) to humans by the International Agency for Research on Cancer (IARC) and for which RNA modifications have been shown to have a potential role in promoting malignancy.2N6‐methyladenosine (m6A) on mRNA. Other modifications on mRNA described here include 1‐methyladenosine (m1A), 5‐methylcytosine (m5C), and N6, 2′‐O‐dimethyladenosine (m6Am).The development of sensitive detection methods and high‐throughput RNA‐sequencing technologies has advanced the characterization and mapping of RNA modifications.Regulation of the epitranscriptome is mediated by three categories of regulatory enzymes: writers, erasers, and readers. Writers catalyze the addition of the modification to the RNA molecule; erasers catalyze the removal of the modifications.2.16A methylation is the most prevalent internal modification in eukaryotic mRNA. It is a reversible modification conserved across various organisms, including mammals, plants, viruses, and bacteria.6A is found on roughly 0.1–0.4% of total adenosine nucleotides in mammals.6A‐seq and next‐generation sequencing have revealed that m6A is found in over 7000 human genes and is enriched in long internal exons, near stop codons, and at the 3′‐untranslated region (UTR).m6A is deposited by a multicomponent writer complex, comprising methyltransferase‐like family members 3 and 14 (METTL3 and METTL14), and Wilms‐tumor 1‐associated protein (WTAP).6A erasers, or demethylases, include fat mass and obesity associated (FTO) prand Alkb homolog 5 (ALKBH5).6A readers include YTH‐binding proteins 1, 2, and 3 , YTH domain‐containing 1 and 2 (YTHDC1 and YTHDC2), heterogeneous nuclear ribonucleoprotein A2/B1 and C (HNRNPA2B1 and HNRNPC), and IGF2BPs.m2.25C is found on both tRNA and mRNA at the 5′‐ and 3′‐UTRs.5C writers include the human ortholog NOP2/Sun domain protein 2 (NSUN2) and other NSUN family members, NSUN1‐7, as well as DNA methyltransferase homolog (DNMT2).5C readers include the Aly/REF export factor (ALYREF) and Y‐box‐binding protein 1 (YBX1).m2.31A modifications are less abundant than m6A, constituting about 0.02% of adenosine in human mRNA, and are preferentially enriched around the start codon.1A writers include tRNA Methyltransferases 6 and 61a (TRMT6 and TRMT61a).m2.46Am modification consists of a dimethylation of the adenosine nucleoside.6Am is found adjacent to the mRNA cap structure at the transcription start nucleotide and internally on U2 small nuclear RNA (snRNA); it is estimated that 50–80% of starting adenosine nucleotides contain an m6Am modification.6Am are phosphorylated CTD interacting factor 1 (PCIF1) and METTL4.m33.1Evidence suggests that exposures to toxic metals arsenic, cadmium, and nickel can cause carcinogenesis by modulating cellular pathways through RNA modifications. Further studies are needed to determine whether exposure to other toxic metals, such as cobalt, lead, and chromium, can regulate RNA modifications to drive cancer development.3.1.16A levels  cells.6A mRNA levels by upregulating FTO in A549 cells. In particular, this study found that FTO demethylated transcripts of Apobec3 (A3B), a DNA deaminase and key driver of arsenic‐induced mutagenicity.A3B expression by inhibiting YTHDF2‐mediated A3B mRNA decay, leading to DNA damage and hypermutation in arsenic‐induced carcinogenesis.6A levels in arsenic‐exposed A549 cells, although the mechanism was not investigated in this study.Arsenic is a naturally occurring metalloid in Earth's crust and an IARC Group 1 carcinogen.s Figure . Cui et 6A levels, increased expression of METTL3, METTL14, WTAP, KIAA1429, and YTHDF1, and decreased expression of FTO. In particular, elevated m6A methylation of the p53 activator PRDM2 and p53 inhibitors YY1 and MDM2 was observed.6A levels and expression of METTL3, METTL14, and WTAP expression were increased, whereas FTO was decreased.In contrast, Zhao et al.3.1.26A levels and increased m6A deposition on cancer‐promoting genes, including Cdcp11, which encodes for an oncogenic transmembrane glycoprotein. Mechanistically, METTL3 deposited m6A onto the 3′‐UTR of Cdcp1 mRNA, resulting in increased CDCP1 expression through a YTHDF1‐dependent mechanism in transformed cells.6A levels, along with increased expression of ALKBH5.Pten, decreasing Pten mRNA stability and reducing PTEN expression, which promoted cadmium‐induced cell transformation.6A, METTL3, and FTO, although the precise nature of this relationship was not elucidated.Cadmium is a heavy metal and IARC Group 1 carcinogen, and is strongly associated with the development of pulmonary cancer.3.1.36A levels and increased m6A deposition on Cdcp1 mRNA, causing increased CDCP1 translation in a YTHDF1‐dependent manner.Nickel is an IARC Group 1 carcinogen that can cause cancers of the lung, nasal cavity, and paranasal sinuses.3.1.46A levels were noted, coupled with increased expression of FTO and ALKBH5, and decreased expression of METTL3, METTL14, and WTAP.6Am writer METTL4 was downregulated; however, changes in m6Am levels were not evaluated.Chromium, cobalt, and lead are toxic heavy metals and IARC carcinogens.3.2There is evidence that exposure to chemical carcinogens, such as Fumoisin B1 (FB1) and diethylnitrosamine, can modulate RNA modifications to promote carcinogenesis by affecting oncogenic cellular pathways. Current work on arecoline exposure suggests a role for RNA modifications in promoting existing cancers, but not in inducing cancer initiation. Lastly, recent studies into other chemical carcinogens, including aflatoxin B1 (AFB1), carbon nanotubes (CNTs), cyclophosphamides (CYPs), and endocrine‐disrupting chemical exposures, indicate that these carcinogens can modulate RNA modifications and influence other biological processes, but how these changes may promote cancer initiation is unknown  after causing hepatocellular carcinoma (HCC), cholangiocarcinoma, and renal tubule carcinomas in mouse models.3.2.2N‐Nitrodiethylamine (DEN), is a synthetic oil and hepatocarcinogen classified in Group 2A, and is found in gasoline and other industrial materials.Fto mRNA and protein expression at 24 and 48 h postinjection.Cul4a transcripts were demethylated by FTO, resulting in decreased Cul4a expression.Diethylnitrosamine, or 3.2.35C reader YBX1.Arecoline is an alkaloid and IARC Group 1 carcinogen found in the areca nut, and chewing the areca nut is linked to a high risk of oral cancer due to arecoline exposure.3.2.46A in bovine mammary epithelial cells treated with AFB1, coupled with increased expression of the demethylase ALKBH5 and decreased expression of methyltransferases METTL3 and METTL14. However, how these changes regulate cellular response to AFB1 damage is unknown.6A modification in the liver as well as decreased expression of FTO and YTHDF2, and higher expression of METTL3.6A levels in the liver and suggested that this may be a mechanism of promoting antioxidant gene expression to repair hepatic function.1A writer TRMT6 in HepaRG cells.5C reader ALYREF.AFB1 is a mycotoxin which can pollute grains and animal products.3.2.56A methylation and increased levels of 5‐hydroxymethylcytosine (hm5C) on RNA.5C on RNA.1A writer TRMT61A.CNTs are composed of graphene sheets rolled into a cylindrical fiber and produce particulate matter similar to that of asbestos.3.2.66A levels and increased METTL3 expression.6A on junctophilin‐2 (Jph2) mRNA decreased Jph2 expression and promoted cardiac dysfunction under cardiotoxic stress.6A in a time‐ and concentration‐dependent manner.CYP is an antineoplastic and alkylating agent used to treat certain cancers and diseases.3.2.76A methylation. Interestingly, these 15 RNA transcripts were associated with AHR signaling, which is involved in responses to environmental stress.6Am and PCIF1 expression.6A writers WTAP and RBM15.PCBs are synthetic organic compounds that were widely used in dielectric fluids and sealants until 1979 and can contaminate soil, air, water, and food.6A levels, increased YTHDC2 expression, and decreased FTO expression. In addition, DHEP exposure resulted in decreased NRF2 signaling and increased oxidative stress, which promoted testicular toxicity.6A levels.6A levels.DHEP is an IARC group 2B carcinogen and Zhao et al.3.3There is evidence to suggest that cigarette smoke (CS) exposure can lead to the development of lung cancer, pancreatic ductal adenocarcinoma (PDAC), and esophageal squamous cell carcinoma (ESCC), through changes in RNA modifications. Studies have also found that particulate matter in outdoor air pollution can affect RNA modifications, but it is unclear whether this particulate matter can contribute to carcinogenesis.3.3.16A levels .6A on the Dapk2 transcript, resulting in decreased Dapk2 mRNA stability and expression through a YTHDF2‐dependent mechanism.As of 2019, 1.14 billion individuals worldwide were estimated to be tobacco smokers.6A and upregulated expression of METTL3 were observed after 48 h of treatment.6A on the 3′‐UTR of the tumor suppressor Zbtb4 resulted in decreased ZBTB4 expression through YTHDF2‐mediated mRNA decay.6A sites on the transcript of the tumor suppressor Cyld and promoted Cyld mRNA stability.Similarly, in HBE cells transformed with 2% CS extract, increased levels of m6A‐dependent manner and the NF‐κB‐associated protein NKAP served as a reader of m6A on miR‐25‐p3.In a PDAC study, exposure to 100 μg/ml of CSC in human pancreatic duct epithelial (HPDE6C7) cells for 48 h increased the expression of METTL3 and the microRNA (miRNA) miR‐25‐3p.LINC20078) into the micropeptide (YY1BM) was regulated by m6A modifications and mediated by YTHDF1.LINC20078 and reduced YY1BM levels, ultimately promoting ESCC carcinogenesis.A study in cigarette smoking‐induced ESCC showed that the translation of a long noncoding RNA .6A modification transiently accumulates at sites of DNA damage. METTL3, METTL14, and FTO were also found to localize to sites of DNA damage in response to UVA laser micro‐irradiation or UVC irradiation in U2OS cells, A375 melanoma cells, and HeLa cells.1A modification levels.5C writer Nsun2 in human epidermal and dermal cells.Other research has shown that m3.4.26A levels after 2 h, accompanied by increased expression of METTL14 and ALKBH5, and decreased expression of METTL3, WTAP, and FTO.6A modifications were suggested to play a role in the development of radiation toxicity, and inhibition of FTO and ALKBH5 reduced γ‐radiation hematopoietic injury.Exposure to γ‐radiation can occur through natural sources and medical use, and can lead to leukemia, breast cancer, and thyroid cancer, among others.3.5Viruses can modulate RNA modifications in both viral and host RNAs to enhance replication, promote long‐term latency, and regulate life cycles.3.5.16A changes in various host RNAs (Figure 6A modification on the tumor suppressor Pten transcript, resulting in decreased Pten mRNA stability.HBV is an IARC Group 1 carcinogen.s Figure . In part6A modification of a circRNA, circ‐ARL3, by upregulating METTL3 expression.Hbx mRNA stability by decreasing m6A modification, suggesting an oncogenic role for ALKBH5 in HBV‐induced HCC.In addition, HBX, a Hepatitis‐B viral protein and a major etiological factor in HCC,6A modification on the proto‐oncogene c‐Myc transcript resulted in increased MYC expression and gastric cancer progression.Furthermore, HBV infection is also associated with gastric cancer development, and a study showed that the hepatitis B X‐interacting protein could upregulate METTL3 during HBV infection.3.5.26A‐dependent manner, establishing a positive feedback loop that contributed to EBV‐induced oncogenesis.6A‐modified viral transcripts through recruitment of ZAP, DDX17, and DCP2, making it a potential therapeutic target for EBV‐associated cancers.EBV was the first oncogenic virus discovered, and is an IARC Group 1 carcinogen that causes Burkitt's lymphoma and Hodgkin's lymphoma, among others.3.5.36A and m6Am modifications together, and noted that in the host RNA of cells latently infected with KSHV, 5′‐UTR were hypomethylated and 3′‐UTR were hypermethylated compared with uninfected cells. These differentially methylated genes belonged to pathways involved in cellular transformation, including mTOR signaling, ephrin receptor signaling, and hypoxia, suggesting that KSHV may utilize RNA modifications to induce tumorigenesis.6A has been shown to be required for the onset of KSHV lytic replication, a major contributor to cancer development, and regulate pre‐mRNA splicing of the replication transcription factor.KSHV is a γ‐2 herpesvirus and an IARC Group 1 carcinogen that can cause Kaposi's sarcoma and primary effusion lymphoma.4Compelling evidence has demonstrated the crucial role of RNA modifications in maintaining the oncogenic traits of established cancers and cancer cells. Recent emerging evidence suggests that upon exposure to external carcinogens, RNA modifications are also implicated much earlier in the carcinogenic process. In this review, we summarize advances in the field, and consider how various exogenous exposures may use RNA modifications to promote oncogenesis.For some carcinogens such as arsenic and CS, there are several described mechanisms by which RNA modifications promote carcinogenesis. More studies must be conducted to understand how the various mechanisms co‐exist and interact with one another to pinpoint where potential therapies may be most successful. However, for many carcinogens such as CNTs and γ‐radiation, a direct link to carcinogenic processes is yet to be established and the limited evidence presented here indicates that these would be areas worth pursuing.6A modifications and their writers, erasers, and readers. However, preliminary evidence for exposures to carcinogens such as UV exposure and PM suggests that m5C and m1A modifications may also play an important role in carcinogenesis. As research into RNA modifications continues to gain prominence, further studies must investigate how different types of chemical modifications in RNA affect carcinogenic processes. Finally, out of 121 Group 1 IARC carcinogens, only 16 were described in this review, indicating a large breadth of potential research into other carcinogenic exposures that are yet to be fully explored. Overall, the role of RNA modifications in carcinogenesis is a new emerging field for continuing to elucidate the molecular mechanisms of oncogenesis and for developing new cancer therapies.Notably, the existing research on RNA modifications in carcinogenesis focuses heavily on mYu‐Ying He conceived the project. Michelle Verghese, Emma Wilkinson, and Yu‐Ying He wrote the paper.


Proximity ligation technologies are extremely powerful tools for unveiling RNA-protein interactions occurring at different stages in living cells. These approaches mainly rely on the inducible activity of enzymes (biotin ligases or peroxidases) that promiscuously biotinylate macromolecules within a 20 nm range. These enzymes can be either fused to an RNA binding protein or tethered to any RNA of interest and expressed in living cells to biotinylate the amino acids and nucleic acids of binding partners in proximity. The biotinylated molecules can then be easily affinity purified under denaturing conditions and analyzed by mass spectrometry or next generation sequencing. These approaches have been widely used in recent years, providing a potent instrument to map the molecular interactions of specific RNA-binding proteins as well as RNA transcripts occurring in mammalian cells. In addition, they permit the identification of transient interactions as well as interactions among low expressed molecules that are often missed by standard affinity purification strategies. This review will provide a brief overview of the currently available proximity ligation methods, highlighting both their strengths and shortcomings. Furthermore, it will bring further insights to the way these technologies could be further used to characterize post-transcriptional modifications that are known to regulate RNA-protein interactions. RNA and proteins are constantly interacting in living cells at multiple stages and their interaction is fundamental for their biological functions . RNA molSeveral methods have been developed to characterize RNA-protein interactions at global level, distinguished into RNA-centric or protein-centric approaches. Usually, the RNA or protein of interest is isolated by affinity purification from cells and binding partners are identified by high-throughput mass spectrometry and RNA sequencing approaches, respectively . DespiteThe use of PDB approaches to assess protein-protein interactions, protein cellular localization and compartmentalization, including the regulation mediated by post-translational modifications have been recently reviewed . The focE. Coli Bifunctional ligase/repressor enzyme carrying the R118G mutation (BirA*) that promiscuously biotinylates any proximal protein  of proximal proteins. The first used biotin ligase is the  protein . Severaln 30 min ; and then 30 min .2O2) treatment, APEX2 converts phenol substrates into short-lived phenoxyl radicals with a half-life of less than one millisecond that covalently attach electron-rich amino acids and nucleotides with a special preference for tyrosine and guanine, respectively , has been exploited for PDB approaches. APEX2 is the more active variant of the initial APEX enzyme derived from soybean . Upon hyectively .The biotinylation process mediated by PDB enzymes is strongly dependent on the abundance, length, composition and structure of the targeted molecules. Moreover, substrate regions have to be exposed and freely available to the labeling of the PDB enzyme. Hence, the labeling intensity is not directly correlated with the strength of the interaction . A detaiRNA centric PDB methods assess which proteins are interacting with a selected RNA transcript, in living cells. The PDB enzyme is recruited to the RNA of interest and, once activated, it starts biotinylating any protein present in proximity over time. Even proteins that transiently interact with the targeted RNA transcript can be covalently labeled with one or multiple biotin molecules. The resulting biotinylated proteins are then affinity purified under denaturing conditions and identified by MS-based proteomics through either label free quantification or stable isotope labeling methods, as previously summarized . DiffereCurrently, the main approaches are based on 1) Aptamer; 2) Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR). They are represented in Aptamer-based PDB exploits the tagging of the RNA of interest with the MS2 or BoxB aptamers that are specifically recognized and bound by the MS2 coat protein (MCP) and λN peptide fused in frame with the labeling enzyme, respectively , without cleaving the RNA or targeting DNA sequences (equences . The PDBFour similar CRISPR PDB tools have been developed: 1) CARPID (CRISPR assisted RNA-protein interaction detection method) , 2) Cas1RNA centric PDB methods require the use of appropriate experimental controls. It is advisable to include in the analysis an unrelated RNA, with length and GC-content similar to the RNA of interest, whose results can be used to measure the experimental background. Moreover, an RNA with known interacting partners can be used as a positive control, assessing the efficacy of the strategy and the sensitivity of the assay and the related instrumentation .2O2 treatment, the APEX enzyme starts biotinylating any macromolecule present in its proximity. The cells are then harvested and biotinylated proteins, together with their associated RNA transcripts, pulled down through streptavidin beads. The RNA is extracted from the beads, purified and analyzed by standard NGS. This strategy has been extremely helpful for the characterization of the RNA transcripts associated with subcellular compartments, such as stress granules (SGs) and the nuclear lamina, using protein markers such G3BP1 and LAMIN B1, as baits  approaches. Differently from standard protein centric methods, this strategy does not require the use of an antibody for the protein target and, hence, can be applied to any protein or protein isoform . The proas baits .In the APEX-Seq approach, APEX2 is used to directly biotinylate nucleic acids . Thus, bRecently, APEX-Seq has been used to investigate even membraneless domains, using as bait their specific markers. Relevant examples are APEX2-FBL for the nucleolus; APEX2-SRSF7, APEX2-SRSF1, and APEX2-RNPS1 for nuclear speckles; APEX2-SMN2 for Cajal bodies; APEX2-SAM68 for the SAM68 bodies; APEX2-PML and APEX2-SP100 for the PML bodies; APEX2-NPAT for the histone locus bodies; and APEX2-LMNA for the nuclear lamina .The labeling of RNA transcripts can be improved by using biotin-aniline as peroxidase substrate. Conversely to proteins, APEX2 biotinylates RNA transcripts approximately 3-fold times more in the presence of biotin-aniline compared to biotin-phenol . NeverthTo correctly define the RNA interactome of a given bait, it is preferable to conduct the same purification strategy on multiple baits that preferably localize to different cellular compartments. This can help to properly assess the experimental background and identify those frequently recurring RNA-protein interactions.The characterization of RNA-protein interactions in living cells has to take into consideration four aspects: 1) the bait has to be soluble to be affinity purified; 2) the preys must interact with the bait for a sufficient time and amount to be detected; 3) RNA-protein interactions have to be preserved during the whole affinity purification procedure; 4) the signal to noise ratio must be high to permit the correct identification of true interactors and minimize number of false positives. To this extent, PDB approaches have sensibly favored the biochemical workflow applied to assess the interactome of an RNA or a protein of interest. The biotinylation process occurs in living cells where the cellular environment is preserved. This eliminates the formation of false positive RNA-protein interactions that can occur during cell lysis, when the cellular membranes are disrupted. At the same time, any bait can be purified thanks to the possibility of adopting even denaturing cell lysis buffers.−14 M) (The strong affinity between streptavidin and biotin (Kd 10−14 M)  allows p−14 M) . TherefoPDB technologies rely on the activity of a bacterial enzyme fused or tethered to the molecule of interest and, hence, poses important limitations. The enzyme may alter the biological properties of the targeted molecules. For instance, the paraspeckle proteins NONO, PSPC1, and EWSR1 tagged with APEX2 at their N-terminus showed a non-physiological localization compared to the respective endogenous proteins . Thus, iOne of the current limits for the characterization of RNA-protein interactions is the difficulties in performing an unbiased identification and characterization of the post-transcriptional modifications present in the RNA transcripts that are interacting with cellular proteins. RNA post-transcriptional events have been recognized with important regulatory functions and are known to regulate RNA folding into secondary structures and the propensity of the RNA to interact with partners . Protein


Alphaviruses such as the human pathogenic chikungunya virus (CHIKV) and Ross River virus (RRV) can cause explosive outbreaks raising public health concerns. However, no vaccine or specific antiviral treatment is yet available. We recently established a CHIKV vaccine candidate based on trans-amplifying RNA (taRNA). This novel system consists of a replicase-encoding mRNA and a trans-replicon (TR) RNA encoding the antigen. The TR-RNA is amplified by the replicase in situ. We were interested in determining whether multiple TR-RNAs can be amplified in parallel and if, thus, a multivalent vaccine candidate can be generated. In vitro, we observed an efficient amplification of two TR-RNAs, encoding for the CHIKV and the RRV envelope proteins, by the replicase, which resulted in a high antigen expression. Vaccination of BALB/c mice with the two TR-RNAs induced CHIKV- and RRV-specific humoral and cellular immune responses. However, antibody titers and neutralization capacity were higher after immunization with a single TR-RNA. In contrast, alphavirus-specific T cell responses were equally potent after the bivalent vaccination. These data show the proof-of-principle that the taRNA system can be used to generate multivalent vaccines; however, further optimizations will be needed for clinical application. Alphaviruses are part of the Togaviridae family. The different members are globally distributed and are transmitted to various vertebrate hosts by mosquito vectors. Traditionally, alphaviruses are classified into “Old World” and “New World” viruses. In general, infection with New World alphaviruses such as Western, Eastern, and Venezuelan equine encephalitis viruses  can cause encephalitic diseases, while infection with Old World alphaviruses causes arthritogenic diseases.Alphaviruses are single-stranded positive-sensed RNA viruses. Their genome consists of two open reading frames, which encode for either the non-structural proteins (nsPs) or the structural proteins. After infection of a host cell, the first open reading frame of the genomic RNA is directly translated and the four nsPs nsP1–nsP4) assemble into the alphavirus replicase. The replicase synthesizes a full-length negative-sensed RNA, from which the genomic and subgenomic RNA are transcribed. The structural proteins (capsid and the envelope proteins E3-E2-6K-E1) are translated from the subgenomic RNA and their expression is under control of the subgenomic promotor (SGP) [–nsP4 assAedes aegypti to Aedes albopictus, and it has now been identified in over 100 countries and has caused multiple epidemics [Among the different members of the Old World alphaviruses are the human pathogenic chikungunya virus (CHIKV), Ross River virus (RRV), and Mayaro virus (MAYV). CHIKV rapidly spread worldwide after the vector extension from pidemics ,5,6. RRVpidemics . A furthpidemics . Acute Cpidemics . Symptompidemics .Different vaccine candidates against CHIKV and RRV have been studied in vitro and in vivo, and although some are promising, none has yet been licensed ,13,14. WRecently, a phase-I clinical study could show the induction of immune responses against the New World alphaviruses WEEV, EEEV, and VEEV using a trivalent virus-like particle vaccine . HoweverHere, we adapted the CHIKV taRNA vaccine candidate for a combined vaccine against the human pathogenic CHIKV and RRV. We could demonstrate that when the CHIKV and RRV envelope proteins were encoded on two separate TR-RNAs, both TR-RNAs were amplified by the CHIKV replicase, resulting in a high antigen expression in vitro. Moreover, vaccination of BALB/c mice with the taRNAs induced humoral and cellular immune responses in vivo. Similar T cell responses could be observed against CHIKV and RRV. In contrast, the total antibody titers and neutralization capacity were reduced after the bivalent vaccination in comparison to a single vaccination.2 at 37 °C. Cell lines were originally obtained from ATCC (American Type Culture Collection. HEK 293T (ATCC CRL-11268) and Vero E6 cells (ATCC CRL-1586) were grown in Dulbecco’s modified Eagle’s medium (DMEM) , and BHK 21 (ATCC CCL-10) and isolated mouse splenocytes in Roswell Park Memorial Institute (RPMI) medium .All cells were cultured in medium supplemented with 10% fetal bovine serum , 1% L-glutamine , and 1% penicillin/streptavidin  in a humidified atmosphere with 5% COFemale, 6–8 week-old BALB/c_Rj mice  were immunized by intradermal injection on day 0 and day 28. The respective RNAs were diluted in 20 µL RNase-free PBS. Blood was collected on day 0 and day 28 by retro-orbital bleeding and final blood was collected on day 56. Splenocytes were harvested on day 56.Animal experiments were performed after approval of the Darmstadt regional council, Germany  in compliance with legal requirements .SpeI and XhoI to generate the plasmid encoding the TR-R-RNA. The sequence of TR-R between the T7 promotor (TAATACGACTCACTATAG) and polyA is given in CHIKV sequences are based on the isolate LR2006 OPY1 , RRV T46 HEK 293T cells were transfected with 2.5 µg total RNA in 6-well plates. For the analysis of cell supernatants, 6 × 106 HEK 293T cells were transfected with 10 µg total RNA in 10 cm dishes. The ratio of replicase-RNA to TR-RNA was 4:1.HEK 293T cells were transfected 24 h after seeding with the respective RNAs using Lipofectamine MessengerMAX according to the manufacturer’s protocol . To evaluate RNA amplification and cellular protein expression, 0.8 × 10TR-RNA amplification was quantified by RT-qPCR, as described previously . BrieflyProtein expression was evaluated from cell lysates or concentrated supernatants  by Western blot analysis. Protein concentrations were determined with the Pierce BCA Protein-Assay Kit  and 20 µg protein per sample were separated by SDS-PAGE and transferred onto polyvinylidene difluoride (PVDF) membranes. Primary antibodies were directed against CHIKV E2 , RRV E2 (ATCC VR-1246AF), and β-actin . After incubation with appropriate secondary horseradish peroxidase (HRP)-coupled antibodies, proteins were detected with the ECL detection system  and the Fusion FX7 imaging system .6 pfu virus  per well. For IgG endpoint titer determination, serially diluted serum samples  were added for 1 h at 37 °C. For isotype determination, serum samples were diluted 1:100. Respective anti-mouse secondary HRP-coupled antibodies were added for 1 h at room temperature prior to detection with TMB substrate .Virus-binding antibodies were detected by ELISA as described previously . BrieflyNeutralizing antibodies were analyzed as described previously . In brie6 splenocytes per well were stimulated with CHIKV, RRV, or MAYV at an MOI of 5 for 22 h at 37 °C. For detection, TMB substrate for ELISpot  was used and spots were counted with an Eli.Scan ELISpot scanner .Murine IFN-γ ELISpot assays were performed to assess virus-reactive T cells after vaccination. The mouse IFN-γ ELISpot Kit  and HRP streptavidin (BD Biosciences) were used following the manufacturer’s instructions. On multiscreen immunoprecipitation (IP) ELISpot PVDF 96-well plates , 1050), which represents the dilution factor required to obtain 50% inhibition of lentiviral vector particle transduction. IC50 values were calculated as a nonlinear regression (log(inhibitor) versus response ) using the GraphPad Prism 7.04 software. To analyze data, one-way or two-way analysis of variance (ANOVA) with appropriate post-test was performed with GraphPad Prism 7.04 software.Mean values and standard deviations were calculated with Excel. ELISA IgG endpoint titers were determined from serial serum dilutions by a four-parameter sigmoidal regression curve with GraphPad Prism 7.04 software . Cutoff values to interpolate the endpoint titers were based on the optical density (OD) values from naïve control sera (average at lowest dilution + four standard deviations). Samples which were below the cutoff were set to 10 for plotting and statistical analysis. The neutralization capacity of serum samples is given as the reciprocal half-maximal inhibitory concentration (ICA taRNA vaccine candidate consisting of a non-replicating RNA encoding the CHIKV replicase and a TR-RNA encoding the CHIKV envelope proteins (TR-C) was able to induce a potent and protective immune response in mice . To exteTR-RNA amplification rates by an alphavirus replicase depends on RNA length, and a shorter RNA is preferentially amplified ,16,20. HTo evaluate if the antigens are also efficiently expressed from both TR-RNAs, expression of the CHIKV and RRV E2 proteins 6 h and 24 h after transfection/infection was studied by Western blot analysis. Expression of both proteins could already be detected 6 h after transfection and, in line with RNA amplification, this expression increased by 24 h A. Both EIn summary, the combination of the TR-C- and TR-R-RNAs in a bivalent taRNA system demonstrated efficient RNA amplification by the replicase, which resulted in a high antigen expression in vitro. Since protein expression of the TR-RNA combination was only slightly reduced in comparison to transfection of a single TR-RNA, it encouraged us to study the bivalent alphavirus vaccine candidate further in vivo.To study the potential of the bivalent taRNA system to induce immune responses against both encoded antigens in vivo, BALB/c were immunized intradermally with a homologous prime-boost vaccination on day 0 and day 28 A. As vacTo study potential immune interferences, the immune responses of mice that had received only the TR-C-RNA, only the TR-R-RNA, or both TR-RNAs were compared. Firstly, the total CHIKV- and RRV-binding antibodies in mouse sera were measured by ELISA. As expected, CHIKV-specific IgG was detected in mice vaccinated with the replicase- and TR-C-RNA, and the antibody titers were boosted by the second vaccination B. AdditiInterestingly, some TR-C-RNA-vaccinated mice had RRV-binding antibodies and some TR-R-RNA-vaccinated mice had CHIKV-binding antibodies, although the induction was not significant. To further evaluate the potential maturation of pan-alphavirus-binding antibodies, MAYV-binding antibodies were also determined. Some mice had MAYV-binding IgG; however, compared to control mice, the observed antibody titers were not significant C.To further characterize the induced humoral immune responses, the CHIKV- and RRV-specific IgG subtypes on day 56 were determined. IgG2a and IgG2b can protect against alphavirus infections via Fc receptor functions . Indeed,Next, neutralizing antibodies were assessed using pseudotyped lentiviral vector particles. At day 56 after vaccination with replicase-RNA and TR-C-RNA, CHIKV-neutralizing antibodies were detected for both tested vaccine doses A. If micTo further analyze the immune responses after taRNA vaccination, splenocytes were harvested on day 56 and re-stimulated with CHIKV, RRV, or MAYV. IFN-γ-secreting T cell were then quantified by ELISpot. Immunization with the TR-C-RNA induced significant amounts of CHIKV-reactive T cells A and immAlphaviruses such as the human pathogenic CHIKV, RRV, and MAYV can cause massive outbreaks of acute infections with high fever, myalgia, and polyarthralgia. The arthritogenic symptoms of patients can become persistent and last for months to years. However, no alphavirus vaccine has so far been licensed. Here, we show that a bivalent taRNA vaccine candidate against CHIKV and RRV was able to induce humoral and cellular immune responses in mice.In vitro, the TR-RNAs were amplified by the CHIKV replicase. Importantly, after transfection of both TR-RNAs, each RNA was efficiently amplified with only a slight preference for the TR-C-RNA. In contrast, a clear preference for a shorter TR-RNA has been observed in our previous studies . Here, wThe TR-RNA was efficiently amplified and the antigenic CHIKV E2 and RRV E2 proteins were expressed at similar levels to those in CHIKV/RRV-infected cells. Furthermore, the CHIKV and RRV E2 proteins were released into the cell supernatant without the expression of an alphavirus capsid protein as we had observed before . This isIndeed, intradermal taRNA vaccination could induce humoral and cellular immune responses in mice. The antibody titers and neutralization activity after the CHIKV only vaccination were comparable to the observed immune responses of our previous study . The bivIn addition to neutralizing antibodies, non-neutralizing antibodies of the IgG2a/c subtype are able to protect mice against MAYV infection . ImportaSuboptimal neutralizing or non-neutralizing cross-reactive antibodies can lead to enhanced disease after dengue virus infections due to antibody-dependent enhancement (ADE), which is a major challenge for vaccine development . In contAlthough very promising, the taRNA vaccine platform should be further optimized. In this study, we used unmodified RNA, but the replicase-RNA could be nucleoside modified. Nucleoside modification reduces the innate immune responses and thus leads to a higher antigen expression and superior immune responses . We applIn our previous study, the single taRNA CHIKV vaccine candidate protected mice against a high dose CHIKV challenge . Still, In summary, taRNA vaccines are a promising vaccination platform, which can be used to design multivalent alphavirus vaccine candidates. However, further optimizations are required for clinical application and the effects of potential immune interference mechanisms on antibody responses need to be monitored.