The central dogma of molecular biology describes the flow of genetic information from DNA to protein via an RNA intermediate. For many years, RNA has been considered simply as a messenger relaying information between DNA and proteins. Recent advances in next generation sequencing technology, bioinformatics, and non-coding RNA biology have highlighted the many important roles of RNA in virtually every biological process. Our understanding of RNA biology has been further enriched by a number of significant advances in probing RNA structures. It is now appreciated that many cellular and viral biological processes are highly dependent on specific RNA structures and/or sequences, and such reliance will undoubtedly impact on the evolution of both hosts and viruses. As a contribution to this special issue on host immunity and virus evolution, it is timely to consider how RNA sequences and structures could directly influence the co-evolution between hosts and viruses. In this manuscript, we begin by stating some of the basic principles of RNA structures, followed by describing some of the critical RNA structures in both viruses and hosts. More importantly, we highlight a number of available new tools to predict and to evaluate novel RNA structures, pointing out some of the limitations readers should be aware of in their own analyses. Indeed, RNA is now implicated in almost every cellular process, including immune defense. It is also recognized as being key to viral infection processes. RNA multifunctionality comes from its ability to fold into complex three-dimensional structures that can often switch conformation to effect different functions such as binding other RNA molecules or proteins. The initial fold of an RNA molecule depends primarily on its sequence and is established by Watson-Crick pairing of complementary bases into stem-loop structures that then orientate themselves relative to one another [for a general review of RNA structure see ]. This tpicornaviridae, initiate translation from IRES elements  . Anotherenv such as the C2 hairpin and the Rev responsive element (RRE)  . It has nt (RRE) . As a cont (RRE) , 40. Thee genome .RNA viruses with segmented genomes can undergo reassortment, leading to the exchange of entire gene segments, potentially giving rise to new viral strains to which humans have no previous immunity. In influenza A, reassortment has been historically associated with the emergence of pandemic strains, including the most recent H1N1 2009 pandemic which contained influenza gene segments from human, avian and swine lineages , 42. It in cis   . When HIin (ZAP) , suggestin (ZAP) , 54.env ORF are better able to control simian immunodeficiency virus (SIV) infection  phylogenetic approaches (ii), structure prediction algorithms, and (iii) experimental methods with chemical/enzymatic probes. Whilst these methodologies cannot determine RNA structure at atomic resolution, they are nevertheless able to generate models that provide useful biological insights , 66, 67.proteins , 73. Furproteins  and crosproteins , and by proteins . More reproteins –84. Thesproteins , the abiJM conceived the review. RS, MN, AL, JM, and JK wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The study by Garson et al.  failed tAnother important consideration in such studies is that ALS is a heterogeneous syndrome. Several genetic mutations have been associated with the disease, which suggest different pathophysiological pathways in these subtypes. Further, the clinical phenotypes may also vary. Some patients have predominant lower motor neuron involvement while others have predominant upper motor neuron involvement. A recent study classified ALS into three categories based on RNA seq analysis of the brain. One of these groups had activation of transposable elements including HERV-K that represented 20% of the samples . Thus, aGarson et al. were meticulous in the design of their PCR assay and they followed the standard recommendations, checking for amplification specificity of the primers and calculating PCR efficiencies. However, if the goal of the study is to quantify RNA transcripts by qPCR, then the integrity of the samples measured by the RNA integrity number (RIN), is key . RIN canAs the authors did in this study, to control for small differences in RNA integrity and total amount of starting RNA, the use of a reference gene is strictly recommended. The selected reference gene should not be regulated or at least be minimally regulated and it must be sensitive to RNA degradation , logicalDetection of transcripts that may be activated poses additional challenges in neurodegenerative diseases since the very cells of interest are the ones that are in various stages of degeneration or may be apoptotic. Analysis of expression by qPCR, due to its dependence on the appropriate selection of housekeeping genes, might not be ideal in these type of diseases. In a study by Rydbirk et al. , the autA better method to use in these conditions might be RNAseq, where the quantification of the expression of a gene is “number of transcripts/million of total reads”. This method would be more adequate for neurodegenerative diseases since it controls for the possible decrease in global transcriptional activity in the disease. However, even this technique would not consider the differential susceptibility to degeneration of the various cell types in the cortex. Another method might be to perform laser capture microdissection of the neurons and then analyze the cells by RNA seq. The expression of the transcripts can also be visualized by in situ hybridization in tissue sections. Thus, due to the complexity of the problem and several technical challenges, no reliable conclusion can be made from the study by Garson et al., but further studies are needed using alternative methods and appropriate controls from brain samples with preserved integrity of the RNA to explore the expression of HERV-K in ALS.


Lilium lancifolium. Consequently, the cpDNA of L. lancifolium is 152,479 bp in length, which consists of one large single copy , one small single copy , and two inverted repeat regions . The comparative genomic analysis of newly sequenced cpDNA and transcriptome data revealed 90 RNA editing sites of which two positions are located in the rRNA coding region of L. lancifolium. A further check on the secondary structure of rRNA showed that RNA editing causes notable structural changes. Most of the RNA editing contents are C-to-U conversions, which result in nonsynonymous substitutions. Among coding regions, ndh genes have the highest number of RNA editing sites. Our study provided the first profiling of plastid transcriptome analyses in Liliales and fundamental information for further studies on post-transcription in this order as well as other petaloid monocotyledonous species.Transcriptome data provide useful information for studying the evolutionary history of angiosperms. Previously, different genomic events  were discovered in the plastid genome of Liliales; however, the effects of these events have not addressed because of the lack of transcriptome data. In this study, we completed the plastid genome (cpDNA) and generated transcriptome data of  Arabidopsis thaliana, rice, sugarcane, and so on5. These data added a deeper understanding of the gene expression and biological mechanisms that allow plants to survive and adapt to the environment. For example, transcriptome data revealed the mechanism that showed how sugarcane responded after being infected by bacteria or a virus5. Also, transcriptome data suggested genes that are responsible for drought and salinity tolerance in rice4. The transcriptome analysis was not only reported for model plants but also wild species. For instance, RNA editing sites were observed in Spirodela polyrhiza and Phalaenopsis aphrodite subsp formosana7. Also, the mechanism of cold responses in Lilium lancifolium was profiled from transcriptome data8. In plants, RNA editing resulted from the modification of nucleotide sequences. This process has a significant effect on gene expression because it can cause the presence of an internal stop codon11. In fact, the effect of RNA editing on the metabolism of plants was reported12. These studies suggested the necessity of transcriptome data in studying the evolution in angiosperms.In the genomic era, besides the nuclear, mitochondrial, and plastid genomes, transcriptome data also provide useful information for exploring the evolutionary history of angiosperms. Previously, different transcriptome studies of model plants by applying next-generation sequencing method (NGS) were reported, including 13. The plastid genomes of this order have been intensively studied, and different genomic events  were discovered19. For example, various stages of rps16 deletion were recorded in the tribe Melanthieae 19. Also, duplication events were found in the Paris  species18. Although genomic events were reported, their effects on the post-transcriptional process are unclear because of the lack of plastid transcriptome data in Liliales. Therefore, in this study, we conducted the first plastid transcriptome analysis in Liliales. First of all, Lilium lancifolium was selected as the target species. Then, complete plastid genome (cpDNA) of L. lancifolium was sequenced using Next-generation sequencing method. Based on the new RNA editing data, we test the hypothesis of whether the pseudogenization can be reversed in L. lancifolium. We also check the effect of RNA editing site on the secondary structure of rRNA.Liliales is a member of petaloid monocotyledons and consists of 10 families of 1558 taxaL. lancifolium  and Korea (accession number KY940844), of which the length of cpDNA is identical , the gene contents and orders are similar among the three individuals. However, the percentage of the identity of new cpDNA in L. lancifolium in this study is 99.8% and 99.9% compared to counterparts from Korea and China, respectively. Also, the translation initiation factor IF-1 (infA) gene was not annotated in previous data, but it was predicted as a pseudogene in this study because of the presence of internal stop codons within the coding region. Additionally, the different length of poly A sequence after start codon caused two types of cemA gene. The first type is functional cemA in L. lancifolium from China, of which 9-bp-poly A sequence was found. In contrast, the malfunctioning cemA was annotated in cpDNA from Korea counterparts, of which 10-bp and 11-bp-poly A sequences were found and caused internal stop codons in the coding region. The IGS regions among three cpDNA sequences of L. lancifolium showed a high similarity (over 95%), except the ISG regions of petA-psbJ, ndhF-rpl32, and ccsA-ndhD with similarity of 84.2%, 93.95%, and 94.83%, respectively  followed by the RNA polymerase genes (16 sites). The Rubisco gene (rbcL) has only one RNA editing site within its coding region. In contrast, ndhB gene has the highest number of editing sites (10 sites). The most abundant content of RNA editing in L. lancifolium is C-to-U conversion  to L (Leucine) is the most frequent (25 sites), followed by S (serine) to F  with 17 sites. Nevertheless, 12 out of 90 editing sites resulted in synonymous substitution  than those in previous studies which have an identical length . Additionally, the Korean L. lancifolium has nonfunctional cemA which was found as a functional gene in Chinese counterpart. These findings suggested the interspecific diversification of plastid genome among wild plants and the need for further studies on this issue. Additionally, low similarities in IGS of petA-psbJ, ndhF-rpl32, and ccsA-ndhD suggest these regions as hot-spot sites for further studies on evolution of L. lancifolium and related species . In contrast, the case of cemA needs further investigations. Among the four examined monocots, there are different numbers of RNA editing sites . To isolate total RNA, the fresh leaves were immediately put in liquid nitrogen after collected. Then, they were stored in the cold condition until being used for RNA isolation, which was conducted using Plant RNeasy mini Kit . Both DNA extraction and RNA isolation were conducted based on manufacturer’s instruction. The quality of DNA and RNA were tested using gel electrophoresis and one spectrophotometer.The mature fresh leaves of L. lancifolium were applied to Illumina Hiseq 200 and Nextseq 500, respectively. First of all, the DNA and RNA were fragmented. Then, newly fragmented DNA and RNA were hybridized and ligated with adapters. In the next step, PCR amplification was employed to create the sequence library. Finally, the library was sequenced and resulted in the DNA-NGS data of 301 bp in length and transcriptome data of 76 bp in length. The DNA-NGS data were imported to Geneious program for further analysis23. The reads were trimmed with more than 5% chance of an error per base before being assembled to reference cpDNA of Lilium lancifolium (Accession number KY940844) with similarity over 95% between reads and reference sequence. Consequently, there are 3,852,736 reads of which 17,473 reads (0.45%) were assembled to reference with coverage of 34.5x. The newly completed cpDNA of L. lancifolium was annotated and manual adjusted in Geneious program. The map of cpDNA was illustrated by OGDraw24 with manual modification. The new cpDNA in this study was aligned with previously reported cpDNA of L. lancifolium (Accession number KY748297 and KY940844) using MAUVE alignment embedded in Geneious to identify hot-spot regions25. Also, the newly assembled cpDNA (GenBank Accession number MH177880) was used for identifying RNA editing sites. The RNA sequence data were imported to Geneious and aligned to cpDNA of L. lancifolium using Bowtie 2.0 with mismatch ≤226. The filtered reads  were then analyzed using Cufflinks to calculate the read per kilobase million (RFKM) and TopHat for variants calling27. The determination of RNA editing sites was based on the division of reads with editing based on the total reads of that position. If the frequency of C-to-U or U-to-C conversion was over 5%, that position was recognized as an RNA editing sites as described in previous study28. An online tool (http://rna.tbi.univie.ac.at/) was used to predict the second structure of rRNA29. Transcriptome data of L. lancifolium was deposited to NCBI (SRA accession SAMN08940087).To generate NGS data, the total DNA and RNA from leaves of Supplementary Tables


Morphological analysis of landforms has traditionally relied on the interpretation of imagery. Although imagery provides a natural view of an area of interest (AOI) images are largely hindered by the environmental conditions at the time of image acquisition, the quality of the image and, mainly, the lack of topographical information, which is an essential factor for a correct understanding of the AOI's geomorphology.More recently digital surface models (DSMs) have been incorporated into the analytical toolbox of geomorphologists. These are usually high‐resolution models derived from digital photogrammetric processes or LiDAR data. However, these are restricted to relatively small areas and are expensive or complex to acquire, which limits widespread implementation.In this paper, we present the multi‐scale relief model (MSRM), which is a new algorithm for the visual interpretation of landforms using DSMs. The significance of this new method lies in its capacity to extract landform morphology from both high‐ and low‐resolution DSMs independently of the shape or scale of the landform under study. This method thus provides important advantages compared to previous approaches as it: (1) allows the use of worldwide medium resolution models, such as SRTM, ASTER GDEM, ALOS, and TanDEM‐X; (2) offers an alternative to traditional photograph interpretation that does not rely on the quality of the imagery employed nor on the environmental conditions and time of its acquisition; and (3) can be easily implemented for large areas using traditional GIS/RS software.The algorithm is tested in the Sutlej‐Yamuna interfluve, which is a very large low‐relief alluvial plain in northwest India where 10 000 km of palaeoriver channels have been mapped using MSRM. The code, written in Google Earth Engine's implementation of JavaScript, is provided as  Morphological analysis of landforms has traditionally relied on the interpretation of near‐vertical aerial and satellite images. Although imagery provides a natural plan‐view of an area of interest (AOI), effective interpretation is largely hindered by the environmental conditions at the time of image acquisition, the quality of the image and, most importantly, the lack of topographical information, which is an essential factor for a correct understanding of AOI morphology. Visual interpretation of aerial imagery has usually relied on assessment of the planimetric morphology of the features of interest and the image elements providing indications of elevation change, such as shadowed areas. Although aerial photography time series capacity to measure geomorphic change cannot be currently matched, the problems associated to the use of imagery for the interpretation of landforms have been long recognized  and digital surface models (DSMs) are types of digital elevation models (DEMs) that replicate terrain and surface topography. These models have become a common source of geographical analysis and are routinely employed in a large number of disciplines with a geographical basis, including engineering, geomorphology, hydrology, landscape architecture, and archaeology. With the wider availability of high‐resolution topographic data derived from photogrammetric models and LiDAR , such as the 30 m/cell SRTM, ASTER GDEM and ALOS World 3D, are freely available and cover most of the Earth surface. Depending on the scale of the features being sought, such medium resolution elevation data has the potential to contain important multi‐scale topographic information that cannot be extracted using standard visualization techniques or current filtering methods. In this regard, the development of techniques able to fully exploit the still largely untapped topographic information in medium‐resolution DSMs for the detection of micro‐reliefs has enormous potential to contribute to geomorphological research and beyond.This paper presents a new algorithm, multi‐scale relief model (MSRM), which is able to extract micro‐topographic information at a variety of scales employing micro‐, meso‐ and large‐scale DSM/DTMs. The algorithm was originally developed to complement large‐area, seasonal, multi‐temporal, multi‐spectral remote sensing approaches that are being applied to the reconstruction of the prehistoric hydrographical network of the Sutlej‐Yamuna interfluve, in northwest India (see later). Our remote sensing‐based method has been able to map more than 8000 km of relict water courses  algorithm, which is a DSM‐based application of standard trend‐removal techniques that aimed to be applicable to the detection of micro‐topographies Hesse, . LRM is The generation of MSRMs consists of the development of several low pass filters of the original surface with different kernel sizes. The kernel diameters should ideally cover the sizes of all potential features to be detected. The results of consecutive low pass filters are subtracted between them, and each of the resulting relief models is summed. The summed model is then divided between the number of relief models in the following manner:r) of ix, for the first term of the summation, and (i + 1)x, for the second one; x is the exponent of a power function determining the scaling factor for the radius values, that is, the spacing of the kernel levels; i, is the lower bound of the summation index, i is also equivalent to the radii of the low pass filter in the first term of the summation when x = 1.Here LPr is the filtered surface with a low pass kernel radius , which corresponds to the original DSM. A value of zero for i, will extract the maximum of topographical information available in the DSM, but in cases where the minimum feature size to be detected is known, the following rule should be adopted in order to filter out unwanted features visible at the finest resolution and reduce the use of system resources:fmin is the minimum size of any feature to be detected, rr is the raster resolution and x the scaling factor employed. This equation can be divided in three components:r in pixels necessary to cover the minimum feature size. If kernel size in pixels equals 2r + rr and the minimum kernel size in pixels to cover fmin equals fmin/rr, then the minimum kernel radius r equals (fmin − rr)/2rr. The resulting value equals the radius of the initial filtered surface, that is, the first term of the MSRM summation during its first iteration, if the exponential scaling factor x equals one. However, in order to allow scaling factors larger than one in the MSRM formula, the calculation of i requires the use of:A calculation of the kernel radius x aimed to compensate the scaling factor x. The first term of the summation in the MSRM formula, LPr (ix), includes the scaling factor in the calculation of the filter radius, which is necessary for the development of non‐linear spacing of the filtered surfaces. However, this initial filtered surface has to use a kernel size equal or smaller than fmin. The use of this exponent ensures that the lower bound of the summation i compensates the scaling factor present in the first term of the MSRM summation.An exponent 1/fmin for the development of the low pass filter.The value resulting from the two previous operations is rounded to the lower integer (flooring operation indicated by the floor brackets in the formula earlier). This is to ensure that an integer number (since the radius is measured in pixels) will be employed with a smaller or equal kernel size than that of The kernel radius of the initial filtered surface n should apply a similar formula to that described earlier:fmax is the maximum size of any feature to be detected. Note that n value resulting from this rule is rounded to the upper integer (ceiling operation indicated by the ceiling brackets in the formula earlier). This is to ensure that a low pass filter with an equal or larger kernel size than that of fmax will be employed. The n value is related to the number of surfaces to be generated: n − i equals the number of relief models (resulting from LPr(ix) − LPr(i + 1)x) necessary for the generation of a MSRM covering both minimum and maximum feature sizes. Thus, n − 1 equals the upper bound of the summation. The subtraction of 1 to n is necessary as the second low pass radius of every term in the summation series adds 1 to the value of the first low pass radius and, therefore, the maximum kernel size is already included in the n − 1 relief model.Selecting an appropriate value for x is an important variable in this algorithm. It makes it possible to set the trade‐off between MSRM sensitivity and the range of feature sizes to be detected. The larger the scaling factor, the wider the range of feature sizes that will be included in the MSRM using an equal number of relief models. A higher scaling factor results in a lower number of surfaces employed for the calculation of the MSRM and, therefore, a significant reduction on the use of computing resources, which can be necessary given the large computing requirements of the MSRM algorithm. The number of relief models included in the operation and the range of values of the resulting MSRM keep an inverse proportional relation. In this regard, the use of a non‐linear spacing of the kernels levels (in which x > 1) provides a larger range of MSRM values than that of a linear spacing (x = 1), which results in an increase of the contrast of the image and slightly improves its visibility. However, a larger scaling factor (i.e. a larger spacing between kernel levels) would also imply a larger topographic difference between relief models, which could result in a loss of sensitivity of the MSRM to intermediate sized features. A more sensitive MSRM can be produced by keeping the increase of filter radii in consecutive filtered surfaces to a minimum: a scaling factor of 1 will produce an arithmetic (linear) scaling of the low pass filters’ radii. This would be convenient when there are no large differences between the sizes of features to be detected or their sizes are likely to be similar. Of course, the use of a linear scaling factor to cover a large range of feature sizes will imply a higher number of relief models and, consequently, a reduction in the range of values in the MSRM and a much higher use of computational resources.The scaling factor When interpreting an MSRM, three characteristics of the resulting DEM need to be kept in mind: (1) MSRM does not provide a natural visualization – although the data represented in MSRM are based on real topography and they keep a proportional relationship with it, MSRM values are not a direct representation of relief or topographic differences, but an artificial way to visualize variable differences in height over large areas; (2) MSRM creates a topographic edge effect around features, which is not based on real topographic data, but is an artefact of the subtraction of filtered surfaces; and (3) it creates an edge effect surrounding the AOI, which equals the kernel size of the filter. This can easily be avoided by increasing the AOI accordingly, which should not constitute a problem when a global DSM is employed.Another factor to take into account is the difference in elevation present in the AOI. When mountain and plain areas are analysed in conjunction the difference in the MSRM values in mountain areas can render the much smaller value range in the plain almost invisible when the data are displayed using a colour scale. The use of minimum and maximum values in accordance with the geomorphological character of each subzone is recommended in such cases. Given the use of multiple kernels sizes, MSRM tends to filter larger areas than other relief visualization tools. For this reason, the application of MSRM to mountainous or rugged terrain does not produce good results for the visualization of micro‐relief.In order to improve its visualization, MSRM can be combined with digital illumination relief enhancing techniques, such as shaded relief models. These can help with the interpretation of the MSRM model by providing a more natural  visualization. However, before employing the MSRM surface to generate shaded relief models, it is necessary to transform the raster values. MSRM rasters include both positive (elevations) and negative values (depressions). Given the inability of shaded relief model algorithms implemented in most GIS software to deal with negative values, a raster with only positive values will need to be employed to generate the shaded relief map. This can be achieved easily by normalizing the MSRM raster values  using standard raster calculator tools. The original un‐normalized MSRM surface can be colour scaled by standard deviations and made slightly transparent so that it can be combined with a shaded relief model of the normalized MSRM.In Figure The visualization methods selected in Figure The MSRM image Figure G includeNon‐kernel size‐dependent visualization techniques clearly highlight modern structures, but while Slope Gradient Figure I does noTwoRains is investigating the interplay and dynamics of winter and summer rainfall systems, the nature of human adaptation to the ecological conditions created by those systems, and the influence of climate change and water availability on the resilience of South Asia's Indus Civilization. A critical component of this overarching project is the reconstruction of the palaeoriver network in the study area of northwest India, the so‐called Sutlej‐Yamuna interfluve, which was a core area for this ancient civilization. The Sutlej‐Yamuna interfluve is a remarkably flat plain with a height difference of c. 100 m in over 300 km that is formed by quaternary alluvial deposits originating from the Himalayas . Lastly, GEE allows users to run scripts through Google's cloud parallel computing infrastructure. MSRM can be extremely demanding on system computing resources. In the case of our research, this was particularly pressing as: (1) the study area extends over more than 80 000 km2; (2) the relatively high resolution of the TanDEM‐X DSM, which amounts to more than 2.5 GB of disk space; and (3) the large variability in size of the features of interest (from 500 m to more than 5000 m in length), which multiplies the number of surfaces to be generated.Google Earth Engine© (GEE)  is applied to both SRTM90  have resulted in the inclusion of many features of no geomorphological interest, the most visible of those are probably the large water channels criss‐crossing the area. In Figures fmin value above the size of these stripes and water channels can filter these out and increase the visibility of palaeorivers and other geomorphological features when using SRTM data , but as an important complement to these methods. MSRM, however, provides the majority of the data on palaeoriver courses in the study area and has enabled us to complete the ancient palaeoriver network derived from multi‐spectral imagery analysis  aerial and satellite imagery and providing images, which are directly related to the object of study: the topography and shape of the feature of interest. The application of MSRM can also be beneficial to all other research fields aiming to interpret small terrain differences, such as engineering, landscape architecture, archaeology and so on. MSRM can also be useful in evaluating the quality of topographic data in particular regarding the identification of stripes caused during the data acquisition process. A judicious selection of MSRM parameters can provide a simple way of masking these elements, improving the DSM capacity to reflect topographic change. MSRM's capacity to map features of constant relative height can also be used to identify and extract these for mapping purposes or to be implemented as part of other analyses. We provide the code employed to implement the algorithm in GEE as Supporting Information with the hope that other researchers will be able to use it in accordance with their own interests, evaluate it and improve it. In this regard, the use of GEE offers a way to make this computing‐intensive algorithm accessible to researchers lacking access to high performance computing resources, GIS software and or programming/GIS skills.Data S1. Supporting Information.Click here for additional data file.


Sabellaria spinulosa for their status as biogenic reef. Data were gathered within the North Norfolk Sandbanks and Saturn Reef candidate Special Area of Conservation/Site of Community Importance in the southern North Sea. Six study areas were identified as potential locations of biogenic reef using previously acquired data, and these were targeted for further investigation using a combination of high resolution multibeam echosounder and sidescan sonar. Where potential S. spinulosa was identified from the acoustic data, a drop‐down camera system was employed for visual verification. Areas of known and potential S. spinulosa reef were mapped successfully at two of the six study areas, although future approaches should take careful consideration of the seabed morphology and predominant habitat backdrop to successfully interpret such data. Camera tows from S. spinulosa reef areas were broken up into 5‐s segments, with each segment scored for (a) average tube elevation; (b) average percentage cover; and (c) for the presence or absence of S. spinulosa. These metrics were utilized to create summary statistics, including a value of patchiness derived from presence/absence data, that is recommended for application as part of future monitoring programs. The application of this methodology could benefit wider assessments of similar threated or declining habitats such as intertidal Mytilus edulis beds on mixed and sandy sediments, Maerl beds, Modioulus modiolus beds, Ostrea edulis beds, and Zostera beds where patchiness may also be considered of environmental importance.Standardized and repeatable data acquisition and analyses are required to enable the mapping and condition monitoring of reefs within Marine Protected Areas (MPAs). Changes in habitat condition must be reliably identified and reported to best support evidence‐based management. Biogenic reefs in temperate waters, that is, hard matter created by living organisms and raised above the seabed, provide food and shelter for many plant and animal species. This article explores the feasibility of habitat mapping, using remote sensing datasets, as well as metrics for repeatable and suitable assessment of areas of  These survey techniques have recently been explored by Limpenny et al.  were considered to be a useful complimentary approach, it was found to be less reliable at identifying the spatial extent of biogenic reef structures.Methods to determine the presence, extent, and condition of y et al. . In partS. spinulosa reef at the Thanet offshore windfarm site. This study presented a multi‐year mapping approach to assess potential impacts associated with an offshore infrastructure development. The assessment utilized sidescan sonar, along with MBES bathymetry and backscatter and ground‐truthing techniques, to explore temporal variability in reef distribution and spatial extent. The authors found a positive correlation between the windfarm development and an increase in reef extent. However, limitations such as the variability in personnel available to produce the habitat maps, on which the estimates of reef extent were based are noted. This limitation results in reduced confidence in the mapping outputs which had indicated an apparent temporal change in habitat extent. These outcomes were supported by the studies of both Coggan, Mitchell, White, and Golding  within a defined area Gubbay, . Video tS. spinulosa tube density, % cover and fauna from 3 to 5 replicate images per habitat, whilst Sheehan et al.  in the marine environment.Consistent and repeatable methods of data acquisition, analysis, and assessment are essential for effective monitoring and management of biogenic reef structures. To that end, we use seabed survey data collected in the southern North Sea in 2013 to (a) critically evaluate data acquisition techniques for informing reef assessment , and their limitations; and (b) to propose further metrics and repeatable methods for reef assessment. While this work specifically considers 22.1S. spinulosa reef, the cSAC/SCI encompasses a series of ten sandbank features , and associated fragmented smaller banks, which collectively represent the most extensive example of the offshore linear ridge sandbank feature in UK waters  and Saturn Reef (SR) candidate Special Area of Conservation (cSAC)/Site of Community Interest (SCI) in the southern North Sea JNCC, , extendiS. spinulosa reef) to favorable condition such that the natural environmental quality, natural environmental processes, and extent are maintained and that the physical structure, diversity, community structure, and typical species representative of the Annex I habitats are restored  of near continuous (90% coverage), and high elevation (>10 cm high) reef. Areas of patchy reef (representing <10–50% coverage) were also observed which contained various shaped holes or comprised elongated strips, raised above surrounding seabed. Surrounding sediments included both tube debris and areas where S. spinulosa tubes were absent . Damage to the physical structure of the reef, which may have been the result of bottom trawling, was also observed, particularly in the south western part of the study area.In 2003, the Saturn Reef S. spinulosa in this area in 2003 does indicate favorable conditions for continued reef formation.In 2006, during a subsequent survey of the Saturn Reef area . Simultaneous collection of multibeam echosounder data (Kongsberg EM2040 system operated at 200 kHz and deployed on the drop keel of the research vessel) was also collected, recognizing that full seafloor coverage would not be achieved with this technique at this line spacing within the water depths encountered.S. spinulosa reef signatures were targeted for the subsequent validation via the collection of video and stills data using a Kongsberg 14‐208, 5 megapixel, camera mounted in a rectangular drop frame. High‐power LED strip lights and a four‐point laser system with lasers set 17 cm apart  were also mounted to the frame. Video tows were a minimum of 10 min long. Still images were captured at regular 1‐min intervals and, in addition, opportunistically if specific features of interest were encountered.Following an on‐board review of acoustic data, areas identified as having potential 2.32.3.1A total of 152 videos were initially analyzed using Cefas video and stills processing protocolS. spinulosa reef was observed were analyzed further using a standardized method for determining reef patchiness developed by JNCC and Cefas  was assessed using the four‐point laser system mounted on the drop frame which provided an accurate and consistent scale for measurement.Measurements of percentage cover and elevation, as proposed by Gubbay , for eac2.3.2S. spinulosa reef to be clustered together rather than to grow uniformly and randomly everywhere.a value to represent the propensity of Here we define “true patchiness” as:1 0 0 1 1 1 0 0 1 1 0, the resultant patch sizes are calculated as 1, 3, and 2.Applying this definition, the size of each patch observed in a video tow can be calculated by creating a presence variable (defined to be: 0 if coverage = 0 and 1 if coverage > 0). A patch is defined as a continuous sequence of values of 1 which is ended by a value of 0. Therefore, if the data for a series of segments comprises the sequence:* and a series of segment values comprises the sequence shown below: 0 1 * 1 1 0 then the resultant patchiness score would indicate a single patch of 3.Missing values  are excluded, resulting in occasional gaps in the data. For example, where a missing value is The definition of patchiness is dependent on the duration of the segments and the mean patchiness statistic used, which for the study reported here is based on 5‐s segments. There may, therefore, be gaps in the reef observations within a segment. The shorter the segment used, the finer the measurement of patchiness because the likelihood of encountering gaps is reduced.The value of mean patchiness per video tow is determined by the number of segments within which reef is observed . If reef is observed in numerous neighboring segments, the mean patchiness will be higher than if there were fewer neighboring segments with reef present.po is the mean patch size observed and pr is the mean patch size if the presence of reef observations in the data string were random.To standardize patchiness measurements between reefs, the statistic provided below was calculated: pr was determined by randomizing the data 1,000 times, calculating the mean patch size each time and then calculating the mean of these 1,000 values. Values of K greater than 1 indicate patchiness.The value of K also allows a p‐value to be calculated to test the null hypothesis that the segments where by S. spinulosa reef was observed are random. The p‐value is calculated from the proportion of times that the mean patch size under randomization is greater than the observed value .Automatic segmentation and classification rely on data values being representative of habitat types to enable effective clustering or object creation. This was not possible using the sidescan sonar data collected here, where raster values varied with distances from the transducers. Alternatively, visual interpretation of sidescan sonar data allows expert judgment to be applied to the habitat delineation process, and as such was more appropriate than automated methods to mitigate the effect of varying raster values caused by the nadir and distance from transducers. The mapped spatial extent of 33.1Sabellaria reef representing the first habitat (95% of the video) with cobbles and pebbles on fine rippled sand representing the second habitat. Reef elevation and percentage cover were both assessed as high. In contrast, video tow A71 was segmented into 8 sections due to a high level of reef fragmentation. Reefiness was assessed as low‐medium and varied between segments.Analyses of video using the Cefas standard broad‐scale habitat methodology resulted in videos segmented into large areas where reef was observed and where reef was absent. The percentage cover and elevation of the reef observed was then estimated for each video segment where it was present. Fourteen videos were determined as crossing more than one habitat and were therefore split into segments (from 2 to 8). For example, video tow A68 was determined as having two distinct habitats (segments); Dense cobbles and pebbles with extensive S. spinulosa reef occurred in study area A . Station A69 showed the most patchiness, having a K value of 3.1.The 3.2S. spinulosa reef presence, varied across the study site. The potential boundaries were delineated for some of these signatures, but represented areas of known and potential S. spinulosa reef presence as was identifiable from the areas where sidescan sonar data were available, rather than exclusively delineating Annex I reef extent within the full extent of the NNSBs and SR cSAC/SCI. Within study area A, there was a demonstrably stronger signature associated with S. spinulosa reef presence than at any of the other surveyed areas within the SCI. This is potentially due to the relative reflectivity of the S. spinulosa reef in relation to the predominant, adjacent substrate types in study area A in contrast to other study areas .The acoustic signature identified from sidescan sonar, to be coincident with 3.3S. spinulosa reef was identified within study areas B, D, and F. As such results from these study areas are not further presented. Data from these locations is reported in Jenkins et al.  critically assessing survey techniques , and their limitations; and (b) development of further metrics and repeatable methods for reef assessment.Using seabed survey data, collected in the southern North Sea in 2013 to assess the presence, spatial extent and status of 4.1Drop frame video transects collected across areas of patchy biogenic reef can successfully be used to explore local variability in parameters from which the status of biogenic reef can be derived along with measures of patchiness. Splitting video tows into 5‐s segments for analysis of reef status, using the metrics elevation and percentage cover, gives a local assessment of reef status beyond that achieved from currently proposed methodologies , postprocessing should be applied to standardize for the discrepancies. The starting position for each tow should also be considered. For example, where a tow begins a long way from start of a reef area, it will not produce similar summary statistics to transects initiated closer to the reef. For that reason, it is proposed that video analysis, for determination of reef status and patchiness, begins where S. spinulosa reef patches are ephemeral. This being the case, there may be high levels of natural variability beyond any anthropogenic impacts, which may hinder our abilities to consistently, and reliably, map S. spinulosa reef boundaries. This, in turn, may affect future monitoring and management of these features. In the current absence of appropriate reference areas, we suggest that establishment of those regions with predicted high environmental suitability for S. spinulosa reef development may be best mapped at a very coarse scale to reflect S. spinulosa reef presence. Combining mapped patches of S. spinulosa reef extent to create larger polygons may mitigate for our poor understanding of patch validity and connectivity, as well as for uncertainty in establishing the presence from remote techniques such as sidescan sonar.As demonstrated by the changes in spatial extent observed over time at the SR location, there is the potential that Providing the application of this methodology is coupled with a survey design encompassing an appropriate level of stratification and replication we suggest this method will allow for exploration of potential habitat condition variability beyond that shown by reef extent alone. We therefore suggest that MPA site monitoring should harness this technique going forward.Laminaria forests by Walls et al. (S. spinulosa reef diversity with this in mind may lead to further insight into the function and conservational importance that this biogenic reef habitat is providing.Further work should explore how patchiness of reef areas does or, potentially, does not impact on the functional diversity of the associated fauna and seek to understand the trade‐offs between larger more established reef over lower lying, patchier, reef complexes in the context of a larger, landscape scale perspective. In terrestrial climes, the benefits of coppicing forested areas are well‐known to provide multiple habitat niches that support wider diversity beyond that are associated with an apex community. Marine systems are also known to demonstrate wider benefits from increased habitat diversity, as noted in s et al. . Explori5S. spinulosa reef extent was achievable for some areas within the study site, but not for all. The lack of a consistent, and replicable, acoustic signatures synonymous with reef presence across the study site made mapping reef extent at the site scale difficult. Data limitations associated with 100% coverage, rather than the recommended 200% sidescan coverage, as well as poor weather conditions at later stages of the survey further limited data interpretation. Where a potential S. spinulosa reef signature was identified, using acoustic techniques, it was possible to combine this with the methodology outlined above to assess reef status along a video transect. The results could then be used to calculate summary statistics, which could be compared between locations and over time. Where applicable, this methodology can therefore be used for the future assessment and monitoring of reef feature at NNSBs and SR SCI. Furthermore, although this was not a primary aim of the study presented here, the novel method for calculating tow patchiness developed in support of the present study delivers a measure of the potential consolidation of a given reef area and may be used to determine the direction of change in reef structure and status over time. The application of this methodology could benefit wider assessments of similar threated or declining habitats such as intertidal Mytilus edulis beds on mixed and sandy sediments, Maerl beds, Modiolus modiolus beds, Ostrea edulis beds, Zostera beds, and deep sea sponge aggregations.Delineating None declared.All authors were instrumental in conceiving the ideas and designed methodology; C. Jenkins and J. O'Connor collected the data; C. Jenkins, J. Eggleton, and J. Barry analyzed the data; C. Jenkins led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.http://portal.oceannet.org/portal/start.php.All data within this manuscript have been acquired using public funding and are therefore publically available for request and download. Requests can be made to the authors or downloaded through the MEDIN data portal;


Understanding how life began is one of the most fascinating problems to solve. By approaching this enigma from a chemistry perspective, the goal is to define what series of chemical reactions could lead to the synthesis of nucleotides, amino acids, lipids, and other cellular components from simple feedstocks under prebiotically plausible conditions. It is well established that evolution of life involved RNA which plays central roles in both inheritance and catalysis. In this review, we present historically important and recently published articles aimed at understanding the emergence of RNA nucleosides and nucleotides on the early Earth. How did life begin? Where did the essential components to life—nucleic acids, proteins, and lipids—come from? To answer these fundamental questions, efforts have been made to understand the prebiotic synthesis of these biomolecules through chemical processes . NucleicAmong the biomolecules, it is well established that RNA may have played a central role in the early evolution of life. Indeed, RNA can not only act as an enzyme and perform catalytic reactions, but it can also store and transfer genetic information ,7,8,9,10First efforts to understand the prebiotic synthesis of ribonucleotides, the building blocks of RNA, have been based on the hypothesis that they should be formed from three distinct entities: a nucleobase , a ribose sugar, and a phosphate, which have been formed separately and combined A 13]..13].Prebiotic chemists suggested that sugar formation relied on the synthesis of formose, discovered by Butlerow in 1861 . This reSignificant efforts have been made to search efficient plausible prebiotic routes to favor sugar synthesis . Indeed,Moreover, ribose is unstable under the alkaline conditions required for the formose reaction . Borate Alternative routes leading to the formation of ribose are possible. Indeed, sugar formation could be coming from hydrogen cyanide irradiated by ultraviolet light in the presence of copper cyanide complexes . MoreoveIn addition, aldoses including ribose pentose sugars have been found in interstellar ice analogs  after their irradiation by ultraviolet light . These rEven if some progress has been made to understand the ribose formation under prebiotic conditions, each suggested route presents obstacles, limiting ribose yield and purity necessary to form nucleotides. A selective pathway has yet to be elucidated.Starting with the prebiotic purine formation (adenine and guanine), it has been shown that the nucleobase adenine could be formed by mixing hydrogen cyanide and ammonia in solution ,32. IndeOther hypotheses on adenine and purine accumulation on Earth have been discussed. Miyakama et al. suggest that purines have been formed in the atmosphere in the absence of hydrogen cyanide . They reMost of the work on the prebiotic synthesis of pyrimidines (cytosine and uracil) suggest reactions between cyanoacetylene or its hydrolysis product, cyanoacetaldehyde, and cyanate ions, cyanogen, or urea ,40,41,42The synthesis of nucleosides from sugars and nucleobases in prebiotic conditions is one of the major difficulties encountered, when attempting to resolve the early formation of nucleosides. The reaction between the ribose and nucleobase is thermodynamically unfavorable, leading to poor yields and little selectivity . Only a N-formamidopyrimidines could be used to generate purine nucleosides with absolute nucleobase regioselectivity [The formation of adenosine (4% yield) has been observed from the condensation of adenine with ribose in the presence of inorganic salts, providing complex mixtures of purine ribosides . Howeverectivity . Recentlectivity .In addition, adenine nucleoside phosphate has been formed from the direct coupling reaction of cyclic carbohydrate phosphate with the free nucleobase. The reaction is stereoselective and regioselective, giving the N-9 nucleotide as a major product . It is uEven though important efforts are made to determine the possible prebiotic conditions for the nucleoside formation from sugars and nucleobases, this strategy leads to significant problems and an alternative approach has been suggested to form nucleosides .The alternative approach for the synthesis of nucleosides is based on the hypothesis that nucleobases and sugars emerged from a common precursor. This would mean that both the sugar and the nucleobase are formed during the same process B. Many years ago, Orgel et al. reported the synthesis of α-cytidine from ribose, cyanamide, and cyanoacetylene in aqueous solution. They showed that the replacement of the ribose by a ribose-5-phosphate allows the obtention of α-cytidine-5′-phosphate, which can be photoanomerized into β-cytidine-5′-phosphate required for RNA synthesis . HoweverRemarkably, one nucleotide, β-cytidine-2′-3′-cyclic phosphate, showed great stability under irradiation; only partial conversion to the corresponding uridine was observed . IrradiaInspired by previously reported work of Navigary et al., who demonstrated that β-cytidine-2′-3′-cyclic phosphate can be obtained from the 3′-phosphate of the anhydronucleoside arabinose  , SutherlThe latter intermediate can be formed from the aminooxazoline arabinose and cyanoacetylene . The preAminooxazoline arabinose could be formed by the reaction of 2-aminooxazole and glyceraldehyde in excellent yields . This reSynthesis of 2-aminooxazole by reaction of glycolaldehyde with cyanamide was achieved . Once agAnother route to obtain β-cytidine-2′-3′-cyclic phosphate by photoanomerization from ribose aminooxazoline instead of arabinose aminooxazoline was investigated . In factThese two routes, exploring both ribose and arabinose variants, are key working examples for understanding pyrimidine formation under prebiotic conditions.9-glycolysation with absolute regiospecificity [Possible routes for purine formation still have to be investigated. However, preliminary studies provide potential leads. Indeed, a purine precursor has been successfully assembled from 4-amino-5-cyanoimidazole or 5-aminoimidazole-4-carboxamide, 2-aminooxazole, and glyceraldehyde A. This Mcificity . This roAnother prebiotically plausible reaction for the synthesis of both pyrimidine and purine nucleotides from an oxazoline scaffold has been reported B. An oxaWhile promising, these proposed synthetic pathways demand further investigation.The role of RNA in the origin of life is well established, and understanding how RNA emerged on the early Earth is one of the first steps in understanding the origins of life. Despite great efforts and impressive advancements in the study of nucleoside and nucleotide abiogenesis, further investigation is necessary to explain the gaps in our understanding of the origin of RNA.The comprehension of nucleotide formation under prebiotic conditions is only one of the steps to understand the complex production of RNA as nucleotides must be oligomerized to generate RNA. Assuming that RNA must be 5′-3′-linked, regioselectivity issues have to be overcome. Polymerization of activated nucleotides has been studied intensely as a model for non-enzymatic oligomerization of RNA and has been considered a plausible scenario for the emergence of RNA during the origin of life ,73,74,75Other than RNA, cells require various chemical subsystems, including peptides for functional support and lipids for compartmentalization. The assumption that one subsystem came first and then generated the others is debated ,79,80. C


Lophelia pertusa as well as bioerosion rates of dead coral framework were assessed in different environmental settings in Norwegian cold-water coral reefs in a 1-year in situ experiment. Net growth (in weight gain and linear extension) of live L. pertusa was in the lower range of previous estimates and did not significantly differ between inshore (fjord) and offshore (open shelf) habitats. However, slightly higher net growth rates were obtained inshore. Bioerosion rates were significantly higher on-reef in the fjord compared to off-reef deployments in- and offshore. Besides, on-reef coral fragments yielded a broader range of individual growth and bioerosion rates, indicating higher turnover in live reef structures than off-reef with regard to accretion–bioerosion processes. Moreover, if the higher variation in growth rates represents a greater variance in (genetic) adaptations to natural environmental variability in the fjord, inshore reefs could possibly benefit under future ocean change compared to offshore reefs. Although not significantly different due to high variances between replicates, growth rates of orange branches were consistently higher at all sites, while mortality was statistically significantly lower, potentially indicating higher stress-resistance than the less pigmented white phenotype. Comparing the here measured rates of net accretion of live corals (regardless of colour morphotype) with net erosion of dead coral framework gives a first estimate of the dimensions of both processes in natural cold-water coral habitats, indicating that calcium carbonate loss through bioerosion amounts to one fifth to one sixth of the production rates by coral calcification . With regard to likely accelerating bioerosion and diminishing growth rates of corals under ocean acidification, the balance of reef accretion and degradation may be shifted towards higher biogenic dissolution in the future.Coral reef resilience depends on the balance between carbonate precipitation, leading to reef growth, and carbonate degradation, for example, through bioerosion. Changes in environmental conditions are likely to affect the two processes differently, thereby shifting the balance between reef growth and degradation. In cold-water corals estimates of accretion-erosion processes in their natural habitat are scarce and solely live coral growth rates were studied with regard to future environmental changes in the laboratory so far, limiting our ability to assess the potential of cold-water coral reef ecosystems to cope with environmental changes. In the present study, growth rates of the two predominant colour morphotypes of live  L. pertusa is widely distributed and builds extended reefs in almost all oceans except for the polar regions . In the Norwegian  sources . In the e corals . Pigmente corals , as the e corals . Moreovee corals . In a reL. pertusa grows slowly, but has a long colony lifespan  was documented  emissions results in lowered seawater pH and decreasing carbonate ion concentrations in the oceans and consequently in a diminished calcium carbonate (CaCO3) saturation state  and linear extension rates (determined via staining). In addition, we compare two different cold-water coral reef locations  in mid-Norway, allowing for a better representation of the natural variability of environmentally differing reef settings. Moreover, live corals of white and orange L. pertusa were compared at each location in order to determine physiological differences between different colourmorphs in a reef with regard to their growth performance. Last but not least, normalisation of physiological rates will be expanded by determining volume and area of each coral fragment after recovery in addition to dry weight and biomass. This provides us with a variety of normalisation parameters and conversion factors between them, which can be used in future cold-water coral growth studies for more easily comparability.Thus, the aim of this study was to simultaneously assess in situ growth and bioerosion rates of Lophelia reef sites with different environmental characteristics were chosen for collection and re-deployment of live corals and dead erect coral framework. The approximately 13 km long and 700 m wide Sula Reef Complex on the Sula Ridge off the coast of Sør-Trøndelag is the second largest known Lophelia reef on the Norwegian Shelf  under permit number 12/17918. Corals from the Leksa Reef were collected on 29th and 30th June 2013 at 63°36.46′N and 9°22.76′E and 157 m water depth (white specimens) and 63°36.43′N and 9°22.45′E and 152 m (orange specimens) during research cruise POS455 with RV POSEIDON , was sampled from the reef basis. All samples were collected by means of the manned submersible JAGO with its sensitive claw for non-destructive sampling  with a purpose built free hanging weighing gondola to enable weighing on board the vessel and to reduce transmission of vibrations onto the balance. Weighing on board was performed at very calm sea, but was nevertheless unsteady. Therefore, the average of 10 consecutive values was taken for each fragment to improve precision. Shortly before deployment of the fragmented and stained corals, one white and one orange coral fragment were attached with cable ties inside a ‘coral cage’ . Four such coral cages were prepared for each study site.Live coral colonies as well as dead erect coral framework were fragmented into fist-sized pieces soon after collection. Afterwards, live corals were stained with Alizarin Red S. For this purpose, live coral branches were placed in separate staining tanks (2 × 30 L plastic containers) mounted within the holding tanks for temperature equilibration. The dye, pre-dissolved in ethanol, was slowly added until a concentration of 10–15 mg Lr corals . The dea® autoclave baskets). For each location six ‘bioerosion cages’ filled with dead coral framework material were weighed under water using the buoyant weighing technique , the cluster and the coral cages were placed into a living reef area at 180 m water depth . At the After 1 year, in July/August 2014, all locations were revisited with RV POSEIDON (POS473) and all coral cages and bioerosion clusters were recollected by means of JAGO and brought back aboard (permit number 14/1781 of Directorate of Fisheries for cruise POS473), where they were immediately transferred to large holding tanks. Except for one coral cage in the Leksa off-reef location, all coral cages and clusters could be retrieved. Soon after recovery, the coral fragments and bioerosion cages were weighed on board following the same protocol and using the same equipment as outlined above. Afterwards, all samples were dried at 60 °C on board and packed cushioned for later laboratory analyses.After the cruise, all samples were dried at 70 °C for several days until constant weight was reached. Dry weights of empty baskets, cable ties, and corals were separately measured. Calcifying epibionts grown on the dead erect coral framework as well as on the live coral fragments (carbonate accretion) over the year of exposure were removed with a scalpel and weighed separately after drying to constant weight. Then, all samples were scanned via computed tomography (CT) for volume and surface area analysis (see detailed description of the methods below), before linear extension rates of the ‘live’ coral fragments were determined by measuring the distance from the Alizarin Red S band of each corallite to the rim of the calyx using a digital calliper. As growth of the calices is sometimes more pronounced on one side of the calyx than on the other, this measurement was done on two opposing sides, with the lowest and the highest distance between stain bands and rim of each corallite. In addition, the numbers of newly grown (completely unstained) corallites, as well as the number of died polyps  of the ‘live’ coral fragments were counted. When being deployed in 2013 it was made sure that only intact corallites remained on the fragments, while all empty corallites were removed. Counted newly grown corallites and dead polyps were compared to the total number of corallites of each branch to assess budding rate as well as mortality as a percentage over the course of the experiment. Finally, all samples were dried again to constant weight and combusted at 500 °C for 5 h for differentiation of organic vs. inorganic content.sw = density of the seawater, BW = buoyant weight of the coral fragments without tissue, and DW = the dry weight of the fragments without tissue.All fragments were dried to constant weight at 68 °C before tissue residuals were removed with chlorine bleach according to the method described by x- and y-direction and 0.5 mm resolution in z-direction (0.3 mm reconstruction unit). Images were reconstructed using Toshiba’s patented helical cone beam reconstruction technique and are provided in DICOM-format. The data were processed with the ZIB edition of the Amira software  . The segmentation result was evaluated and the coral cage was removed from the computation of each sample with the Segmentation Editor. Afterwards, the Generate-Surface module was used to compute a surface model of the coral specimens. Finally, the volume and the area of the specimens were determined using the Surface Area Volume module.Computed tomography scans of all dried samples  for volume and surface area analyses were carried out with a Toshiba Aquilion 64 computer tomograph at the hospital Klinikum Bremen-Mitte with a voltage of the X-ray source of 120 kV and a current of 600 mA. The resulting CT image stacks have a resolution of 0.35 mm in Germany) . With Am−1) as parameterisation predominantly used in experimental studies with live corals applying the buoyant weighing technique. In addition, rates were normalised to weight change in grams per square metre coral surface (gained from the CT measurements) per year (g m2 yr−1), which represents the most common unit in bioerosion studies. Data are depicted as mean ± standard deviation (SD). Statistical analyses were performed using SigmaPlot©  and MS Excel Redmond, WA, USA. For statistically comparing the results between the three locations of white as well as orange coral colourmorphs, One-way analysis of variance tests (ANOVAs) were carried out with n = 4 replicates per location, except for the Leksa off-reef location at which one basket and therewith one orange and one white replicate were missing. In case of statistical differences, a post-hoc test for pairwise multiple comparisons following the Holm-Sidak method was carried out to distinguish differences among groups/locations. Whenever data were pooled to increase the sample size and statistical power, this was done upon confirmation that there were no significant differences in the statistical tests among pooled groups. For direct comparisons of white and orange corals or only two locations, t-tests were performed. In order to obtain more accurate and reliable means for conversion factor calculations, outlier tests were carried out in MS Excel (Excel QUARTILE and OR functions).Growth and bioerosion rates were calculated according to descriptions in p ≤ 0.001; One-way ANOVAs), with significantly less bulky coral fragments deployed in Sula compared to both Leksa locations. While Leksa corals had 119 ± 44 polyps/corallites per fragment on average, the Sula corals had only 35 ± 11 polyps per branch (R2 = 0.7) with a slightly better correlation of surface area with total polyp count than volume. Coral surface area, volume, corallite number, and skeletal density of live and dead coral fragments  were gatr branch . This is−3. Orange corals had a slightly lower skeletal density (<1%) by trend than white corals. Note that the skeletal density of the orange coral fragments from the Sula Reef was considerably lower than the densities of all other fragments and was identified as outliers. The outlier values were therefore omitted from the average skeletal density of live corals. Bioeroded skeleton material had slightly higher densities than live corals averaging 2.758 ± 0.031 g cm-3. Both, white vs. orange live as well as bioeroded vs. live coral skeletons were not significantly different in densities (t-tests). For the calculation of growth and bioerosion rates the specific density means of live or bioeroded skeleton material was used.Mean skeletal density of all live corals was 2.734 ± 0.043 g cmp = 0.002; Mann–Whitney Rank Sum Test). While the orange coral fragments had on average 8 ± 9% dead polyps per replicate, the white corals had 33 ± 23%   vs. 1.96 ± 0.84 mm yr−1; n = 10 (white); p = 0.03; t-test; 2.34 (n = 14) vs. 1.31 mm yr−1 (n = 4); R2 = 0.83).The overall mean extension rate of all stained corallites of the living coral branches  from all sites was 2.12 ± 0.86 mm yr n = 18; . Example t-test) . Neverthp = 0.043; t-test) than all stained polyps of the branches . Mean linear extension of newly grown polyps/corallites over all locations and specimens was 2.84 ± 1.04 mm yr−1 compared to 2.11 ± 0.86 mm yr−1 total extension on average . Comparing new vs. total extensions  of the different groups shows that the greatest effect of new growth took place in the Leksa on-reef location. On-reef, 60–75% higher linear extension rates of new corallites of white and orange specimens were gained, while in the Leksa off-reef location it was less than half as much (20–33%). In Sula, growth rates of newly grown corallites were not different or even lower than total stained corallites, although the percentage of newly grown corallites per branch was similar to the percentage of newly grown polyps on the Leksa branches. As newly grown corallites make up for almost half of all stained corallites the pattern of the slight differences between location and/or colourmorph is similar to average extensions of all (old and young) corallites.The amount of newly grown corallites that developed after staining was similar in all locations and averaged 47.4 ± 12.5%. New corallites alone had significantly higher extension rates  and 0.0138 ± 0.0137% d−1 corresponding to 67.42 ± 61.51 g m−2 yr−1 (n = 11), respectively. Calcification rates did not differ statistically significantly between white and orange corals , nor between the different sites (One-way ANOVAs). However, corals from Sula Reef generally showed lower calcification rates with only half as much CaCO3 precipitation on average (48%) as the Leksa off-reef corals when comparing all live corals (white and orange pooled) of the single sites . Although growth rates in the top reef replicates in Leksa on-reef were even higher on average than at the Leksa off-reef site, the top reef corals were not significantly different from the Sula corals. A similar comparison of all inshore vs. offshore replicates as for the linear extension rates  vs. 0.0074% d−1(n = 8)). Differences between the different locations and between the two colourmorphs of L. pertusa are shown in Overall net carbonate production rate of all observed live coral fragments based on buoyant weight measurements (SD of the 10 consecutive measurements of each fragment of the Leksa weighing session = 0.052 g and Sula weighing session = 0.082 g) was 0.0122 ± 0.0103% d n = 22; ; Table 3 compare  revealed−1, corresponding to −12.37 ± 9.40 g m−2 coral surface yr−1 , accounting for about one-fourth (23.7%) of the growth in percent per day of the living corals  at the on-reef location.R2 ranging from 0.616 to 0.999).We used various methods for growth rate measurements as well as for the normalisation of the different variables, and are thereby able to provide conversion factors for coral growth in size and weight and for the standardisation of these data . Since tL. pertusa of different morphological structure and colour were 0.011% d−1 on average over three different reef sites. Bioerosion rates of dead erect coral framework averaged −0.002% d−1 (−12.37 g m−2 yr−1) of the same reef habitats (disregarding the relatively high accretion rates of epibionts). Calculating a net production rate of accretion and bioerosion of live and dead coral fragments of this experiment is not legitimate, as here two different sample sizes are compared. For this reason we refrained from adding up calcification rates of living corals and erosion rates of dead coral framework, since the obtained rates do not reflect the actual reef carbonate budget, which would demand the determination of the proportions of live and exposed dead coral framework in a given reef. However, considering that bioerosion rates amounted to one fifth to one sixth of calcium carbonate loss compared with the production rates through coral calcification in this direct comparison, it can be assumed that the carbonate balance of accretion and bioerosion in cold-water coral reefs could be shifted towards higher biogenic dissolution in the future under ocean change. Supposing that the relative proportions in this one-to-one relationship are roughly correct and reef base degradation increases with acidification  and 1.00–4.05 mm yr−1 in average linear extension. These values are in the lower range of literature values both in terms of buoyant weight measurements and length growth assessments  on average  and with different perspectives or intentions (i.e. determining the highest possible growth rate vs. average growth rate). Growth estimates from indirect analyses vary widely with extension rates from approximately 3.2–34.7 mm yr−1 . The bro−1 .−1 in an individual colony , while lower extension rates of 2.44 mm yr−1 on average were measured of corals placed in a usually non-inhabited area ~0.25 km away from the coral area after staining. The off-reef corals in the present study were placed less than 100 m away from the reefs and no difference was observed in average extension between the on- and off-reef locations in Nord-Leksa. However, the offshore corals from Sula Reef showed lower values of linear extension than the fjord corals with 44% higher extension rates inshore compared to offshore over the year. This finding is supported by the measured growth rates in weight gain, which even showed twice as high calcification rates in the Leksa locations, although not significant. The observed discrepancy in the differences among sites between the methods is most likely attributable to the allometric growth of Lophelia, which is reflected more pronounced in the assessment of linear extensions.Directly obtained in situ average linear extensions of all stained corallites on several branches in the present study were in the same order of magnitude, but slightly lower than extension rates reported for echnique . In theiLophelia growth forms and presumably also rates vary largely with environmental factors based on growth rhythmicity observations and stable isotope data. Hence, growth rates might vary between in- and offshore reef habitats due to physicochemical environmental factors such as current velocity, oxygen saturation, light, trace elements, and food availability although the measured physical seawater parameters and carbonate chemistry parameters did not vary considerably between locations  at each location are pooled, results show a statistically significant difference between both ‘off-reef’ sites in the reefs near Nord-Leksa and Sula. The higher growth rates at the inshore off-reef site would also apply to the on-reef growth in Leksa if the rates were not so variable among all specimens, since these corals exhibited even higher mean values. Thus, inshore growth rates were by trend higher than in the Sula Reef. The 44–50%  lower growth rates in Sula on average may be explained by different deployment conditions, since these coral cages were placed on relatively soft sediment ground approximately 50–100 m apart from the nearest reef structures, while the off-reef corals at Nord-Leksa were situated on rather hard bottom, which is attributable to the different habitat situations at the in- and offshore reef sites. Thus, lower growth rates of the offshore corals might be a result of an inconvenient place the coral baskets were deployed at, which might have been too far away from the reef, potentially too unsheltered with regard to sedimentation and other environmental disturbances. However, looking at the fragments in the aftermath, we observed more pronounced epibiont growth and sedimentation on the Leksa corals than at Sula and also the polyp mortality was considerably higher in both fjord deployment sites. In order to validate the trend of lower growth at the outer shelf compared to the fjord conditions, this experiment should be repeated in Sula (or comparable offshore reef) with corals being deployed closer to or directly into the reef using bigger fragments of live corals with comparable volume like the fjord corals in order to enhance comparability to the inshore experimental conditions.L. pertusa of highly pigmented orange corals vs. the typical white appearance were assessed. Growth rates expressed in weight gain showed a greater range in orange corals compared to the white colourmorph. While white corals had maximum growth rates of 0.019% d−1, the orange corals’ growth rates reached up to 0.049% d−1, that is, more than twice as high in maximum growth rates. However, the average growth rate of orange corals was only ~30% higher and not significantly different from the white corals. While this was also true in terms of average extension rates when taking means over all locations (18% increased length growth in orange compared to white specimens), the overall range of average extensions was relatively similar in white and orange corals. So far, only few studies took a closer look into physiological differences between the colourmorphs of L. pertusa and the physiological advantages and potential costs of enhanced carotenoid concentrations in cold-water corals remain unknown and require further investigation. L. pertusa showed a dominance of highly productive Rhodobacteracea, which can exploit even small amounts of organic material as carbon sources and which may support the nutrition of L. pertusa in environments with moderate carbon supply . Although levels of sedimentation were expected to be highest in Sula, where the coral baskets were placed in relatively soft sediment, sediment particles as well as overgrowth by epibionts were more pronounced on Leksa specimens and less on Sula corals. ral days . MoreoveInstead, the higher mortality at the inshore sites may be related to stronger environmental fluctuations with regard to abiotic factors such as temperature, salinity, and currents, as the hydrodynamic conditions inshore are more variable than offshore. Moreover, elevated concentrations of nutrients or pollutants due to aquacultures in the Trondheimsfjord, for example, may be a possible explanation for higher polyp mortality inshore. Higher nutrient levels inshore must yet be verified.Orange specimens showed far lower mortality (less than a third) at all locations compared to white specimens. In addition to slightly higher growth rates (18–30%), this implies that another underlying mechanism than environmental differences or handling stress causes coral mortality to a different extent in the two colourmorphs.3 degradation by bioeroders is an essential parameter to consider with regard to the impacts of ocean change on coral reef ecosystems. In this context, the in situ bioerosion rates reported herein may serve as base-line estimation for carbonate budget modelling assessments of Norwegian cold-water coral reefs.Although bioerosion contributes globally to a greater extent to reef degradation in marine habitats than physical erosion or passive chemical dissolution, this process is often being neglected in studies investigating coral growth and ocean change . Since oLophelia skeletons  are filter feeders that profit from the enhanced current regime and higher food availability in the more exposed live zone of the reef. Bioerosion rates in the Sula Reef site and the Leksa off-reef location, in contrast, were not significantly different. However, this might be a result of the mixture of Sula and Leksa dead coral material in the Sula location with relatively young bioeroded fragments from Sula. With more advanced bioerosion stages from Sula and cage placements directly into the reef structures as in Nord-Leksa, bioerosion rates could perhaps be higher in the Sula Reef.A statistically highly significant difference was found between both off-reef placements and the Leksa on-reef location despite mixed bioeroded material in Sula, with the on-reef fragments exhibiting four times higher bioerosion rates. This may reflect a higher abundance and rate of colonisation of bioeroders within the live reef structures and/or it may mirror environmental conditions to be more favourable for bioeroders in that reef zone. The latter is supported by the fact that the majority of macroborers in Lophelia reef environment were those obtained via a settlement experiment carried out at the Säcken Reef in the Swedish Kosterfjord . Other methodological bias, including the removal of calcifying epibionts prior to and after the experiment, trapped air bubbles during buoyant weighing sessions, breakage during deployment or recovery, all potentially affect weighing results, but may also balance each other to some extent. With no possibility to accurately quantify these factors, we have to assume that the overall bias is reasonably low and the calculated in situ bioerosion rates are the closest achievable approximation of a conservative estimate for total cold-water coral community bioerosion rates.Previously, the only experimentally determined bioerosion rates from a terfjord , where pL. pertusa from the present study are considerably lower than rates determined from the analysis of coral samples or in situ settlement experiments of shallow-water tropical coral reefs around the globe (see −2 (planar substrate surface) yr−1, which means they are about two orders of magnitude higher  to directly assess natural growth rates of living corals and measured different parameters to describe the coral fragments physically . These different approaches allowed for computing conversion factors of the corresponding parameters to transform growth estimates based on buoyant weight measurements to linear extension rates and vice versa, for example, and to convert standardisation parameters such as dry weight, buoyant weight, volume, and surface area of L. pertusa from different environmental settings. In situ community bioerosion rates were significantly higher in the live reef structures compared to the two off-reef sites (in- and offshore), which is consistent with the greater presence of bioeroders within the reef. With regard to calcification of living corals, specimens from both inshore deployment sites at Nord-Leksa performed better than the ones from the offshore Sula Reef. Besides, inshore corals showed a broader range of net accretion and bioerosion rates between fragments than offshore, which might be attributable to a higher genetic variation in the fjord. Being accustomed to a higher variability in environmental conditions, fjord reefs may be more resilient with regard to environmental changes, particularly if genetic diversity supports adaptive responses to future ocean change  need to be assessed in future studies in order to quantify the relative contributions of both processes in a census-based approach (10.7717/peerj.7586/supp-1Supplemental Information 1L. pertusa with regard to net calcification, linear extension and mortality over 1 year deployment in the natural environment (Each data point indicates the average performance of live ironment . Dead coironment . For botironment  and 2. Fironment  are calcClick here for additional data file.


RNAct allows easy lookup of known and predicted interactions and enables global views of the human, mouse and yeast protein–RNA interactomes, expanding them in a genome-wide manner far beyond experimental data (http://rnact.crg.eu).Protein–RNA interactions are implicated in a number of physiological roles as well as diseases, with molecular mechanisms ranging from defects in RNA splicing, localization and translation to the formation of aggregates. Currently, ∼1400 human proteins have experimental evidence of RNA-binding activity. However, only ∼250 of these proteins currently have experimental data on their target RNAs from various sequencing-based methods such as eCLIP. To bridge this gap, we used an established, computationally expensive protein–RNA interaction prediction method,  RNA-binding proteins (RBPs) are key in RNA splicing, processing, export, localization and regulation of translation and are implicated in a number of pathologies in humans. Examples include heterogeneous and life-threatening genetic disorders, such as amyotrophic lateral sclerosis , spinoceHomo sapiens RBPs currently have high-throughput experimental data on the identity of their target RNAs  and SacRIP-Chip ). A compcatRAPID  and APPRIS classification for isoforms and RNA subcellular localization from the RNALocate database. Known RBPs as well as interactions confirmed by large-scale experiments from the ENCODE project are clearly highlighted.Proteomes were obtained from UniProt . SequencTranscriptomes were obtained from GENCODE (for human and mouse)  and EnsecatRAPID algorithm: short or extreme length , or unsuccessful RNA secondary structure prediction using the ViennaRNA package which catRAPID relies on  area under the curve (AUC) of 0.78 with high-confidence eCLIP data .To compute the interaction propensity scores, we used the approach  with theapproach ,19 and napproach . The catcatRAPID scores available for download upon request.When including all eCLIP interactions regardless of replication (723 881 interactions for GENCODE ‘basic’ RNAs), this AUC is still 0.76. Normalizing the prediction score by sequence lengths, similarly to a previous work , we founet al.  were obtained from the ENCODE Project in narrowPeak format ,25. Thiset al. .A very recent census of proteins with experimental evidence of RNA-binding activity in human (1393 known RBP genes), mouse (1914 known RBPs) and yeast (1273 known RBPs) was used to flag proteins as known RBPs in RNAct  for the score distribution for known human RBPs from Hentze et al. . RNAct collects no data on its users.RNAct is built for extreme ease and speed of real-world use. The landing page (Search) contains a single search box which allows entry of any protein or RNA identifier (e.g. ‘tdp43’ or ‘hotair’). Unless the term is highly ambiguous (e.g. ‘ataxin’), most searches resolve to a single gene symbol, giving a choice of species and protein or RNA on the disambiguation page that follows Figure . Table 1This design minimizes tedious input elements (e.g. a species dropdown box) and instead facilitates discovery and comparison across protein families and species. Matching fields are highlighted in green, which allows intuitive selection of the intended match (e.g. the RNA transcript in question when searching for ‘ENST00000237536’) while leaving room for additional useful choices (e.g. the corresponding protein for transcript ‘ENST00000237536’). The search box is available in the top right of every page and is easily navigated to by pressing the tab key.Once a protein of interest is selected, the Protein view Figure  shows a Once an RNA is selected, the RNA view shows a list of predicted protein interaction partners prioritized by prediction score. Alternatively, the view can be sorted by experimental results simply by clicking on the experimental columns. Interactions with experimental evidence are highlighted ,24, as aA common use case for RNAct is the prediction of interactions within a set of proteins and RNAs, allowing the rapid prioritization of candidates for validation, and the analysis of specific pathways or systems. The Pairwise search feature allows entry of a set of proteins and a set of RNAs, either in multiple lines or separated by commas, and allows any identifier types which the Search function can resolve, including ambiguous queries (e.g. for ‘lysine demethylase’). The only limitation is the total number of pairs queried, which is currently limited to 10 000 .These views list all proteins or RNAs contained in RNAct, i.e. the human, mouse and yeast reference proteomes and transcriptomes. In the Browse Proteins view, proteins are listed in order of availability of experimental interaction data (e.g. from eCLIP), evidence of RNA-binding activity (known or predicted RBPs), species and gene symbol. This allows the easy retrieval of known RBPs, particularly those with experimental interaction data. In the Browse RNAs view, transcripts are sorted by species, gene symbol, GENCODE ‘basic’ status, APPRIS classification, TSL and descending transcript length. This means that the best-supported transcript for a given gene will appear first.C. elegans and D. melanogaster.All RNAct protein–RNA interaction prediction data for human, mouse and yeast are available from the Download page. For human, the predictions are split into two sets for performance reasons: GENCODE ‘basic’ transcripts (covering a representative subset of 98 608 RNAs), and ‘non-basic’ transcripts making up the rest of the transcriptome. Both files can be concatenated for a full view of the human protein–RNA interactome, covering 20 778 proteins and 199 330 RNA transcripts. For mouse, only the GENCODE ‘basic’ transcripts are currently available, while the full annotated transcriptome is available for yeast. The RNAct predictions are licenced under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Licence (CC BY-NC-SA 4.0). A complete set of supporting tables containing protein and RNA annotations, identifier mappings used internally for searching, and the experimental data used (e.g. eCLIP) is available on the Download page as well. We intend to complete and add predictions for additional species such as The About page gives more details on the algorithm and datasets used, provides literature references and answers what we expect to be frequently asked questions, including contact details.catRAPID, similar to a CLIP binding profile, although this would require us to upgrade our server infrastructure due to the terabytes of data involved for all pairwise interactions. In summary, RNAct provides easy access to genome-scale protein–RNA interaction predictions with useful supporting annotation and experimental interaction evidence.RNAct provides an easy-to-use view of protein–RNA interactions in model organisms. It is intended to grow, both in terms of the number of species covered  and in terms of the experimental datasets provided. We hope our database will be particularly useful for studying gene regulatory events and networks at the post-transcriptional level . In addi


One of the key pathways implicated in amyotrophic lateral sclerosis (ALS) pathogenesis is abnormal RNA processing. Studies to date have focussed on defects in RNA stability, splicing, and translation, but this review article will focus on the largely overlooked RNA processing mechanism of RNA trafficking, with particular emphasis on the importance of glia. In the central nervous system (CNS), oligodendrocytes can extend processes to myelinate and metabolically support up to 50 axons and astrocytes can extend processes to cover up to 100,000 synapses, all with differing local functional requirements. Furthermore, many of the proteins required in these processes are large, aggregation-prone proteins which would be difficult to transport in their fully translated, terminally-folded state. This, therefore, highlights a critical requirement in these cells for local control of protein translation, which is achieved through specific trafficking of mRNAs to each process and local translation therein. Given that a large number of RNA-binding proteins have been implicated in ALS, and RNA-binding proteins are essential for trafficking mRNAs from the nucleus to glial processes for local translation, RNA misprocessing in glial cells is a likely source of cellular dysfunction in ALS. To date, neurons have been the focus of ALS research, but an intrinsic deficit in glia, namely astrocytes and oligodendrocytes, could have an additive effect on declining neuronal function in ALS. This review article aims to highlight the key evidence that supports the contention that RNA trafficking deficits in astrocytes and oligodendrocytes may contribute to in ALS. Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are characterized pathologically by the accumulation of cytoplasmic inclusions in affected neurons and glial cells that, in 97%–98% of cases, stain positive for TAR-DNA binding protein 43  are highlighted in Humans have the highest proportion of glial cells, with their primary role being to support neuronal function, whether that be Q331K mouse model, it was shown that selective removal of the mutation from motor neurons alone did not prevent motor neuron or neuromuscular junction loss and did not reduce the activation of microglia or astrocytes , FUS RNA-binding protein (FUS), C9orf72 and, to a lesser extent, heterogeneous nuclear ribonucleoproteins A1 (hnRNP A1) and A2/B1 (hnRNP A2/B1), EWS RNA-binding protein 1 (EWSR1), angiogenin (ANG), senataxin (SETX), matrin 3 (MATR3) ataxin-2 (ATXN2) and TATA-box binding protein associated factor 15 ; reduced MeCP2 protein levels in glia has previously been shown to be toxic to neurons leading to altered dendritic morphology and, ultimately, neuronal death , which pathologically presents as G4C2 repeat RNA foci and sense and anti-sense repeat-associated non-ATG (RAN) translation of di-peptide repeat proteins (DPRs) as well as TDP-43 aggregation. G4C2 repeat RNA foci co-localize with various RNA-binding proteins in neurons, including hnRNP A1 and hnRNP H/F, shown in post-mortem tissue  neuronal support, including metabolic, trophic and structural support; (ii) neuronal synapse and axon formation; and (iii) regulation of cerebral blood flow  mRNA is known to be translated locally at astrocytic processes and is an abundantly expressed protein in all astrocytes, despite the known heterogeneity among the astrocyte population. To further validate this, it has been shown to equate to 15% of an astrocyte’s volume and the protein expression is isolated predominantly to its processes  is Quaking  and whilst its trafficking has not been explored, EAAT2 transcripts with both intron-retention and exon-skipping have been found in 65% of sporadic ALS patients in neuropathologically affected areas as well as in the cerebrospinal fluid , Aldh1a1 , Fads1, Fads2, Scd1, Scd2, Elovl5 and Hadha , Kif1c and Myo1D (encoding proteins that maintain the cytoskeleton), Mertk, Sparc and Thbs4 (encoding proteins key for synapse regulation), ApoE (encoding a class of proteins required for fat metabolism) and CLU , profiling these inclusions demonstrated that many astrocytic proteins have been associated with these FMR inclusions, including vimentin, GFAP, and hnRNP A2/B1 [and, interestingly, myelin basic protein (MBP; Iwahashi et al., Thus, if these mRNAs failed to adequately translate, this would have a detrimental impact on neuronal function given that they are critically involved in supportive pathways like metabolism and synapse regulation. Further, not only could mRNAs become sequestered Thus, the sequestration of key RNA-binding proteins in astrocytes is likely to have a detrimental effect by impeding the trafficking of mRNAs critical for cellular function to their site of local translation.Oligodendrocytes have been underappreciated for their role in the CNS with the focus to date limited to their role in myelination. Recent research has uncovered their ability to metabolically support neurons through the shuttling of lactate (Fünfschilling et al., MBP (Kristensson et al., MOPB; Holz et al., CAII; Ghandour and Skoff, tau (LoPresti et al., APP; Garcia-Ladona et al., It has long been established that many mRNAs in oligodendrocytes are translated locally at the myelin sheath (Campagnoni et al., MBP mRNA trafficking and both are also known binding partners of G4C2 repeats and TDP-43 (Highley et al., SMN2; Kashima et al., MBP trafficking is QKI; QKI mutant mice actually have impaired nuclear export of MBP mRNA resulting in impaired myelination in these mice (Larocque et al., C9orf72 mutations demonstrated that RNA foci sequester hnRNP H/F (Cooper-Knock et al., hnRNP A1 and hnRNP A2/B1 are involved in Oligodendrocytes have a very low annual turnover of just 1 in 300 oligodendrocytes in humans, making them a very stable cell type in the CNS (Yeung et al., C9orf72 cases, can have a toxic gain-of-function effect by sequestering other mRNAs and proteins, compromising normal cellular function. We suggest this process is also occurring in glia and is complicated by the fact that many critical mRNAs required for normal astrocytic and oligodendroglial function are locally translated. Given the association of many RNA-binding proteins with the typical protein aggregations found in ALS cases, and the known associations of these RNA-binding proteins with key mRNAs in astrocytes and oligodendrocytes, we suggest that this is a potential pathogenic pathway of disease contributing to cellular dysfunction in patients with ALS.It is well established that the hallmark pathological signature of ALS is misfolding and accumulation of aggregation-prone proteins, the most common of these proteinopathies being TDP-43. To date, very little attention has been given to the role of glia, specifically astrocytes and oligodendrocytes, in the pathogenesis of ALS as a result of intrinsic cellular deficits arising from protein aggregation. It is known in neurons that pathological protein aggregation, as well as the formation of RNA foci in SB, JG, SC and BT contributed to the preparation, writing and editing of this review article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.