Map accuracy assessment yielded an overall accuracy of 94.9% once adjusted for known map marginal proportions. Cluster analysis of cross-shelf habitat type and widths indicated that the benthic habitats were different than those further south and warranted designation of a new coral reef ecosystem region. Unlike the FRT further south, coral communities were dominated by cold-water tolerant species and LIDAR morphology indicated no evidence of historic reef growth during warmer climates. Present-day hydrographic conditions may be inhibiting poleward expansion of coral communities along Florida. This study provides new information on the benthic community composition of the northern FRT, serving as a baseline for future community shift and range expansion investigations.Climate change has recently been implicated in poleward shifts of many tropical species including corals; thus attention focused on higher-latitude coral communities is warranted to investigate possible range expansions and ecosystem shifts due to global warming. As the northern extension of the Florida Reef Tract (FRT), the third-largest barrier reef ecosystem in the world, southeast Florida (25–27° N latitude) is a prime region to study such effects. Most of the shallow-water FRT benthic habitats have been mapped, however minimal data and limited knowledge exist about the coral reef communities of its northernmost reaches off Martin County. First benthic habitat mapping was conducted using newly acquired high resolution LIDAR bathymetry and aerial photography where possible to map the spatial extent of coral reef habitats. Quantitative data were collected to characterize benthic cover and stony coral demographics and a comprehensive accuracy assessment was performed. The data were then analyzed in a habitat biogeography context to determine if a new coral reef ecosystem region designation was warranted. Of the 374 km Rigorously ground-truthed benthic habitat mapping via geographic information systems (GIS), a process by which remote sensing data are interpreted into seafloor habitats, provides this valuable information. Globally, benthic habitat mapping has been employed in many coral reef ecosystems, utilizing various techniques and data types including the interpretation of aerial photography, satellite imagery, bathymetric data, Coral reefs thrive in warm tropical waters, therefore much of the coral reef habitat mapping has focused on tropical and subtropical areas with little regard for higher latitude temperate regions even though coral communities may be present As the northern extension of the Florida Reef Tract (FRT), southeast Florida is a prime region to study climate change effects. The FRT, the third largest barrier reef ecosystem in the world Most of the shallow-water FRT benthic habitats have been mapped No specific permissions were required for this study. The study was observatory and did not include the disturbing or removal of organisms other than those expected from normal SCUBA diving. The study was approved by Florida Department of Environmental Protection, Florida Fish and Wildlife Conservation Commission, and the National Oceanic and Atmospheric Administration, with the latter two providing the funding. A portion of the study included the St. Lucie Inlet Preserve State Park, who also supported the effort. The field studies did not involve endangered or protected species at the time of the study.2 of seafloor from shore to the 30 m depth contour. Image-based analyses in deeper water were not useful due to poor water clarity; therefore, a high resolution (4 m) LIDAR bathymetric survey was conducted to image the sea floor. LIDAR were acquired in December 2008 and 2009 by Blom Aerofilms, Ltd. using an Airborne Hydrography AB Hawkeye II LIDAR The marine benthic habitats in Martin County were mapped using a combined technique approach similar to other southeast Florida counties Benthic habitat maps were produced by visual interpretation of the bathymetric LIDAR, aerial photography, and other data at a 1∶6000 scale with a 0.4 hectare minimum mapping unit, classifying seafloor features based on their geomorphology. Geomorphology and depth were used as surrogates for differing benthic communities The benthic habitat classifications conformed to the scheme used in previous regional efforts Coral reef and colonized hardbottom. Hardened substrate of unspecified relief formed by the deposition of calcium carbonate by reef building corals and other organisms or existing as exposed bedrock. Habitats within this category have some colonization by live coral.Colonized pavement. Flat, low-relief, solid carbonate rock with coverage of macroalgae, hard coral, gorgonians, and other sessile invertebrates that are dense enough to partially obscure the underlying carbonate rock.Colonized pavement-shallow. Colonized pavement in water shallower than 10 m. This category includes rubble in many areas; however, consolidated rubble fields are a less frequent feature in shallow water. Especially inshore of the ridge complexes, limited rubble is found and a wide, contiguous area of pavement is encountered. This area can have variable sand cover, which shifts according to wave energy in response to weather. Thus, some of the colonized pavement will always be covered by shifting sand and the density of colonization will be highly variable.Ridge. Linear, shore-parallel, low-relief features that appear to be submerged cemented ancient shoreline deposits. Presumably, they are an extension of the foundation upon which the linear reefs grew further south and consist of early Holocene shoreline deposits; however, verification is needed. The biological cover is similar to that of colonized pavement with macroalgae, scleractinians, gorgonians, and other sessile invertebrates that are dense enough to partially obscure the underlying carbonate rock.Ridge-deep. Linear, often shore-parallel, low-relief features that mostly occur deeper than 20 m. It consists of hardbottom with sparse benthic communities in most parts likely due to variable and shifting rubble and sand cover. Some parts contain exposed ledges where large fish  may congregate.Ridge-shallow. Ridges found in water shallower than 10 m near shore that are geomorphologically distinct, yet their benthic cover remains similar to the shallow colonized pavement communities on the surrounding hard grounds.Deep ridge complex. A complex of ridges found in deep water in northern Palm Beach and Southern Martin Counties. These features reside in depths from 20 to 35 m and are presumed to be of cemented beach dune origin. Most of this habitat consists of low cover, deep communities dominated by small gorgonians, sponges, and macroalgae, but denser areas exist, especially near areas of higher relief. Some areas, particularly between ridges, may contain large areas of shifting unconsolidated sediments.Scattered rock in unconsolidated sediment (SCRUS)-deep. Primarily sand bottom with scattered rocks that are too small to be delineated individually in water deeper than 20 m.SCRUS-shallow. Primarily sand bottom with scattered rocks that are too small to be delineated individually in water shallower than 20 m.Unconsolidated sediments. Unconsolidated sediment with less than 10 percent cover of submerged vegetation.Sand. Coarse sediment typically found in areas exposed to currents or wave energy.Sand–deep. Sand deeper than the 25 m contour exposed to a lower energy environment that can have finer grain size, sparse Halophila spp., and a rubble component. This habitat can contain a high cover of turf and low-lying benthos in some areas.Sand–shallow. Shallow water (< 25 m) sediment exposed to a higher energy environment. Large, mobile sand pockets are found on the areas of consolidated hardgrounds. It is believed that the sand movement is a deciding factor in the generation of benthic patterns.Artificial. Manmade submerged habitats such as wrecks, portions of rip-rap jetties, and spoil piles.Inlet jetty. Artificial structures placed at the inlet channel primarily to block wave energy and reduce erosion.Sand borrow area. Pits excavated during previous sand dredging projects for beach nourishment.et al. Benthic characterization surveys were conducted in August 2012. Site locations were determined by a statistically robust random sample design similar to Smith Multivariate analyses were performed in Primer v6. A cluster analysis and corresponding non-metric multi-dimensional scaling (MDS) plot was constructed using Bray-Curtis similarity indices of the benthic cover data (square-root transformed) to evaluate benthic cover between sites. A one-way analysis of similarity (ANOSIM) was performed to statistically determine the strength of the site categorization by habitat. ANOSIM is a permutation-based hypothesis test analogous to univariate analyses of variance (ANOVAs) that tests for differences between groups of (multivariate) samples from different experimental treatments. The closer the R statistic is to 1, the stronger the categorical groups. Its strength is dependent on the number of samples per category which defines the number of possible permutations. One-way nonparametric ANOVA using the Wilcoxon method was used to examine differences in rugosity and biological cover category data  by habitat.A map accuracy assessment (AA) was performed. Target locations were determined by a GIS-based, stratified random sampling technique used in other regional mapping efforts Underwater video from a drop camera was taken at each AA target location. This procedure involved the boat positioning itself within 5 m of the target and lowering a Sea Viewer 950 underwater color video drop camera with a Sea-trak GPS video overlay connected to a Garmin 76CSx GPS with WAAS correction (<3 m accuracy) to the bottom. Color video was recorded over the side of the stationary/drifting vessel approximately 0.5–2 m from the seafloor. Fifteen second to two minute video clips were recorded directly to a digital video recorder. Video length depended on the habitat type and vessel drift. Videos of large expansive sand habitats were generally short (< 1 min) while reef habitats, especially edges, were longer. Concurrent with recording video, an observer categorized each site according to the video and surrounding area into a database.o) was calculated as the sum of the major diagonal (i.e. correct classifications) divided by the total number of accuracy assessment samples. The producer’s and user’s accuracies are both category-specific. Each diagonal element was divided by the column total to yield a producer’s accuracy and by the row total to yield a user’s accuracy. The producer’s and user’s accuracies provide different perspectives on the classification accuracy of a map. The producer’s accuracy (omission/exclusion error) indicates how well the mapper classified a particular habitat (e.g. the percentage of times that substrate known to be sand was correctly mapped as sand). The user’s accuracy (commission/inclusion error) indicates how often map polygons of a certain habitat type were classified correctly . The Tau coefficient (Te) is a measure of the improvement of classification accuracy over a random assignment of map units to map categories e is simply an adjustment of Po by the number of map categories. As the number of categories increases, the probability of random agreement diminishes, and Te approaches Po.Statistical analyses to determine the thematic accuracy by confusion matrix approach were derived from Congalton Direct interpretation of producer’s and overall accuracies can be problematic, as the stratified random sampling protocol can potentially introduce bias Benthic habitat polygons were tested for spatial autocorrelation in ArcGIS using Moran’s Index to ensure the polygons did not significantly differ from a random distribution. Map data were then combined with the previous southeast Florida maps 2 in GIS of which 95.2% was Sand, 4.1% was Coral Reef and Colonized Pavement, and 0.7% was Other Delineations  indicated the Ridge-Deep and Ridge-Shallow were significantly different (p  =  0.03), supporting the MDS results, yet the difference was not very strong (R  =  0.257) . CompariDifferences of mean percent benthic cover by habitat were evident, however, cover varied greatly within habitats and most cover types were low (< 5%) . Turf alAlthough the mean biotic cover categories  was smaller on the Colonized Pavement-Shallow (5.5±0.84 SEM), it was not significantly different from the Ridge-Shallow (7±0.75 SEM) and Ridge-Deep (7.4±0.64 SEM). However, mean rugosity of the Ridge-Shallow (1.25±0.07 SEM) and Colonized Pavement-Shallow (1.09±0.08 SEM) sites were significantly higher than the Ridge-Deep (1.04±0.06 SEM) (p  =  0.03) indicating the Ridge-Deep sites were flatter.2 surveyed was 0.32 m−2, equating to one coral every 3.1 m2. Although many corals were counted, colony size was generally small. The estimated total area of live tissue (max length * max width) –  for all 553 colonies was 2.8 m2. Three species accounted for 97.7% of the total live coral tissue in the transects; P. clivosa (42.9%), Siderastrea siderea (30.2%), and O. diffusa (24.6%). Although only 8 P. clivosa colonies were counted, they were the largest colonies and accounted for the most live tissue area. Interestingly, S. siderea had the smallest mean length (4.7 cm), yet was the second highest contributor to live tissue area because of its high numbers (444).A total of 553 stony coral colonies were identified, counted, and measured. Nine species were found , but Siderastrea siderea (80.3%) and O. diffusa (15.9%) dominated the populations. Stony coral density for all sites out of 1737 m− = 0.48±0.11 SEM) followed by Ridge – Shallow (x− = 0.29±0.13 SEM) and Colonized Pavement – Shallow (x− = 0.22±0.14 SEM). S. siderea and O. diffusa were the densest corals in all habitats. Although not significant, mean S. siderea densities were highest in the deep ridge (x− = 0.43±0.10 SEM), then shallow ridge (x− = 0.23±0.12 SEM), and were lowest on the shallow colonized pavement (x− = 0.13±0.14 SEM). Mean O. diffusa densities were highest on the shallow colonized pavement (x− = 0.08±0.04 SEM), lower on the deep ridge (x− = 0.05±0.03 SEM), and lowest on the shallow ridge (x− = 0.03±0.04 SEM). Mean estimated live tissue area was not significantly different between habitats. Pseudodiploria clivosa had the highest estimated mean coral live tissue, but it was only found in the shallow ridge habitat.Coral density and live tissue area varied between species by habitat. Although not significant due to high variation, Ridge – Deep habitats had the highest mean coral density  cm out of 8 colonies that ranged from 33 to 80 cm. Two of these colonies were also the tallest corals encountered (25 cm). P. clivosa (11.6±9.9) was the only species whose mean max height was above 10 cm.Mean maximum coral length and height were low for most species (less than 10 cm) and did not significantly differ between habitats. There were one 12 cm The assessment of major habitats yielded a high level of accuracy as indicated by the overall accuracy (85.6%) , the oveFive regions along the coast were previously identified using the same methodology The analysis of similarity (ANOSIM) performed to statistically determine the similarity of the six final regions based on the cross-shelf transect data showed strong differences (R statistic > 0.849) between categories in 11 of the 15 pairwise tests . The weaRecent analyses of habitat spatial distributions along the southeast Florida coast identified 5 coral reef ecosystem regions and potential biogeographic boundaries Differences in benthic cover indicate that the Martin region has a biological composition different from other areas of the FRT. In 2007, a two-year detailed regional study on macroalgal communities showed that Martin County had the highest macroalgal cover in southeast Florida 2 of survey area, compared to nine found in Martin in 1737 m2 of survey area. Similarly the Southeast Florida Coral Reef Evaluation and Monitoring Project (SECREMP), a regional coral reef monitoring program in place since 2003, found 9 species present in Martin compared to 25 species further south −2; 8.1 times greater than our density estimates in Martin (0.32 m−2) Diadema were more abundant in the Martin County sites than the sites in the other three counties  Comparisons of the coral communities also support regional separation. Monitoring data of reefs in similar depths approximately 75 km south (Broward County) found 2.8 times more stony coral species Epinephelus itajara) .This is not to diminish the importance of reef communities in Martin, but rather to place them in context with the rest of the reef tract. In total, a species list of occurrences has logged twenty-two species of hard corals in Martin since the early 1980’s  . They also host numerous reef fish species at high densities in certain areas including large aggregations of Goliath grouper  The benthic habitats in the northern latitudes of the Florida Reef tract off Martin County are distinctly different in both habitat morphology and biological communities than the reefs further south. The shelf is much wider, yet the amount of exposed hardbottom habitat is much less. And, as reported in regional monitoring studies, the number of coral species is reduced 77% from 38 in the Florida Keys to 9. This pattern is similar to other high-latitude reef systems located on eastern continental margins Although the causes limiting coral reef growth are complex, temperature is often used as a surrogate because of its high correlations with many of the causative factors Historic information might give clues as to how present coral reefs may respond to global warming An explanation for the abrupt end to historic coral reef growth might be evident along the coast today.Martin County is situated just north of a distinct area along the southeast Florida coast called the Bahamas Fault Zone Siderastraea and Oculina, which are known to be cold tolerant Siderastraea and Oculina colonies, neither of which is considered a major constructional component of tropical Caribbean coral reefs.Globally, the poleward distribution of coral reefs coincides with the 18°C isotherm It is likely that intense, frequent, long-duration upwelling events are inhibiting tropical coral reef communities from establishing in the Martin region. Although it is unknown how climate change will affect coastal currents and upwelling, historic reef growth in Martin during warmer times is not visually evident. If the Gulf Stream continues to carry the warmest water offshore and these upwellings continue, conditions in Martin and further north will not be conducive for coral reef development and may inhibit poleward expansion of tropical coral reefs.


Long noncoding RNAs (lncRNAs) have been detected in nearly every cell type and found to be fundamentally involved in many biological processes. The characterization of lncRNAs has immense potential to advance our comprehensive understanding of cellular processes and gene regulation, along with implications for the treatment of human disease. The recent ENCODE (Encyclopedia of DNA Elements) study reported 9,640 lncRNA loci in the human genome, which corresponds to around half the number of protein-coding genes. Because of this sheer number and their functional diversity, it is crucial to identify a pool of potentially relevant lncRNAs early on in a given study. In this review, we evaluate the methods for isolating lncRNAs by immunoprecipitation and review the advantages, disadvantages, and applications of three widely used approaches – microarray, tiling array, and RNA-seq – for identifying lncRNAs involved in gene regulation. We also look at ways in which data from publicly available databases such as ENCODE can support the study of lncRNAs. Long noncoding RNA (lncRNA) is operationally defined as RNA longer than 200 bases that does not encode mRNA, rRNA or tRNA ,2. AlthoThe first challenge in studying lncRNAs is how to collect RNA pools that potentially contain lncRNAs of interest. One can prepare RNA pools by simply isolating total RNA from cells or tissues in an unbiased manner; however, immunoprecipitation-based approaches are also commonly used to enrich lncRNAs associated with specific proteins. RNA immunoprecipitation (RIP) can be performed with or without cross-linking whole cellular components before making cell extracts. Without cross-linking, one can isolate lncRNA complexes already existing in soluble form and those that can be readily dissociated from chromatin. Zhao et al. used RIP of polycomb repressive complex 2 (PRC2), a key regulator of epigenetic silencing, without cross-linking and co-immunoprecipitated the lncRNA Xist, which was amplified by RT-PCR . Using tThere are several RIP techniques that employ cross-linking. RIP is sometimes performed after ultraviolet (UV) irradiation of cells, which cross-links RNA and protein  but not protein and protein . This unA related variation is called CLIP (cross-linking and immunoprecipitation), which was designed to isolate a protein-interacting domain within a given RNA molecule after using a stringent wash to reduce non-specific binding . In a tyCross-linking with UV or formaldehyde followed by fragmentation of chromatin is used to immunoprecipitate RNA-chromatin complexes (RNA-chromatin immunoprecipitation or RNA-ChIP) ,14,22. WFor any of these immunoprecipitation-based approaches, specificity and affinity of the antibodies are decisive factors for the success or failure of the projects. While the specificity of the antibodies is commonly verified by detecting only one band in western blotting, the antibodies may react with other proteins when detergents are used at a low concentration during immunoprecipitation. One solution to address the specificity issue is to use multiple antibodies against the same protein and select reproducibly co-precipitated lncRNAs for further study. Similarly, immunoprecipitation of several different subunits within a single protein complex is also an option to identify lncRNAs that are likely to be genuinely interacting with the complex.Microarray-based approaches and RNA-seq are two of the most commonly used genome-wide screening methods to identify lncRNAs that might be relevant to a specific biological question. Although a tiling array should be included in the microarray section by definition, it will be discussed separately in the next section as it is frequently used for different purposes. Because traditional microarrays can only detect the presence or absence of known lncRNAs in an RNA pool, they are inherently incapable of identifying novel lncRNAs. Inability of distinguishing different splicing variants is another disadvantage of microarrays unless probes encompassing exon-exon junctions are present on the chip. However, given the cost and complexity of the analysis of RNA-seq data, microarray remains the first choice in many applications -18. In pData generation with microarrays is relatively easy compared to the subsequent step of selecting potentially important lncRNAs from the positive probes on the arrays because the majority of identified lncRNAs remain uncharacterized. Here, the work by Loewer et al. serves as an exemplary case study of how to narrow down lncRNA candidates relevant to one’s interest, in this case, association with pluripotency . Loewer HOX genes and compared skin fibroblasts isolated from different anatomical regions of the body [HOX gene clusters. This configuration allowed for the identification of hybridized DNA sequences at 5-base resolution. Polyadenylated RNAs prepared from fibroblasts were then hybridized to the tiling arrays, resulting in the discovery of the lncRNA HOTAIR transcribed from an intergenic region within the HOXC cluster. A similar HOX tiling array was used to identify lncRNAs specifically expressed in metastatic breast carcinoma [HOXA1 and HOXA2 genes with commercially available tiling arrays covering the human HOXA gene cluster [Unlike traditional microarrays, DNA tiling arrays contain oligonucleotide probes encompassing an entire length of a defined DNA region. Resolution of the hybridized genomic DNA sequence can be adjusted by changing the length of the overlapping sequences between two neighboring probes. A major advantage of using tiling arrays is their capacity to identify novel lncRNAs in a selected DNA region without prior knowledge of their precise locations within the region. The DNA region can be defined by the residing genes of interest. For instance, Rinn et al. focused on lncRNAs expressed in the region of the human he body . They prrcinoma . The lnccluster .The DNA regions of interest can also be determined by the unique epigenetic features of the regions. Actively transcribed genes are enriched with trimethylation of lysine 4 on histone H3 at their promoters and trimethylation of lysine 36 on histone H3 in their coding regions , which aRNA-seq is a powerful tool based on the principles of next-generation sequencing that can be applied to the detection and quantification of lncRNAs. Some advantages of using RNA-seq over a microarrary-based approach are that RNA-seq works on a genome-wide scale at single nucleotide resolution and is not limited to detecting already known sequences. Thus, it can be used to discover previously unknown lncRNAs in an unbiased manner . HoweverBefore beginning RNA-seq, one must decide whether to use total RNA or polyadenylated RNA. The presence of rRNA  and tRNA (15%) ,35 can dab initio[After sequencing, the generated reads are typically aligned to the UCSC mouse mm10 or human hg19 reference genomes using software programs such as the short-read mappers Bowtie 2  and Burrab initio. From heab initio, as wellab initio and NREDab initio.Novel lncRNAs often undergo further scrutiny to verify that they are not transcriptional noise and that they indeed do not encode proteins. For instance, if the candidate is located within a K4-K36 domain and enriched with RNA polymerase II binding sites and DNase I hypersensitivity sites (a sign of open chromatin) as detected with the ENCODE data, the candidate is likely to be a product of active transcription ,26,29. TThe recent identification of the genome-wide human lncRNA loci by the ENCODE project is undoubtedly a milestone toward the long-term goal of understanding the functional significance of lncRNAs in many biological phenomena. Applications of microarrays containing these probes will certainly lower the threshold of launching new studies of lncRNAs. However, the use of tiling arrays and RNA-seq will continue to be required to identify splicing variants and tissue-specific lncRNAs. In addition, because of the low conservation of lncRNA sequences across species, the use of these approaches in new species will remain necessary until their ENCODE equivalents become publicly available. Furthermore, we expect that additional technological innovations geared toward studying lncRNAs will continuously emerge to support the rapid development of this fascinating research field.ChIP: Chromatin immunoprecipitation; CLIP: Cross-linking and immunoprecipitation; ENCODE: Encyclopedia of DNA Elements; FANTOM: Functional annotation of the mammalian genome; lincRNA: Large intergenic noncoding RNA; lncRNA: Long noncoding RNA; NRED: Noncoding RNA Expression Database; RefSeq: Reference Sequence; RIP: RNA immunoprecipitation; RNA-seq: RNA sequencing; UV: Ultraviolet.The authors declare that they have no competing interests.CL and NK wrote and edited the drafts of the paper. Both authors read and approved the final manuscript.


Based on the presumed capability of a prebiotic pocket-like entity to accommodate substrates whose stereochemistry enables the creation of chemical bonds, it is suggested that a universal symmetrical region identified within all contemporary ribosomes originated from an entity that we term the ‘proto-ribosome’. This ‘proto-ribosome’ could have evolved from an earlier machine that was capable of performing essential tasks in the RNA world, called here the ‘pre-proto-ribosome’, which was adapted for producing proteins. In this process, messenger RNA (mRNA) carries the genetic information, and tRNA molecules carry the amino acids. The ribosome possesses a channel along which the mRNA chain progresses as well as three tRNA binding sites, designated as A (aminoacyl), P (peptidyl) and E (exit). Among those, decoding as well as peptide bond formation are performed by the A- and P-tRNAs.In all organisms, ribosomes consist of two subunits of unequal size, each having defined tasks. The small ribosomal subunit is involved in the initiation of the translation process, in selecting the translated frame, in decoding the genetic message, and in controlling the fidelity of codon–anticodon interactions. The large subunit catalyses the formation of the peptide bond, elongates the newly synthesized proteins and gates the nascent chains by channelling them through their dynamic exit tunnel. The ribosomes are multi-component riboprotein assemblies of molecular weights that span between 2.5 MDa (in prokaryotes) and 4 MDa (in eukaryotes). Their main catalytic task is peptide bond formation. The fact that peptide bonds can be made spontaneously from activated amino acids, should not be overlooked. The ribosome increases the pace of this reaction, positions the substrates in the right stereochemistry for peptide bond formation and substrate-mediated catalysis, and ensures processivity.a). During the elongation cycle, the three tRNA molecules act in a concerted manner and translocate from the A- to the P-site and from there to the E-site. In conjunction with the tRNA translocation, the mRNA progresses by precisely one codon.The L-shaped tRNA molecules are composed primarily of double helices, with an anticodon stem loop at one edge and a single-stranded universal moiety composed of the three nucleotides cytosine, cytosine, adenine (CCA) on their other edge (their 3′ end). The CCA end of the A-site tRNA is aminoacylated with the amino acid to be incorporated into the nascent protein. Similarly, the newly formed polypeptide chains are bound chemically to the CCA end of the P-site tRNA. The mRNA and the anticodon loop of the tRNA molecules interact with the small subunit, whereas the acceptor stem and the 3′ end of the tRNA molecules bind to the large subunit. Hence, all three tRNA sites span the two subunits, and each of the three tRNA molecules is located on both subunits a. DuringAnalysis of the various high-resolution crystal structures of bacterial ribosomal particles at various functional states, as well as the slightly lower resolution structure of the eukaryotic ribosome, which have become available in the last decade –8, indic2.Deinococcus radiodurans, Haloarcula marismortui and Escherichia coli , v, vb) [18b) , in the b) ,7.3.c and 2c). Hence it appears to be a relic of an ancient ribozyme that was capable of catalysing various RNA-involved reactions, which was adopted by the amino acids once they appeared and invaded the RNA world. Then it could have evolved into a molecular machine for peptide bond formation and non-coded amino acid polymerization, which was later optimized to become a pocket in which each half hosts a slightly different substrate.Within the contemporary ribosome, this entity has a pocket-like structure, with a shape that seems to be capable of maintaining stable conformation as an independent chemical entity with ribozyme catalytic capabilities figures c and 2c.The backbone fold of each half of this semi-symmetrical region resembles motifs identified in various ‘ancient’ as well as ‘modern’ natural RNA molecules of comparable size, consisting mainly of stem-elbow-stem motifs. Examples are tRNA, gene regulators, riboswitches, RNA polymerases, ribozymes catalysing phosphodiester cleavage, RNA processing and RNA modification. Many of these ribozymes are also believed to be remnants from the prebiotic world, which are supposed to be sufficiently stable to survive environmental alterations and evolutionary stresses.It was shown recently that certain RNA bases could have been produced under prebiotic conditions . AdditioActivated amino acids could be the suitable substrates of the ancient machine. These could be formed by their attachment to nucleotides, exploiting rather common reactions that were shown to occur by diverse processes –29. HencConceptually, the initial dipeptides could be elongated and form oligopeptides via the same reaction principles. The existence of well-performing polypeptides, catalysing fundamental reactions and/or stabilizing the machine producing them, may have led to the emergence of the genetic code. It is conceivable that initially short oligopeptides were produced accidentally. Among them, those that could provide additional structural support for the synthetic apparatus that produced them could attach to it and form structural arrangements similar to protein–RNA interactions of the contemporary ribosome. According to this suggestion, the proto-ribosome produced peptides with amino acid composition that was sufficiently biased for fulfilling simple tasks, giving a selective advantage.c and 2c). Pocket formation by dimerization is supported by the finding that mutations in the regions involved in A/P interaction (c) within the contemporary ribosome prevented dimerization. A noteworthy example is the segment introduced by us into the construct that is composed of the GNRA tetra loop containing an A-minor interaction (A-minor interactions involve adenine and the minor groove of an RNA double helix) that seems to stabilize the pocket.We are assessing the feasibility of the existence of a dimeric proto-ribosome capable of the formation of chemical bonds according to the following scheme. First, we are assessing the tendency of various RNA chains to dimerize and form a pocket-like entity figures c and 2c.eraction c within in vitro, several, albeit not all chains with sequences resembling those observed in the contemporary ribosome, formed dimers that may adopt a ‘pocket-like’ structure. So far, a marked preference for dimerization was detected for sequences resembling the P-region of the PTC, including those that underwent so-called ‘mutational’ alterations by in vitro mutagenesis, introduced in the regions that connect the two symmetrical halves of our constructs (c). Remarkably, P-loop incorporation (d) into the RNA constructs did not prevent dimerization. These dimers are tested for their ability to bind small molecules, nucleotides and amino acids conjugated with short nucleotides. Preliminary results indicated detectable binding of several compounds representing RNA world components.Among the various RNA segments that have been synthesized nstructs c. Remarkporation d into tha,b), which is in accord with the contemporary accommodation of the initial tRNA at the P-site of the PTC. Similar preference was observed for semi-random-sequence constructs of size and base pair composition with potential to self-fold into conformations resembling the ribosome symmetrical region, presumably owing to the above considerations [So far, particularly noticeable preference to dimerize was observed for sequences identical to or resembling the P-region of the PTC, contrary to those that are similar to the A-site region. This unexpected observation may indicate that the precursor of the ancient ‘pre-proto-ribosome’ machine was built mainly by the dimerization of the P-portion of the symmetrical region. The reasons for this preference are still to be clarified. It may result from a higher structural, chemical or functional stability. This preference may indicate that the proto-ribosome (or the pre-proto-ribosome) was composed of a dimer of the P-region a,b, whicerations –17. The c) dictating the involvement of carriers that could decode while bound to the cognate amino acid, namely the tRNA molecules, similar to the opinion discussed by Di Mauro [The emergence of coded translation that includes the growing complexity of the ribosome into the size and shape allowing programmed translation could have occurred as a consequence of the survival of those oligopeptides that were produced accidentally, but became useful in the RNA world. Their existence could have been the driving force for the production of some kind of replication machinery. Coevolution of the proto-ribosome into the modern complex molecular machine may have led to the small, albeit significant, differences between the A- and the P- regions c dictatiDi Mauro . This suDi Mauro  as well Di Mauro . FurtherDi Mauro .4.Here, we suggest that a vestige of an RNA apparatus with ribozyme capabilities is embedded and functions within the modern ribosome. We advocate the existence of a chemical prebiotic machine, originating from an oligonucleotide and proceeding via a self-folded unit into a self-assembled dimer, thus producing a chemical pocket that could turn into an apparatus for peptide bonds formation. Internal interactions could have stabilized it. Hence, it could have survived and functioned on its own. However, additional interactions, e.g. with its own products, non-coded oligopeptides, could have contributed to its stability. This hypothesis requires the existence of self-replicating RNA molecules that can fold and create a pocket with catalytic capabilities.In short, the ribosome's architecture hints at its evolutionary pathway. Thus, it seems that the proto-ribosome was originally an RNA dimeric machine for performing RNA needs prior to the appearance of amino acids. The amino acids snatched it and turned it into an efficient machine producing proteins. Originally, small oligopeptides were formed. Those found to be useful have survived and led to the creation of a mechanism for duplicating themselves. This suggests that the more fit proto-ribosome products guided the appearance of the genetic code, namely the genetic code was created according to its products.


We show that this sequence is also required for the interaction between Spt5 and Nrd1, an RNA specific binding protein, and an exosome cofactor. Both the Spt4-Spt5, and the Nrd1-Nab3 complexes interact functionally with Rrp6, and colocalize at the rDNA. Mutations in the RNA binding domain of Nrd1, but not in its RNA Pol II CTD-interacting domain, and mutations in the RRM of Nab3 led to the accumulation of normal and aberrant polyadenylated pre-rRNAs. Altogether these results indicate that Nrd1-Nab3 contributes to recruiting the nucleolar surveillance to elongating polymerases to survey nascent rRNA transcripts.Ribogenesis is a multistep error-prone process that is actively monitored by quality control mechanisms. How ribosomal RNA synthesis, pre-rRNA processing and nucleolar surveillance are integrated is unclear. Nor is it understood how defective ribosomes are recognized. We report in budding yeast that,  Ribosome synthesis is a complex pathway that involves hundreds of individual error-prone reactions  or in its RNA recognition motif   . In partactivity . We noteractions .3 in the internal transcribed spacer 1 (ITS1), is known to strongly accumulate in strains inactivated for RRP6 . However, interestingly, only the nab3-11 mutation impacted strongly pre-rRNA processing, not the nrd1-101 or nrd1-102 mutations (compare lanes 3 and 4 in panel I in The 0–A2 (reviewed in Nrd1 and Nab3 have previously been shown to stabilize RNA Pol II transcripts originating from the rDNA intergenic sequence (IGS) and UV cross-linking has recently established that Nrd1 and Nab3 directly interact with these short-lived RNAs, as well as with RNA Pol I and RNA Pol III transcripts . AltogetDRB sensitivity-inducing factor, or DSIF, in humans), which interacts physically and genetically with RNA Pol II and is required for RNA Pol II elongation  and in vivo (Rpa135 and Rpa190), and to colocalize to the rDNA , consisting of 15 repeats of the hexamer S-T/A-W-G-G-A/Q, somewhat reminiscent of the RNA Pol II CTD . This doin vivo  and whether Spt4 is present or not . In addition, cells expressing a version of Spt5 truncated of its CTR were analyzed. Analysis of RNA steady-state levels indicated that strains defective for either component of the Spt4-Spt5 complex accumulated most of the pre-rRNA intermediates, starting with the accumulation of the primary transcript (35S) and early nucleolar precursors (33/32S) .Previous analysis established that cells deleted for kinetics , we were(33/32S)  and S4. vely see . At 37°Cspt4 and spt5 mutants there is a disconnection between the pre-rRNA processing and surveillance machineries leading to some stabilization of the RNAs. This view is further supported by the observation that deletion of SPT4 enhances the accumulation of several polyadenylated pre-rRNA precursors detected in rrp6 mutants  and aberrant truncated rRNA transcripts (e.g. 17S' and *), known to be polyadenylated by TRAMP5  from a repressor to a positive regulator of transcription . In yeasWhile several ribosome surveillance mechanisms have recently been described, an outstanding question in the field has remained to understand how defective ribosomes are recognized. Our model postulates that the Nrd1-Nab3 complex contributes to selecting unfaithful pre-ribosomes by binding to cryptic sites on nascent pre-rRNA transcripts and recruiting TRAMP and the RNA exosome.nrd1-101, nrd1-102 and nab3-11 temperature-sensitive alleles have been described . The escribed . Tempera600 0.8, fixed with 750 µl of 37% formaldehyde (Sigma) for 15 min at 25°C. For the Spt4–Spt5 complex, and respective controls, cells were fixed for 20 min, as described in Yeast cells were grown in 25 ml YPD at the appropriate temperature to OD600. Total RNA  was used to confirm that the fusions had the expected size. For ChIP control, primary antibodies were used at 1∶1000 dilutions in PBS-3% milk and revealed with the supersignal west pico chemiluminescent kit (Pierce).Total protein was extracted from 20 cell ODotal RNA  and S2 aotal RNA , S2B–C w+ RNA were either analyzed on 1.2% agarose/6% formaldehyde and 6–8% acrylamide-urea gels. Oligonucleotides used in the hybridizations are listed in RNA extraction and Northern blotting were as described previously in + RNA purification used 500 µg of total RNAs and the Poly(A)tract mRNA isolation system IV (Promega).Poly(A)All quantitations were performed on a Fuji phosphoimager FLA-7000 and used the Multi Gauge v3.1 native software.600 0.5. The yeast cells were lysed with glass beads and total proteins was extracted with 1 ml of IP buffer . Immuno-precipitation was performed with 400 µl of total protein and 40 µl of IgG-coated magnetic beads , previously washed three times with PBS-0.1% BSA and resuspended in PBS-1% BSA, for 2 h at 21°C and at 1.3 krpm in a Thermomixer (Eppendorf). The beads were washed once with 400 µl IP buffer and three times with IP buffer supplemented with 150 mM NaCl. The elution was performed for 20 min at 65°C in elution buffer .Yeast cells expressing suitable epitope-tagged constructs were grown in YPD at 30°C and 250 ml were harvested at ODReverse transcription reactions were performed with Superscript II (Invitrogen), according to the supplier's instructions, using 1 µg of total RNA previously treated by DNase I (Fermentas). Real-time PCR used the Platinum SYBR Green qPCR SuperMix-UDG with Rox (Invitrogen) on Applied Biosystems hardware. The qPCR datasets were analyzed using the ΔΔCt method. All results were normalized to ACT1. Primer sequences for PCR are provided on-line in Figure S1Mapping Nrd1 and Nab3 consensus binding sites bioinformatically onto yeast pre-rRNA. The sequence encoding the large RNA Pol I transcript in budding yeast was analyzed for the presence of Nab3 and Nrd1 consensus binding motifs with a dedicated software package . Randomizing the yeast rRNA sequence significantly reduced the number of hits for Nab3 binding sites and had no effect on the number of Nrd1 hits. There were three times less Nab3 hits in bacterial and archaeal sequences than in yeast; the number of putative Nrd1 sites was unaffected.(TIF)Click here for additional data file.Figure S2The thermoinactivation of Nrd1 or Nab3 leads to GRE1 readthrough transcript stabilization. A, Genomic structure of the locus used for readthrough transcript detection. The enlarged open arrow represents a cryptic unstable transcript (CUT) promoter, the closed arrows are promoters of adjacent genes  Validation of the nrd1 inactivation by detection of 3′-extended readthrough products. Readthrough transcripts are not normally detected in wild-type strains but they are stabilized upon exosome inactivation (lanes 5–6) or when their termination is compromised  is stabilized. (right) Western blot analysis of Nrd1-HA steady-state accumulation in different mutants grown at different temperatures. Total protein was extracted from the same cultures as those used in the RNA analysis presented in C,nrd1-101 analysis. Legend as in panel B. D,nab3-11 analysis. Legend as in panel A. E, Legend as in panel B.enes see . Transcr(TIF)Click here for additional data file.Figure S3Yeast pre-rRNA processing pathway. A, rDNA unit and probes used in this work. A single large RNA Pol I transcript (35S) encodes three out of the four ribosomal RNAs. The coding sequences for the 18S, 5.8S and 25S rRNAs are flanked by the 5′- and 3′-external (5′- and 3′-ETS) and internal transcribed spacers 1 and 2 (ITS1 and ITS2). Cleavage sites (A0 to E) and the oligonucleotides, used in the Northern-blot hybridizations are indicated. The fourth rRNA (5S) is synthesized independently by RNA Pol III (not represented). B, Pre-rRNA processing pathway. The 35S RNA is initially cleaved at sites A0–A2 by the SSU-processome. The resulting 20S and 27SA2 pre-rRNAs are destined to the small and large subunit, respectively. The 20S pre-rRNA is exported to the cytoplasm where it is converted into 18S rRNA, following 3′-end endonucleolytic cleavage at site D by Nob1. The 27SA2 pre-rRNA is matured following two alternatives pathways resulting in the production of two forms (short and long) of 5.8S rRNA that differ in size by about 7 nucleotides at their 5′-ends. In the major pathway (representing ≈80% of molecules), 27SA2 is endonucleolytically cleaved at site A3 by RNase MRP, and digested to site B1S by the exoRNases Rat1-Rai1 and Rrp17. In the minor pathway (≈20%), the 27SA2 is cleaved endonucleolytically at site B1L by an unknown activity. Both forms of 27SB pre-rRNAs are cleaved at site C2 within ITS2, generating the 7S pre-rRNAs, precursors to the 5.8S, and the 26S pre-rRNA, precursor to the 25S rRNA. The 7S pre-rRNA is digested to site E, corresponding to the 3′-end of 5.8S, by an extremely complex succession of reactions involving the core exosome, the nuclear specific subunit exosome sununit Rrp6, Ngl2 and the Rex exoRNases. The final steps of 5.8S 3′-end formation occurs in the cytoplasm. The 26S pre-rRNA is digested to site C1, the 5′-end of the 25S rRNA, by Rat1. In fast growing cells, up to 70% of transcripts are cleaved cotranscriptionally in ITS1 (not represented). C, Aberrant pre-rRNA precursors. In normal conditions, cleavage at sites A0–A2 is tightly coupled and largely occurs prior to cleavage at site A3. Under perturbed conditions, pre-rRNA processing kinetics might be altered, leading to premature cleavage at A3 and partial or complete uncoupling of cleavages at A0–A2. As a result, aberrant RNAs, such as the 21S and 23S, are generated. The 23S extends from the transcription start site to A3. The 21S RNA extends from site A1 to A3. The 17S', previously described in 3. How the 5′-end of 17S' is generated is currently unclear. One possibility is that it corresponds to a strong secondary structure that impedes exoribonucleolytic digestion; alternatively it might directly result from an endonucleolytic cleavage at a cryptic site by salient cellular RNases.(TIF)Click here for additional data file.Figure S4Quantitation of RNA ratio of Northern blots presented in .(TIF)Click here for additional data file.Materials and Methods S1Strains and oligonucleotides used in this study.(DOC)Click here for additional data file.


The demands of microarray expression technologies for quantities of RNA place a limit on the questions they can address. As a consequence, the RNA requirements have reduced over time as technologies have improved. In this paper we investigate the costs of reducing the starting quantity of RNA for the Illumina BeadArray platform. This we do via a dilution data set generated from two reference RNA sources that have become the standard for investigations into microarray and sequencing technologies.We find that the starting quantity of RNA has an effect on observed intensities despite the fact that the quantity of cRNA being hybridized remains constant. We see a loss of sensitivity when using lower quantities of RNA, but no great rise in the false positive rate. Even with 10 ng of starting RNA, the positive results are reliable although many differentially expressed genes are missed. We see that there is some scope for combining data from samples that have contributed differing quantities of RNA, but note also that sample sizes should increase to compensate for the loss of signal-to-noise when using low quantities of starting RNA.The BeadArray platform maintains a low false discovery rate even when small amounts of starting RNA are used. In contrast, the sensitivity of the platform drops off noticeably over the same range. Thus, those conducting experiments should not opt for low quantities of starting RNA without consideration of the costs of doing so. The implications for experimental design, and the integration of data from different starting quantities, are complex. The introduction of Illumina BeadChips with a standard labelling reaction requiring only 250 ng of total RNA made analysis of some previously unconsidered sample types possible; e.g. limited clinical samples or samples requiring considerable microdissection and pooling.Gene expression microarrays have become a routine analysis tool; from their introduction  to recenhttp://www.illumina.com/technology/direct_hybridization_assay.ilmn. If one can indeed use so little starting material then this is of tremendous importance in terms of the scope of experiments that become possible. However, there is a wealth of literature that is based upon 250 ng, and it is important that future results are comparable to those obtained previously. One small comparison has previously been made [Whilst many researchers continue to push the limits of starting materials , developeen made . This stMicroarray dilution experiments ,12, wherHere, we examine the costs and consequences of reducing the amount of starting RNA, with consideration for the issues of experimental design and meta-analysis, while also providing a unique bead-level dilution experiment to serve as a public resource to the Illumina-using community. We use the Illumina HumanWG-6 V3 BeadArray, analysed at the bead-level as we have previously recommended . One of Six samples can be hybridized to the Illumina HumanWG-6 V3 chip, each sample on two array-sections of approximately 1, 000, 000 beads that are distributed amongst approximately 50, 000 bead-types. We treat the two sections as separate arrays for the purposes of analysis, due to previously observed inter-section differences ,15. ThisWe have used two reference RNA samples, previously employed in the MAQC study , which hThese nine levels allow for investigation of broad trends, and for the detection of subtle differences. Combined with the four levels of starting material that we are investigating, this leads to 36 samples to be arranged across six Illumina HumanWG6 V3 BeadChips. Clearly it would not be desirable to confound levels of starting material with BeadChips as we would be unable to distinguish our comparison of interest from technical variation. However it is desirable that our data resemble data from a 'real-world' experiment else they have no external validity and, in general, experiments are conducted on BeadChips using only one level of starting material.Our design was chosen to address this tension between internal and external validity. Each BeadChip was run with samples from two starting quantities of RNA (three samples from both chosen starting quantities), and each possible combination of the two starting quantities was run once and only once amongst the six BeadChips. Full details of the design are given in Section 1 of Additional File μl and the dilution series was created to the specifications given above. The minimum pipetting volume used was 10 μl.Stock UHRR tubes were prepared following manufacturer's recommendation and pooled to create a stock of 1 mg/ml; Brain RNA was received at 1 mg/ml. The quality was checked using the Agilent Bioanalyser. The RNA was accurately diluted to a working stock of 100 ng/The Illumina TotalPrep-96 Kit (4397949) was used to process the samples using the range of input concentrations in question. For the 50 ng and 10 ng input quantities a 1:10 dilution of working RNA was used. Quality and quantity of the cRNA was checked before proceeding with hybridisation to Human WG-6 V3 BeadArray. The Illumina WGGX DirectHyb Assay Guide (11286331 RevA) protocol was followed for hybridisation, washing and scanning of the BeadArray, with the scanner set to return bead-level data  data from random arrays, the files are available to download from our website at A previously proposed  non-linemrp Eis the observed  log-intensity for probe p at starting RNA quantity r in mixture level m, m cis the proportion of the mixture that is UHRR, rp Uis the intensity associated with probe p at starting RNA level r in the UHRR sample, and rp Bis similarly defined for the Brain sample. The mrp ϵare independent measurement errors with mean zero and standard deviation rpσ.where This model implicitly assumes a linear relationship between quantity of RNA and measured intensity. This assumption is known not to hold over the full range of observed intensities for microarrays , and sperp = rp U- rpB,The model can be rewritten in terms of Δnls function, weighting each observation by the number of beads that contributed to the observation. Under this formulation, it is clear that the test of Δ = 0 from the summary.nls function in R provides an approximate and quick test of a difference in log-intensities.and we fit this model in R using the We have re-annotated the bead-types on the array , and havThe random assembly of Illumina arrays is often a virtue, but prevents the conduct of true replicate experiments. In particular, the number of usable beads on each array can vary, and will influence performance. There are a number of reasons why disparities emerge. Not all beads are decoded by Illumina when the array is manufactured, . Further beads are 'lost' due to spatial artefacts and to beads being classed as outliers during summarization. The numbers in our experiment are given in Table It has been observed previously that spatial artefacts can be associated with nearby regions where beads are non-decoded , so it mAs noted above, we restrict analyses to an analysis-group containing only 'perfect' bead-types, with desirable GC composition and at least six beads on each array-section. This reduces the number of bead-types considered to 21, 627. This also has a marginal effect on improving the balance between experiments in terms of the numbers of beads analysed.In Table Naturally, any bead-type that shows expression in both Brain and UHRR should show expression in all mixtures of those two samples, and we see that the proportion of bead-types satisfying the former that are also returned by the latter exceeds 80% for the 250 ng, 100 ng and 50 ng experiment but reduces to below 70% for the 10 ng experiment. Agreement between experiments is reported in Table The detection p-values for expression depend on the performance of negative control bead-types for their calculation. This platform has 759 negative control bead-types, which should have no match to the human transcriptome. Due to the nature of the calculation, at least seven (1%) of these will themselves apparently detect significant expression. Table Such observations could have explanation other than the bead-types showing specific signal. For instance, thermodynamic variation could lead to some negative control bead-types regularly being called as 'detected', but evidence of differential expression is harder to explain. Using Benjamini-Hochberg control for false discovery rate, there are still three negative control bead-types for the 250 ng starting material experiment  that show differential expression. The greatest evidence of a negative control showing specific hybridization is for bead-type ILMN_1343923  and the negative control bead-types, suggesting that the levels of non-specific hybridization vary according to the amount of starting material (Table The log-intensity levels for the housekeeping control bead-types decrease at a greater rate than those for the negative control bead-types (except when saturation effects are apparent). Thus the log-fold-change in intensities from housekeeping gene to negative control  decreases with the amount of starting RNA. This change in performance is apparent even at 100 ng levels of starting material. Other control bead-types on the Illumina BeadArray platform are not sample dependent, and do not vary considerably between starting quantities of RNA.Figure It is also clear from Figure p < 0.001, for the non-linear model), decreases with the amount of starting material much as did the number for which expression was detected  is high with a drop-off only when 10 ng of starting RNA are used, and the FDR (defined as for expression detection) remains low across all quantities of starting RNA Table . If we bThe variance of observations is not independent of their value. Since expression levels decrease as the quantity of starting RNA decreases, it is not possible to assess the change in variance as the quantity of starting RNA decreases, without simultaneously considering the level of expression.From the non-linear model we can compare the estimate of the difference in expression levels to the estimated standard error of the difference. This side-steps the complications of the variance and fluorescence levels changing in a dependent manner as the amount of starting material changes. Considering only the analysis-group of bead-types, the median ratios of standard error to estimate are 0.23, 0.28, 0.31 and 0.52 for 250 ng, 100 ng, 50 ng and 10 ng of starting RNA respectively. The median ratios of the two signal to noise ratios are 1.12, 1.16 and 1.76 for 100 ng, 50 ng or 10 ng respectively comparing to a reference starting quantity of 250 ng.Inevitably, there will be occasions when we wish to combine data sets generated using different quantities of starting material, possibly because we are performing a meta-analysis across different experiments, or possibly because not all samples within a single experiment can supply the desired quantity of starting RNA. Our analysis has, so far, considered the different quantities of starting material in this study as being different experiments, but we will now briefly consider strategies for combining them.Consider if samples were run in strata of starting RNA, e.g. we have an experiment where some samples were run using 250 ng, while others were run using 50 ng. The strata were not balanced in terms of experimental design, so we may not wish to obtain two simple estimates for the parameters of interest (one from each stratum) and then combine the estimates. Our strategy for analysis may depend on whether some samples had been run in both strata.Consider further that we only have Brain run at 50 ng and UHRR at 250 ng, and we wish to transform the 50 ng Brain data for comparison with UHRR. Essentially we wish to simulate a 250 ng Brain data set from this restricted data set, and can use the fact that we do have Brain run at 250 ng to assess the performance. We will consider both the scenario where we have only the two samples with which to work, and a second where we have additionally run UHRR at 50 ng.If we are in this first scenario, then there is little option but to normalize between the samples. The high rank correlation we have observed between data arising from different starting amounts of RNA gives cause for optimism that a simple quantile-style normalization of the 50 ng data to the expression profile of the 250 ng data will prove successful. With data available from samples run at both starting levels, we can use the 50 ng UHRR and 250 ng UHRR samples to estimate the bias due to starting RNA quantity  and can then project the 50 ng Brain sample with that model to obtain our prediction for how a 250 ng Brain sample would look. Such an approach shows a marginal improvement over the basic attempt in our example Figure .Using the additional data (50 ng UHRR) makes only a small improvement to our ability to transform the 50 ng Brain data and in a real experiment running 50 ng of an additional sample may provide greater value to the ultimate analysis. We should be wary of trying to use a sample for both bias-estimation and analysis as there will be a lack of independence between these samples and all those that are bias-corrected using the results. Moreover the small improvement we see here, over the simpler quantile-normalization style approach, comes using samples that have large numbers of expressed genes. For bias correction of this nature to be useful, we need to observe a wide range of log-intensities which in turn requires large numbers of genes to be expressed. Thus the appropriateness of this more complicated method will be dependent on the size of the experiment and the nature of the samples being hybridized.A number of implications for experimental design are obvious. It is clear that all things being equal, of the range of starting quantities of RNA considered here, it is preferable to use 250 ng. If there are limitations to the amount of starting RNA available, then the more starting material used the better (within this range examined). Should the amount of available RNA differ between samples then more subtle decisions are required. On the basis of the signal-to-noise results, we can infer that if using 100 ng or 50 ng then the sample size would need inflating by a factor of at least 1.2 to achieve the same performance, while if using 10 ng, then in the region of three times the numbers will be required. Thus, when we have the choice and free from other pressures, reducing the starting RNA level is only desirable if it allows sample numbers to be increased by these factors.The combination of multiple starting RNA levels in one experiment will be problematic. If we wish to normalize using data from the same sample hybridized from multiple quantities of starting RNA, then clearly we must stratify samples into a few starting quantities. If we do not have, or do not wish to make, recourse to replicate samples hybridized from several RNA quantities, and are simply going to normalize samples together, then there is merit in using as much starting RNA as possible for each sample, as was noted in the previous section.In this scenario, where all samples are independent, it would still be hard to criticize a design that opted for a fixed number of starting levels, especially if this came at minimal cost to quality (i.e. 250 ng reduced to 220 ng but not to 10 ng) and allowed balance of experimental criteria to be achieved within each stratum of starting quantity. Such an approach is suboptimal by our criterion, but may be more robust to those unexpected events that befall real-world experiments.We have presented a bead-level Illumina BeadArray dilution control experiment that will be a valuable resource for the Illumina analysis community. As intended, the experiment also answers an important experimental question regarding the required levels of starting RNA, however it also allows for a number of questions to be addressed regarding experimental design when large quantities of RNA are difficult to obtain.We have shown that reliable signal is obtainable using as little as 10 ng of starting RNA. However we have also seen that lower levels of starting RNA are associated with a bias in expression levels (which may be correctable), and drop in sensitivity (which will not be).This increase in noise implies that, if using less starting RNA, more samples would be needed in an experiment to achieve the same levels of precision. However, it seems that few false discoveries result from using even as little as 10 ng of starting RNA. Thus while a small experiment using a low starting quantity of RNA may fail to identify many subtle changes, one can have confidence in any changes that are reported.AGL finalized the design of the experiment, performed the analysis, and drafted the manuscript. JH supervised the experiment and drafted the manuscript. MJD participated in the design of the study, participated in the array data processing and provided Illumina expertise. MO conducted the experiment. NPT and ST participated in the conception and design of the study. All authors read and approved the final manuscript.Supplementary material. File giving details of 1) Experimental Design: Array Layout, 2) Lab Methods: Obtaining bead-level data, 3) Lab Methods: Quality assessment metrics, 4) Lab Methods: Quality assessment - comparison with MAQC, 5) Lab Methods: Quality assessment - Association between starting RNA quantity and intensity, 6) Criteria for including bead-types, 7) Results: Detection, 8) Results: Negative controls, 9) Results: Differential expression but no expression, and 10) Results: Differential expression - detection of small changes.Click here for file


Adenosine-to-inosine (A-to-I) RNA editing is recognized as a cellular mechanism for generating both RNA and protein diversity. Inosine base pairs with cytidine during reverse transcription and therefore appears as guanosine during sequencing of cDNA. Current approaches of RNA editing identification largely depend on the comparison between transcriptomes and genomic DNA (gDNA) sequencing datasets from the same individuals, and it has been challenging to identify editing candidates from transcriptomes in the absence of gDNA information.We have developed a new strategy to accurately predict constitutive RNA editing sites from publicly available human RNA-seq datasets in the absence of relevant genomic sequences. Our approach establishes new parameters to increase the ability to map mismatches and to minimize sequencing/mapping errors and unreported genome variations. We identified 695 novel constitutive A-to-I editing sites that appear in clusters (named “editing boxes”) in multiple samples and which exhibit spatial and dynamic regulation across human tissues. Some of these editing boxes are enriched in non-repetitive regions lacking inverted repeat structures and contain an extremely high conversion frequency of As to Is. We validated a number of editing boxes in multiple human cell lines and confirmed that ADAR1 is responsible for the observed promiscuous editing events in non-repetitive regions, further expanding our knowledge of the catalytic substrate of A-to-I RNA editing by ADAR enzymes.The approach we present here provides a novel way of identifying A-to-I RNA editing events by analyzing only RNA-seq datasets. This method has allowed us to gain new insights into RNA editing and should also aid in the identification of more constitutive A-to-I editing sites from additional transcriptomes. RNA editing is a post-transcriptional modification process which not only expands the number of functions encoded by our genomes but also provides additional mechanisms of gene regulation. The most predominant form of such editing in higher eukaryotes is adenosine-to-inosine (A-to-I) RNA editing, which is catalyzed by members of ADAR enzyme family (adenosine deaminases that act on RNA) ,2. The r2CR) [The pattern of A-to-I RNA editing, either site-specific or promiscuous, is likely to determine the fate of an edited RNA molecule. The majority of A-to-I editing in the human transcriptome is located within inverted-repeated Alu elements (IRAlus) positioned within introns and UTRs as revealed by the systematic comparison of cDNA or EST libraries to genomic sequences -7, and b2CR) ,16. In a2CR) -22. ThusInosine base pairs with cytidine during reverse transcription and therefore appears as G during sequencing of cDNA. Thus, A-to-I editing sites can be inferred by the presence of G at a given position in a cDNA sequence but only A in the corresponding genomic position ,2. Most Transcriptome and genomic DNA sequencing datasets are not always available for single individuals. However, RNA-seq data is widespread and available through public datasets and thus represents a relevantly rich source of yet unexplored RNA editing sites. There are two features that currently limit the application of RNA-seq data to identify A-to-I RNA editing without the relevant genomic information. On one hand, the nature of nucleotide mismatches reduces the ability to uniquely align RNA-seq reads to the genome, and therefore reduces the capability to retrieve nucleotide variants. On the other hand, true editing events are often hidden in a background noise caused by sequence errors, mapping errors and genome variations, including genomic single nucleotide polymorphisms (SNPs) and somatic mutations. Thus, it has been challenging to accurately identify editing candidates from transcriptomes in the absence of gDNA information.To overcome the aforementioned issues, we have developed a new pipeline to accurately predict editing sites from 18 human RNA-seq datasets, even without knowledge of relevant genomic sequences from which the RNA-seq data were derived. We identified 2,245 constitutive A-to-I editing sites that occur in clusters (named “editing boxes”). Some of these are enriched in non-repetitive elements and exhibit an extremely high A-to-I conversion frequency. Importantly, editing sites located in non-repetitive editing boxes were validated in multiple human cell lines using conventional PCR and Sanger sequencing and were proven to be catalyzed by ADAR1. Finally, distinct editing ratios of RNA sites in editing boxes from different tissues/cell lines clearly suggest a spatial and dynamic regulation of A-to-I RNA editing across human tissues.It has been challenging to discover A-to-I RNA editing sites from RNA-seq datasets for a number of reasons. First, edited As are interpreted as Gs in sequencing reads. This leads to problems with alignment of edited reads to the genome. Second, random sequencing errors and mapping errors are often problematic. Third, some genomic polymorphisms and somatic mutations are unpredictable from an individual genome without knowledge of the genomic sequence. Finally, transcriptome and genomic DNA sequencing datasets are not always available for single individuals. To overcome these difficulties, we have developed a computational approach consisting of four key steps Figure  to identSTEP 1: a two-round unique mapping strategy with Bowtie to improve the mapping ability and to obtain an increased number of aligned mismatches. Multiple mapping pipelines have been developed to align individual RNA-seq reads to the corresponding genomes -26. HoweSTEP 2: a series of stringent cutoffs to reduce sequencing/mapping errors and to remove known genomic SNPs. As different samples vary in genome coverage and sequencing depth, we used the HPB value  in predicting clustered A-to-I editing sites, we also investigated the performance of this method on editing sites that are not clustered Table . HoweverSince this work is the first demonstration of promiscuous editing in non-repetitive regions catalyzed by ADAR1 Table , we furtTo further test this possibility, we cloned sequences of editing boxes in 3′UTR of egfp or in the upstream region of single Alu or IRAlus in 3′UTR of egfp Figure C. We havAs 2,245 constitutive A-to-I sites could be found in multiple human tissues and cell lines, we were able to analyze the spatial and dynamic regulation of A-to-I RNA editing. Surprisingly, constitutive A-to-I sites in editing boxes are highly dynamic across human tissues at two levels. On one hand, individual sites exhibit distinct patterns of editing across human tissues and cell lines Table . On the Taken together, we have developed an approach to quantitatively profile constitutive A-to-I RNA editing from multiple human transcriptomes in the absence of the relevant genomic information. The application of our approach has allowed us to identify a large number of clustered constitutive A-to-I sites, including 695 novel sites. Our analysis also revealed that non-repetitive editing boxes could be promiscuously edited by ADAR1, independent of their adjacent IRAlus. Finally, although functionally unknown, marked differences of editing ratios in the same sites identified in editing boxes clearly suggest a spatial and dynamic regulation of A-to-I RNA editing across human tissues.RNA-seq datasets, widespread through currently available public databases, are rich sources to search for A-to-I RNA editing sites. However, RNA-DNA mismatches between RNA-seq reads and the genome make the alignment of nucleotide variations to the genome problematic. In addition, transcriptome and genomic DNA sequencing datasets are not always available for single individuals, thus making straightforward prediction of A-to-I editing sites from available transcriptomes even more challenging. In this study, we developed a new computational approach to predict RNA editing from multiple tissues in the absence of the genome information. An additional 695 novel A-to-I editing sites have been identified compared to several other recent studies -10,27 anet al. also reported the identification of edited sites from transcriptome data only [Very recently, Ramaswami ata only . Their mata only  and sligata only . In our ata only  althoughSince we focused on clustered A-to-Is which are constitutive edited in at least three human tissues/transcriptomes, limited editing sites were identified in this study. It is also noteworthy that some limitations exist in this approach, including the insufficiency to predict more restricted tissue-specific editing, the inadequacy to identify some true editing sites with 40-60% or >95% editing ratios, and inaccuracies in identifying non-clustered editing sites  and two additional cell lines sequenced by Illumina Genome Analyzer IIx (GAIIx)  were retA two-round-unique mapping strategy with Bowtie , SOAP 88, or BWAHits Per Billion-mapped-bases (HPB) > 5. Since multiple RNA-seq datasets with different sequencing depths were used in this study, we developed HPB to normalize the expression level for each base across samples, and selected a HPB > 5 for each mismatch site  and 1000 Genome database .As the strand information of these RNA-seq datasets was not available, we referred plus strand of (“+”) chromosomes as reference for mismatch calling. In addition to trim 75-nt reads from both ends to 70-nt, we carried out the following stringent criteria for mismatch calling: (i): Each mismatch site must have a PSS was set up to further reduce unknown genomic noise by taking advantage of multiple human tissue RNA-seq datasets. Notably, most mismatches showed low ratios (< 20%) from multiple human tissues, while some showed high mismatch ratios >60%) Figure . In cont0% FigureMismatch sites were selected using the following criteria: (i) predicted editing sites were constitutively transcribed at least from three human tissues/cell lines; (ii) each site is no longer than 50 bp away from the nearest site and the minimum transcribed genomic region is 20 bp long; (iii) Each site has a greater than 20% mismatch rate in at least one tissue; (iv) at least 5 mismatch sites clustered in one region with at least 20% conversion rate for each type of nucleotide. Thus, We named these regions containing promiscuous edited A-to-I sites as “editing boxes”.http://darned.ucc.ie/) and different studies [http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/refFlat.txt.gz). Alu and non-Alu repetitive elements were retrieved from http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/rmsk.txt.gz. IRAlus were defined as any two or more inversely oriented Alu elements located within two kilobases in their genomic location [Previously identified editing sites were retrieved from the RNA editing database  were maintained as described before . Sequenchttp://rsb.info.nih.gov/ij/index.html). Briefly, the areas of edited and unedited signals, indicated as the signal intensities at each site, were carefully selected and measured by “ImageJ”. The editing ratio was then calculated by dividing edited intensity with total intensity at the same site. Correlation of editing ratios calculated from Sanger sequencing and RNA-seq were determined by scatter plot.Total RNAs from HeLa, ADAR1 knockdown HeLa cells, transfected HeLa cells, and H9 cells were extracted with Trizol Reagent (Invitrogen) according to the manufacturer’s protocol. After treatment with DNase I , the cDNA was transcribed with SuperScript II (Invitrogen) with oligo (dT) or random hexamer. Genomic DNAs were purified from both cell lines by TIANamp Genomic DNA kit (Tiangen Biotech). PCR products from cDNAs and gDNAs were amplified with primers  with minor modifications. Briefly, after enriched by oligo-dT selection, poly(A) + RNAs were fragmented, and treated with phosphatase and polynucleotide kinase to repair the ends. RNA adapters were sequentially ligated to the 3′ and 5′ ends of RNA fragments and reverse transcribed using a primer complementary to the 3′ linker. cDNA library was then amplified and sequenced on HiSeq2000 with 1x100 bp reads. The sequence file can be accessed from the NCBI Sequence Read Archive by GEO Accession Number GSE44450.We present an integrative approach to quantitatively profile constitutive A-to-I RNA editing from multiple human transcriptomes in the absence of the relevant genomic information. The application of our approach has allowed us to identify a large number of clustered constitutive A-to-I sites, including 695 novel ones. We further demonstrated that non-repetitive editing boxes could be promiscuously edited by ADAR1, independent of their adjacent IRAlus. Strikingly, clear differences of editing levels in the same editing box sites but from different tissues/cell-lines were also observed, strongly indicating a spatial and dynamic regulation of A-to-I RNA editing across human tissues. Our work thus offers new insights into the catalytic pattern and complex regulation of A-to-I editing by ADAR1.EB: Editing box; ESC: Embryonic stem cell; FPKM: Fragments per kilobase per million; gDNA: Genomic DNA; gSNP: Genomic SNP; HPB: Hits per billion-mapped-bases; RPKM: Reads per kilobase per million; PSS: Potential SNP score; SNP: Single nucleotide polymorphisms.The authors declare that they have no competing interests.LY and LLC conceived the study, analyzed data and wrote the manuscript from the inputs from all authors. SSZ and LY carried out the computational analyses, XJF and CT carried our all experiments. All authors have read and approved the manuscript for publication.Distribution of RNA-DNA mismatch ratios along the reads. Reads from 18 of human tissues/cell lines were uniquely mapped to human reference and all types of RNA-DNA mismatches were examined at each position of 75-bp reads. Each sample was shown with different color.Click here for fileNumbers of total reads used for alignment, mapped reads after the first- and second-round alignment for all transcriptomes from 18 tissues/cell lines.Click here for fileThe evaluation of the two-round mapping with other aligners, SOAP [(A) and BWA [(B). The two-round mapping approach achieved a significant increase of mapped mismatches (bottom panel) and subtle changes of mapped reads (top panel). The 1st-round mapping, light grey bars; the 2nd-round mapping, dark grey bars.rs, SOAP (A) and B and BWA (B). The Click here for fileA formula to show that normalized expression level  of a give site is equivalent to the value of RPKM/FPKM at one nucleotide base resolution.Click here for fileConstitutive A-to-I editing sites in editing boxes (see spreadsheet). 2,245 constitutive A-to-I editing sites in editing boxes were listed with their genomic location, expression levels in each tissue/cell line (> 5 HPB), unique hits, editing ratios and final PSSs. Each site was also characterized with information of the overlapped gene, strand information, overlapping with RepeatMask sequences, genomic location with IRAlus and comparison with other reported editing datasets  heterozygous or (B) homozygous sites, which were highlighted with arrows and were proven to be real genome variations. The reference genome sequences from hg19 human genome are listed with the variation sites underlined. (C) Additional eight examples of unannotated SNPs predicted with PSS were shown with genomic locations, types of nucleotide conversion, and PSS. All of them were validated by Sanger sequencing. Three heterozygous sites (A) were only in genome 1000 dataset, but not in UCSC SNP135. All other 11 homozygous site (B and C) were not reported by either dataset.Click here for fileTissue-specific expression of GluR-B in brain. (A) The well characterized A-to-I editing sites at chr4:158,281,294 and chr4:158,257,875 in the pre-mRNA of GluR-B were only detected in brain, with editing frequencies at 69.1% and 91.7%, respectively, as predicted with our computational flow. (B) The expression of GluR-B in all examined samples was retrieved from UCSC genome browser and the relative expression was listed with a normalized FPKM value for each sample. Note that GluR-B is highly expressed in human tissue but few if any in other samples.Click here for fileExpression of an intergenic region with two predicted editing boxes in all 18 samples. The expression of the intergenic region from chr2 along with its adjacent genes in all examined RNA-seq samples. The gene models, reported and predicted poly(A) sites, transcription level, and ENCODE epigenetic modifications of ChIP-seq data  were retrieved from UCSC genome browser. A new gene model of EIF2AK2 with extended 3′ UTR (red line) was drawn beneath the UCSC genome browser snapshot. Three editing boxes (two non-repetitive boxes in purple and one Alu box in pink) were indicated in the extended 3′UTR region of EIF2AK2. Note that editing boxes in this unannotated region were highly expressed in all examined samples.Click here for fileValidation of predicted A-to-I editing sites in other editing boxes. Predicted A-to-I editing sites were highlighted in red (novel sites) or black (reported ones). Predicted editing ratios were shown above each site in the cDNA sequencing chromatograms. Validation of some A-to-I editing sites from editing boxes at (A) chr2: 37,327,644-37,327,685; (B) chr12: 69,237,506-69,237,558; (C) chr14: 23,441,376-23,441,503. Editing ratios in chr12: 69,237,529 (B) were underestimated in our analysis compared with conventional Sanger sequencing, probably due to more mismatches in short fragments failed to map to reference genome. Note that predicted sites with low editing ratio were difficult to be validated due to the limited sensitivity of the Sanger method. (D) Scatter plot of editing ratios for 31 A-to-I editing events  identified by RNA-seq and Sanger sequencing method. Data points corresponding to false positive or false negative predictions were shown with red dots. R, R squares and P value for the linear regression (black line) indicated the relatively good correlation between these two methods.Click here for fileValidations of A-to-I sites in editing boxes with knockdown of adar1 in HeLa cells. Editing sites in regions chr2:37,327,656-37,327,685 (A) and chr12: 69,237,509-69,237,534 (B). Click here for fileEditing ratios in three editing boxes in chromosome 2 across human tissues/cell lines.Click here for fileThe distribution of mismatch ratios of known genomic SNPs and predicted mismatches in all 18 samples.Click here for filePrimer sets for PCR/RT-PCR, editing box cloning and Sanger sequencing validation. Same primer sets were used for genomic DNA and cDNA amplification unless addressed separately (−g for genomic DNA or -c for cDNA). Forward primers were chosen for Sanger sequencing. Primers for editing box cloning at ch2:37327479–37328193 region were also listed.Click here for file


Single-molecule experiments have shed new light on the mechanisms responsible for the movement of RNA polymerase along DNA during transcription. Related research article Dangkulwanich M, Ishibashi T, Liu S, Kireeva ML, Lubkowska L, Kashlev M, Bustamante CJ. 2013. Complete dissection of transcription elongation reveals slow translocation of RNA polymerase II in a linear ratchet mechanism. eLife2:e00971. doi: 10.7554/eLife.00971Image Schematic of the dual optical trap used to measure the translocation of RNA polymeraseeLife, Carlos Bustamante and colleagues describe how a combination of single-molecule measurements and theoretical modelling has provided important insights into these questions . For several decades, a variety of structural, biochemical and single-molecule studies have been used to probe the details of transcription, but many open questions remain. Specifically, what are the key steps in the process that converts chemical energy into the mechanical energy that is needed to move the RNA polymerase in the required direction, and how fast are these steps? Now, in uestions .The main reaction pathway for transcription has three basic steps . First, When developing theoretical models of the reaction pathway, researchers have generally assumed that the first two steps in the main reaction pathway—the translocation step and the NTP binding step—are much faster than the chemical catalysis step and may, therefore, be treated as though they are in equilibrium . HoweverThere are good reasons to believe that NTP binding is in equilibrium: in other words, the rates at which NTP molecules bind to, and unbind from, the RNA polymerase are much faster than the catalysis rate. However, it is not clear that the rates at which the RNA polymerase moves forward and backward are also much faster than the catalysis rate. Now, by questioning the assumption that the translocation step is in equilibrium, Bustamante and colleagues—including Manchuta Dangkulwanich, Toyotaka Ishibashi and Shixin Liu as joint first authors—argue that a secondary NTP binding site is not needed.Dangkulwanich et al. employed a dual-optical trapping technique : one optBased on these results, Dangkulwanich et al. generated an energy landscape for the reaction pathway and arrived at several important conclusions. First, they found that the forward motion of the RNA polymerase was much slower than previously assumed, and also much slower than the backward motion: the forward motion was faster than the catalysis step by a factor of only 2.5, whereas the backwards motion was faster by a factor of 20. Since such a simple model with the translocation steps in the main reaction pathway not being in equilibrium could explain the force-velocity results, Dangkulwanich et al. argue that there is no need to invoke a more complex model with a secondary NTP binding site. Second, they found that backtracking by one base pair is much easier than backtracking by two or more base pairs.E. coli RNA polymerase without assuming that the translocation step was in equilibrium (E. coli plays a similar role in modelling as the one-base-pair backtracking state does for Pol II in yeast.This new model for Pol II bears some resemblance to a model that Andrei Ruckenstein’s group at Rutgers University developed for ilibrium . Both moE. coil RNA polymerase (The work by Bustamante and colleagues—who are based at the University of California Berkeley, the Lawrence Berkeley National Laboratory and the National Cancer Institute Center for Cancer Research—has now brought the importance of kinetic measurements to the forefront. However, more work is needed to work out all the details of the coupling between the chemical and mechanical processes. In particular, this latest Pol II model does not consider the effects of the sequence of bases, even though the elongation velocity is expected to greatly depend on sequence: indeed, if sequence-dependent kinetics is included in a simple model that assumes that the translocation step is in equilibrium, the force-velocity curve predicted by the model agrees with that observed in experiments on lymerase .Comparing the various models of transcription will require sequence-dependent measurements that are capable of directly imaging translocation events. Luckily, it appears that advances in single-molecule optical trapping and fluorescence techniques will make such measurements possible in the years ahead.


T. brucei. MRB11870 ablation causes a massive defect in RNA editing, affecting both pan-edited and minimally edited mRNAs, but does not substantially affect mitochondrial RNA stability or processing of precursor transcripts. The editing defect in MRB1-depleted cells occurs at the initiation stage of editing, as pre-edited mRNAs accumulate. However, the gRNAs that direct editing remain abundant in the knockdown cells. To examine the contribution of MRB11870 to MRB1 macromolecular interactions, we tagged core complexes and analyzed their composition and associated proteins in the presence and absence of MRB11870. These studies demonstrated that MRB11870 is essential for association of GAP1/2 with the core, as well as for interaction of the core with other proteins and subcomplexes. Together, these data support a model in which the MRB1 core mediates functional interaction of gRNAs with the editing machinery, having GAP1/2 as its gRNA binding constituents. MRB11870 is a critical component of the core, essential for its structure and function. Uridine insertion/deletion RNA editing is a unique and vital process in kinetoplastids, required for creation of translatable open reading frames in most mitochondrially-encoded RNAs. Emerging as a key player in this process is the mitochondrial RNA binding 1 (MRB1) complex. MRB1 comprises an RNA-independent core complex of at least six proteins, including the GAP1/2 guide RNA (gRNA) binding proteins. The core interacts in an RNA-enhanced or -dependent manner with imprecisely defined TbRGG2 subcomplexes, Armadillo protein MRB10130, and additional factors that comprise the dynamic MRB1 complex. Towards understanding MRB1 complex function in RNA editing, we present here functional characterization of the pentein domain-containing MRB1 core protein, MRB11870. Inducible RNAi studies demonstrate that MRB11870 is essential for proliferation of both insect vector and human infective stage  Trypanosoma brucei is comprised of dozens of maxicircles and thousands of minicircles that are concatenated into a dense network  ATP (6000 Ci/mmol) using T4 kinase. Membranes were prehybridized in ULTRAhyb-oligo solution (Ambion) for 30 minutes and then hybridized with the probe overnight at 42°C. The membrane was washed 3 times for 30 minutes each with 20 mL of 4xSSPE, 0.5%SDS at 42°C and was exposed to a phosphor screen overnight. The screen was scanned with the Bio-Rad Personal Imager FX and the quantitation of the density of bands in northern blots was performed with the Quantity One software (Bio-Rad). The following 5’- 32P-labeled oligonucleotides were used:Quantification of the total gRNA population by guanylyltransferase labeling was performed as previously described . For norATAATTATCATATCACTGTCAAAATCTGATTCGTTATCGGAGTTATAGTATATgA6: ATAATTACATTCAATTACTCTAATTTAATTTTATTTTTGTGCgMURF2[II] gRNA (maxicircle encoded): GCGGATCCTTATTCCCTTTATCACCgCYb[558]: CCATCGCGACACGTTGTGGGAGCCGPre-5.8S rRNA:  PF cells expressing both the endogenous PTP-MRB3010 and the tetracycline-inducible MRB11870 RNAi constructs were either grown in the presence or absence of the tetracycline for 4 days. Expression of PTP-MRB3010 was verified by western blot using affinity purified rabbit anti-protein C (ICL). MRB11870 knockdown was verified by qRT-PCR at the RNA level as described above and by western blot with anti-MRB11870 antibodies . Whole cell extracts were nuclease treated and MRB3010 and its associated proteins were affinity purified by IgG Sepharose 6 Fast Flow chromatography and TEV protease cleavage as described previously . Input a