We integrated coral reef connectivity data for the Caribbean and Gulf of Mexico into a conservation decision-making framework for designing a regional scale marine protected area (MPA) network that provides insight into ecological and political contexts. We used an ocean circulation model and regional coral reef data to simulate eight spawning events from 2008–2011, applying a maximum 30-day pelagic larval duration and 20% mortality rate. Coral larval dispersal patterns were analyzed between coral reefs across jurisdictional marine zones to identify spatial relationships between larval sources and destinations within countries and territories across the region. We applied our results in Marxan, a conservation planning software tool, to identify a regional coral reef MPA network design that meets conservation goals, minimizes underlying threats, and maintains coral reef connectivity. Our results suggest that approximately 77% of coral reefs identified as having a high regional connectivity value are not included in the existing MPA network. This research is unique because we quantify and report coral larval connectivity data by marine ecoregions and Exclusive Economic Zones (EZZ) and use this information to identify gaps in the current Caribbean-wide MPA network by integrating asymmetric connectivity information in Marxan to design a regional MPA network that includes important reef network connections. The identification of important reef connectivity metrics guides the selection of priority conservation areas and supports resilience at the whole system level into the future. The rapid decline in coral reef health ,2,3,4 isClearly, more research is needed to find cost effective and meaningful pathways for incorporating ecological connectivity into MPA design . One of To address these challenges, we modeled larval dispersal across coral reefs in the Caribbean and Gulf of Mexico to identify important reef connections on a regional scale. Questions driving our research were: Following a spawning event, where do coral larvae go? Where is settlement and recruitment most likely to occur? How dependent are reefs within each jurisdiction on recruits from each local or upstream reef in other jurisdictions? Where are the key source sites of marine connectivity within the region and are they protected? In order to answer these questions, we modeled coral population connectivity based on a 30-day maximum larval dispersal period across eight spawning events from 2008–2011 using a spatially explicit connectivity model ,26. We uWe modeled coral connectivity and integrated the results into a conservation optimization algorithm to identify priority reef conservation areas within ten marine ecoregions that make up the Caribbean Basin, Gulf of Mexico and the southwest Sargasso Sea  . SeveralWe used coral reef data from the Millennium Coral Reef Mapping Project  as a conOcean current data were acquired from the NOAA Real-Time Ocean Forecast System (RTOFS) database . The RTOWe modeled the dispersal of reef building coral larvae for spawning events using a spatially-explicit larval dispersal model ,39,40. W In each simulation, the amount of larvae released was proportional to the reef area within each reef unit. For each simulation, we used a maximum pelagic larval duration (PLD) of 30 days [-1. Although the influence of mortality is well recognized [-1 to 10% day-1, with some corals experiencing up to approximately 35% day-1 mortality [-1 rate for illustration purposes.The dispersal model included the following parameters to quantify connectivity: time and frequency of spawning, pelagic larval duration, settlement behaviour, and larval mortality  serves as a stepping-stone in the shortest paths between all other pairs of nodes in the network. This measure can be used to identify important stepping-stones that facilitate connectivity in a network. Closeness centrality values are higher for a particular node when its total distance to all other nodes in the network is lower. This measure indicates how close a particular node is to the other nodes in the network.We used a centrality measure to represent conservation value. In our connectivity network, we transformed the edge weights using te et al  and Holste et al , we calcWe used Marxan (v. 2.42) ,50, a wiTargets are set by the user for each conservation feature under consideration for inclusion in the resulting set of priority areas. Marxan has several other inputs  that inform the algorithm about the cost of creating a potential set of priority areas from an input set of planning unit sites . Using tCost represents the sum of the costs associated with the planning units or sites that will comprise the selected conservation priority area. These can be the actual cost associated with acquiring the area, or opportunity costs, or some entirely different metric of cost as defined by the user.The Boundary represents the actual length of the boundary of the selected priority area. Using the actual length of the boundary allows Marxan to prioritize solutions that create reserve systems with smaller external boundaries. This has the effect of creating clumped priority areas which may be desirable in certain situations . Costs are sometimes alternatively used as “boundaries.” In this research we demonstrate the use of the strength of the connection between reefs as a boundary cost between reef units. In this way, as Marxan seeks to find potential priority areas by minimizing the external boundary, it clumps the priority areas based on the strength of the connections between reef units. In other words, a reef unit with a strong connection will be more likely to be selected than a disconnected reef, especially if has similar amounts of other costs and contributes similarly to meeting the user defined targets.The Penalty, often referred to as the “conservation feature penalty factor” is a term that allows the user to express a cost associated with a set of priority areas that do not meet the established targets. Increasing this value will cause Marxan to increase the total cost of the solution in the case where a target is not met for a set of priority areas. Thus, increasing a conservation feature’s penalty makes it more likely Marxan will select an output solution that will meet the target for that conservation feature. Finally, the Cost Penalty Threshold is a cost associated with exceeding the total user-defined cost for the potential set of priority areas. For this study, we applied a cost penalty value between 1.0–1.4 which allowed the algorithm to find a reasonably efficient solution.The t in the formula above. In practice, Marxan is run a number of times, each with different initial solution which may produce different final results. The primary output in this case, is the “best” solution over all the runs, which is the one that meet the targets and captures the lowest overall cost . Another commonly used output is the “sum of solutions” which will report the number of times each planning unit or site was included in the set of priority areas.Selecting a set of conservation priority areas is iterative and Marxan offers several algorithms that can be selected from to determine the process whereby the objective function is implemented. The factors in the objective function are affected by the makeup of the initially selected set, and then recalculated for the current selected priority areas for each iteration. Therefore, the solution is complex and the algorithm works towards a final solution that meets user-defined conservation targets while minimizing cost ─represented by In order to explore the differences between a typical Marxan analysis and one which includes connectivity data, we compare the results of Marxan using two scenarios: a) a typical scenario where conservation targets are set using reef area (per reef unit) and a boundary file that uses a transformation of Euclidean distance to reef units (i.e. nearby reefs effectively shared a larger common boundary); and b) a connectivity-based scenario where conservation targets are set using local retention and betweenness centrality (per reef unit) and a boundary file that uses the asymmetric strength of connection values calculated between reef units. We chose to use Euclidean distance in the first scenario because reef units rarely shared an actual boundary, and we needed a surrogate for boundary length in order to clump model results and provide a manageable output solution. To test the influence of using a boundary length modifier, a third scenario with a single 30% target of reef area was tested, but it didn’t use a boundary file. The results appeared to be driven solely by the reef units with the largest reef area and lowest cost, since Marxan was simply balancing the selection of reef units by reef area and cost.i.e. where targets can be met anywhere within the study area); and b) a multiple region stratification using the ten marine ecoregions (i.e. where targets are met within each marine ecoregion). For all scenarios, we set a 30% target and penalty factor of 10 for the conservation feature(s) being considered in each scenario and ran 100 repetitions with one million iterations each using the simulated annealing algorithm with iterative improvement, commonly used by Marxan users [We evaluated and compared results of the two scenarios using two stratification schemes: a) a single region strata  local retention of larvae; and b) betweenness centrality of reef units. Local retention of larvae is a measure of the proportion of larvae that were released that remain in the natal patch. Higher values of local retention suggest that a reef is more likely to be self-sustaining . Upon evThe Boundary Length Modifier (BLM) parameter is a multiplicative factor in the Marxan algorithm, which attempts to minimize the boundary length to area ratio, thus increasing the continuity of the solution set. High BLM values force the clustering of the solution set, whereas low BLM values allow for a more fragmented set to be selected as a model solution. Typically, boundary length often is represented by the measured length of the boundary between each pair of planning units that share a common border. In the Marxan selection algorithm, removing planning units that share a large common boundary will incur a greater cost than removing those that have a smaller common boundary or no boundary at all. In our analysis, we used the reef units as the planning units, however, these units did not share any actual common boundaries as they exist apart from each other. In the reef area target scenario, the boundary length was based on Euclidian distance between reef units. However, since Marxan expects connected planning units to have larger values, we first scaled all the distance pairs to a range of 0–1 and then reversed them to be on a scale of 1–0: a value of 1 meaning that that pair had a distance of 0 (meaning a self-connection) and 0 being the pair of reef units furthest from each other. For the connectivity-based scenario, we used the asymmetric strength of connection values calculated between reef units and a similar operation to scale connection strength values. In this scenario, it was not necessary to reverse these as the values were already in an order that descended from the strongest connections down to the lowest. In choosing a BLM value for each Marxan scenario, we calculated a calibrated value based on an analysis of boundary length and cost using multiple runs.Our results are summarized in the following products: Animation of the larval transport models, analysis of the larval dispersal (received and contributed) and local retention by Exclusive Economic Zone (EEZ), analysis of centrality measures by reef unit, and a comparison of Marxan scenarios.http://tnc.usm.edu/connectivity/animations.zip).Visualization of larval dispersal probabilities is a useful tool for understanding ocean dynamics and how coral reef ecosystems depend on each other. We created of a series of hourly animations showing modeled larval dispersal for each spawning event. An example of the data used to create several frames within the animation for the spawning event on August 21, 2011 can be seen in e.g. Outside of Cuba, where do Cuba’s coral larvae come from?) and “Into what jurisdictions do an individual jurisdiction’s coral larvae settle?” .Using the modeled reef connection strength results, we analyzed larvae recruitment and settlement exchange rates as well as local larval retention based on 32 Exclusive Economic Zones (EEZ). Figs i.e. larvae that originates and settles within the same EEZ). For example, according to the model, Belize captures much of its own larvae, receiving much less larvae that originate outside its EEZ. Honduras on the other hand, receives the most incoming larvae from other EEZs, while contributing the second highest level of larvae to other EEZs. In the last graph . Although Figs betweenness (a), closeness (b)) calculated as part of the network graph analysis are shown in betweenness captures many of the important bridges that maintain connectivity within the region while the other two measures characterize the core or center of the network. For this reason, and the fact that previous reef connectivity research had used this measure, we use the betweenness scores for target setting in the Marxan analysis. Based on model results, reefs on the edges of larger islands or land masses that are closest to other islands have higher betweeness centrality measures . Island clusters such as The Bahamas and northern side of Cuba score high in this measure due to the high density of reefs in these areas.The results of each of the two centrality measures (i.e. region as a whole); (b) stratification using ten marine ecoregions. For the first scenario, the solution steers the selection of the 30% target to the reef units with the highest reef area at the lowest cost  using E2) mapped within the study area, approximately 6,104 km2 (37%) were selected in the Marxan connectivity-based scenario stratified using marine ecoregions. Of these reef areas selected, only 1,424 km2 are included within the existing MPA network, indicating that approximately 77% of all coral reefs selected as having a high connectivity value are not included in the existing regional MPA network. We used the current World Database on Protected Areas [Our results indicate that of the total coral reef area  and severe deficiencies in management efforts [This paper addresses some of the marine connectivity challenges identified in Lagabrielle et al ; specifi efforts ,58,59. C efforts ,22,60. HWhen comparing the connectivity model results to previous regional models, one can find common areas of strong connections, breaks, and isolations. Roberts  was the i.e. 8km cell), which does not take into account local-scale processes, as well as the level of uncertainty in the use of biological parameters. For example, the use of a constant maximum pelagic larval duration (PLD) of 30 days for all coral larvae is applied and may be considered typical for most corals, however, PLD varies between coral species. There also exists uncertainty regarding the scale of larval dispersal and whether populations are mostly self-seeded or are maintained principally by recruits arriving from nearby or even distant reefs [While the results of this study help to identify general connections between coral reef areas, it is important to note several model limitations such as the treatment of all coral reef areas as equal in their ability to release larvae  when we know that reefs vary in their species composition, condition; and health depending on environmental conditions. Other limitations include the scale at which these ocean circulation models operate (nt reefs ,46,66. Ont reefs . Cowen ent reefs  suggest nt reefs  highlighDespite the many challenges in predicting larval dispersal , we demo


TargetRNA2 is a web server that identifies mRNA targets of sRNA regulatory action in bacteria. As input, TargetRNA2 takes the sequence of an sRNA and the name of a sequenced bacterial replicon. When searching for targets of RNA regulation, TargetRNA2 uses a variety of features, including conservation of the sRNA in other bacteria, the secondary structure of the sRNA, the secondary structure of each candidate mRNA target and the hybridization energy between the sRNA and each candidate mRNA target. TargetRNA2 outputs a ranked list of likely regulatory targets for the input sRNA. When evaluated on a comprehensive set of sRNA-target interactions, TargetRNA2 was found to be both accurate and efficient in identifying targets of sRNA regulatory action. Furthermore, TargetRNA2 has the ability to integrate RNA-seq data, if available. If an sRNA is differentially expressed in two or more RNA-seq experiments, TargetRNA2 considers co-differential gene expression when searching for regulatory targets, significantly improving the accuracy of target identifications. The TargetRNA2 web server is freely available for use at  The larrget RNA . Because RNase E ,4. Less  RNase E ,6. The p RNase E , enablin RNase E . A numbe RNase E ,10, yet TargetRNA2 builds on several other web servers that exist for biocomputational prediction of sRNA regulatory targets in bacteria (reviewed in ). The fiTargetRNA2 combines many of the features from the above-mentioned tools into a fast and accurate target identification system. Specifically, TargetRNA2 uses four primary features for sRNA target identification. The first feature is conservation of the sRNA. The input sRNA sequence is compared to every sequenced replicon available in RefSeq . RegionsEscherichia coli, TargetRNA2's performance was evaluated on a comprehensive set of 105 experimentally verified interactions in E. coli .TargetRNA2 uses four features to identify likely sRNA regulatory targets. The first feature is conservation of regions of the sRNA sequence. BLASTN  is used P-value. The P-value for a target corresponds to the likelihood of observing as strong an interaction by chance. For each target identified by TargetRNA2, a graphical depiction of the sRNA–mRNA interaction is shown along with information about the message's product and a link to the corresponding gene page from RefSeq  in the same pair of conditions as the sRNA were retained  with an sRNA in two or more RNA-seq experiments. To evaluate the effect of incorporating co-expression patterns into sRNA target identifications, RNA-seq data derived from nditions  was usedretained . The fivWith the rapidly expanding number of bacterial sRNA regulators that are being identified, thanks in part to advances in RNA-seq technology, comes the need for approaches that can characterize targets of these sRNA regulators. Computational methods for identifying regulatory targets of sRNA action offer a good first step, in that they are scalable and they can efficiently provide a small set of candidate targets, which can then be followed up on through more focused experimental methods. TargetRNA2 is a web server that enables rapid and accurate identification of targets of bacterial sRNA action.TargetRNA2 uses several features to identify message targets of sRNA regulation, including conservation of regions of the sRNA, structural accessibility of regions of the sRNA, structural accessibility of regions of the mRNA and energy of hybridization between the two RNAs. When compared to other computational approaches, TargetRNA2 offers improved performance both in terms of the accuracy of its predictions and the speed of its execution. TargetRNA2 is also unique among computational target identification methods in that it allows for incorporation of RNA-seq data. Our results using TargetRNA2 with RNA-seq data suggest that there is significant value in integrating RNA-seq data into target identification systems, particularly in reducing false positive rates, which has been a major challenge for computational identification of sRNA targets. This feature of TargetRNA2 will become increasingly useful as the number of RNA-seq data sets from diverse bacteria continues to grow. We offer the TargetRNA2 web server in the hope that it will be a useful resource for those interested in RNA regulators in bacteria.


Aptamers can be expressed on demand from transgenes and are intrinsically amenable to recognition by nucleic acid-binding proteins such as TFs. In this study, we review several natural prokaryotic and eukaryotic examples of RNAs that modulate the activity of TFs. These examples include 5S RNA, 6S RNA, 7SK, hepatitis delta virus-RNA (HDV-RNA), neuron restrictive silencer element (NRSE)-RNA, growth arrest-specific 5 (Gas5), steroid receptor RNA activator (SRA), trophoblast STAT utron (TSU), the 3′ untranslated region of caudal mRNA, and heat shock RNA-1 (HSR1). We then review examples of unnatural RNA aptamers selected to inhibit TFs nuclear factor-kappaB (NF-κB), TATA-binding protein (TBP), heat shock factor 1 (HSF1), and runt-related transcription factor 1 (RUNX1). The field of RNA aptamers for DNA-binding proteins continues to show promise.Transcription factors (TFs) are DNA-binding proteins that play critical roles in regulating gene expression. These proteins control all major cellular processes, including growth, development, and homeostasis. Because of their pivotal role, cells depend on proper TF function. It is, therefore, not surprising that TF deregulation is linked to disease. The therapeutic drug targeting of TFs has been proposed as a frontier in medicine. RNA aptamers make interesting candidates for TF modulation because of their unique characteristics. The products of  Gene expression is regulated at many stages, but a dominant role is played by control of transcription initiation. Crucial in this process are sequence-specific DNA-binding proteins termed transcription factors (TFs). Possessing modular structures often including a DNA-binding domain and a transcriptional activation or repression domain, some TFs also contain signal-sensing domains  ..18].Xenopus laevis is controlled by TFIIIA, a positive regulator that binds to an internal control region of the 5S rRNA gene . Fu. Fu48]. NRSF/REST is a Krüppel family of zinc finger transcription repressor expressed at high levels in most non-neuronal cells and in undifferentiated neuronal progenitors. However, its expression is low in mature neurons . REST biIt was shown that the noncoding NRSE-RNA binds transcription factor REST during a defined period of neuronal differentiation and effects changes in REST-dependent gene expression, thus modulating the function of REST between activation and repression of neurogenesis. Electrophoretic gel mobility shift assays revealed that the affinity of NRSF/REST for NRSE-RNA was much higher than its affinity for NRSE double-stranded DNA. Moreover, the expression of NRSE-RNA was shown to be necessary and sufficient to direct multipotent neuronal stem cells toward a neuronal fate, suggesting that this RNA can function as an endogenous inducer of neuronal differentiation . Based oA simple model was proposed in which NRSE-RNA-dependent gene activation is induced through the physical interaction of the RNA as a competitive decoy for REST, releasing the genome from repression. Although structural studies are lacking, it has been suggested that the interaction of NRSE-RNA with NRSF/REST involves one of the eight zinc fingers of the protein . Zinc fiin vivo studies from two different groups .,93.83,93Mysteriously, HMGA1 proteins are frequently overexpressed in tumor cells, with HMGA1 expression levels often correlated with tumor malignancy. This has suggested that HMGA1 proteins might be targeted therapeutically ,95. The The p53 tumor suppressor protein plays a prominent role in cell growth, DNA repair, cell cycle arrest, and apoptosis. Mutation of p53 is among the most prevalent abnormalities in human cancer . Althougin vitro and in the yeast three-hybrid system [The physiological significance of RNA-p53 interactions remains controversial. It has been shown that the unmodified cationic p53 C-terminus binds RNA strongly and with little sequence specificity both d system . In contd system ,105. Becd system .Nuclear receptor (NR) TFs respond to small molecule metabolites and fat-soluble compounds to regulate gene expression. They differ from other receptors in their unique ability to directly control gene expression through binding to genomic DNA, thus being classified as TFs ,108. NRsIn the past decade, an increasing number of noncoding RNAs with regulatory functions have been reported . Two of Gas5 RNA was so named because it has been observed to accumulate in growth-arrested cells . SurprisGas5 RNA apparently functions as a starvation-linked or growth arrest-linked riborepressor of GR. Gas5 RNA was identified by a LexA-based yeast two-hybrid screen with the Jurkat cell complementary DNA (cDNA) library, using the GR DNA binding domain (DBD) as bait. Increased association of GR and Gas5 RNA was observed by coimmunoprecipitation when HeLa cells were treated with dexamethasone, a GR agonist. Evidence of Gas5 RNA association with the GR DNA-binding domain came from results of experiments in which a GR chimera, G-gal-G, in which the DBD was replaced with that of the bacterial transcription factor GAL4. This chimera showed no interaction with Gas5 RNA . Gas5 RNStudies thus suggest that Gas5 RNA accumulates in growth-arrested cells in response to serum withdrawal, acting as a negative regulator of GR-induced transcription. Gas5 RNA is encoded by the Gas5 gene, which produces two mature, spliced forms of Gas5 RNA. The full-length Gas5b RNA is 630 nucleotides long and forms hairpin structures , apparenThe steroid receptor coactivator 1 (SRC-1), as well as an RNA that increased the transcription activation of steroid receptors, were found when searching for coactivators of NRs using a yeast two-hybrid screening assay normally used to identify protein–protein interactions . The uneTreatment of cells with antisense oligodeoxyribonucleotides against SRA reportedly induced ∼70% reduction of the steroid-dependent transcription . SRA difin vitro selection to seek engineered competitive TF inhibitors of this type. This work has led to the description of several unnatural RNA aptamers with potential for therapeutic control of gene expression . This TF is an important activator of genes involved in diverse biological activities, including cell proliferation, cell growth, resistance to apoptosis, and immune functions, such as inflammation and the synthesis of chemokines, interferons, MHC proteins, growth factors, and cell adhesion molecules [olecules . NF-κB aolecules . Inactivin vitro selection [In vitro and in vivo selections were then used to improve the affinity of anti-NF-κB for p502 and heterodimer p50/p65 [With aims of generating a potential candidate for inhibition of NF-κB-dependent gene activation, a high-affinity 31-nucleotide RNA hairpin aptamer was identified as a subdomain of a larger RNA developed through election . In vitr p50/p65 . Other s p50/p65 ,128.Kd ∼ 1 nM) in a 1:2 RNA-to-p50 ratio in solution [in vivo using the yeast three-hybrid system [2:p502 complex [Anti-p50 RNA aptamers were found to bind with nanomolar affinity  have been described in two reports ,132. TBPin vitro selection. After 11 selection cycles, no dominant consensus sequence was observed. Nonetheless, subsequent validation experiments suggested that the recovered RNAs specifically bound to TBP. Three aptamers were chosen for further affinity characterization by electrophoretic gel mobility shift assay. Equilibrium dissociation constants ranged between ∼3 and 10 nM. The aptamers were shown to compete with TBP for a TATA-binding sequence in double-stranded DNA, suggesting that the aptamers target the DNA-binding surface of TBP. The RNA aptamers were then incubated with multiple TBP•TATA-containing complexes to explore their disruptive activity. These data provided insight into the dynamics of TBP interactions during transcription reinitiation on a relevant kinetic time scale, and it was proposed that RNA aptamers could be used as tools to dissect transcription initiation mechanisms.In the first study , RNA aptin vitro selections against Drosophila TBP in free and TATA-bound forms. Using RNA libraries from 4 cycles of selection against TBP (prior study), 12 further parallel selection rounds were performed against TBP or a TATA•TBP complex. Interestingly, no sequence was found in common between either the selected pools or the pool created in the first study. Aptamers from both selections were studied in transcription initiation assays in vitro. It was proposed that aptamers from selections against free TBP target the DNA-binding surface of TBP and inhibit TBP binding to TATA box. In contrast, aptamers from the TATA•TBP selection were proposed to bind other surfaces and disrupt TBP interactions with other factors, thus preventing formation of the transcription initiation complex (The second study  involved complex . No furtin vivo and in vitro [RNA aptamers have been selected against heat shock factor (HSF) with the goal of dissecting transcription activation mechanisms in vitro . HSF is in vitro  and aginin vitro  of eukarin vitro . HSF funA dominant anti-HSF RNA aptamer was 90 nucleotides in length and secondary structure prediction suggested a three-way junction radiating three different stem-loops . It was in vivo studies were then designed using an aptamer expression system to rapidly generate high concentrations of a divalent version of anti-HSF, with a higher affinity for HSF (Kd∼8 nM). Endogenous expression during Drosophila development produced phenotypes that closely resembled abnormalities that occur when HSP activity is reduced, particularly a notched wing phenotype . It was further shown that abnormal phenotypes caused by aptamer inhibition of HSF1 could be suppressed upon HSF1 overexpression. Thus, these experiments convincingly demonstrated that anti-HSF1 aptamers can prevent HSF1 from activating gene expression under normal and stress conditions [Clever nditions . Since inditions , anti-HSRNA aptamers against the runt-related transcription factor 1 (RUNX1) TF have been reported. RUNX1 also known as acute myeloid leukemia 1 protein (AML1) is the α subunit of the core-binding factor (CBF) . RUNX1 iKd ∼ 100 nM). Surprisingly, when the authors tested aptamers with or without 2′-fluoro-modified pyrimidines, similar affinities were observed, suggesting that these modified nucleotides did not contribute to aptamer structure or sequence recognition by the protein. Competition experiments confirmed that anti-RHD-CBFβ RNA aptamers interfere with the formation of DNA-RHD-CBFβ complexes. Secondary structure prediction for these aptamers suggested that they contain a 5′ stem-loop that is strongly protected by RHD-CBFβ in footprint assays [A 2009 report describes 2′-fluoro-pyrimidine-modified RNA aptamers selected against a recombinant RHD-CBFβ complex using a random library containing 50 random nucleotides . After 1t assays . This st+-C mismatch and a base triple to adopt an unusual backbone structure that mimics the double-stranded DNA structure of the Runt recognition sequence [RNA aptamers against Runt domain of RUNX1 were also described in a 2013 publication describing two distinct RNA libraries with 30 or 40 random positions . After nsequence . A compaTFs are often deranged in disease, making them attractive targets for therapy. TFs occur in lower concentrations than other targets and form focal points in deregulated pathways . InhibitIn this study we have reviewed several examples of RNAs that modulate the activity of TFs in various contexts. Our survey began with natural RNAs that modulate TF activity either resulting in inhibition of transcription  or in modulation of transcription by binding to TFs or polymerases . Although unrecognized initially, the increasing inventory of natural RNA partners for DNA-binding proteins, including TFs and polymerases, suggests that there may be another level of gene expression regulation yet to be characterized. It is interesting to note that several of the known TFs modulated by RNA are zinc finger proteins  . The DNAWe next reviewed examples of several artificially selected RNA aptamers against TFs. Such aptamers have typically been selected with the goal of creating tools for understanding protein–protein and protein–DNA interactions, and with an eye toward potential therapeutic use . The reported aptamers typically have high target affinity, often higher than their affinity for a cognate DNA-binding sequence. The aptamers are also typically characterized by high specificity and distinguish between isoforms and other related proteins. Most anti-TF aptamers engage in mimicry of double-stranded DNA. These characteristics together with low immunogenicity, small size, ease of synthesis and purification, and facile modification have made RNA aptamers attractive leads for therapeutic concepts ,158.Although RNA aptamers present obvious challenges in drug delivery, they can be encoded by synthetic genes whose expression can be controlled. Maximizing aptamer expression from transgenes has been overcome in two ways. Expression can be increased by creating expression systems in which aptamer multimer transcripts self-cleave using punctuating ribozyme sequences ,159, or Therapeutic aptamer drugs have been slow to develop. While a number of aptamers have completed various stages of preclinical development, only one aptamer  completed phase III clinical trials and is now marketed for the treatment of age-related macular degeneration ,161. The


Artificial reefs are commonly used as a management tool, in part to provide ecosystem services, including opportunities for recreational fishing and diving. Quantifying the use of artificial reefs by recreational boaters is essential for determining their value as ecosystem services. In this study, four artificial–natural reef pairs in the eastern Gulf of Mexico (off western Florida) were investigated for boat visitation rates using autonomous acoustic recorders. Digital SpectroGram (DSG) recorders were used to collect sound files from April 2013 to March 2015. An automatic detection algorithm was used to identify boat noise in individual files using the harmonic peaks generated by boat engines, and by comparing the sound amplitude of each file with surrounding files. In all four pairs, visitation rates were significantly higher at the artificial reef than the natural reef. This increase in boat visitation was likely due to actual or perceived increased quality of fishing and diving at the artificial reefs, or to lack of knowledge of the presence or locations of the natural reefs. Inshore reefs (<15 m depth) had high variability in monthly visitation rates, which were generally highest in warmer months. However the seasonal signal was dampened on offshore reefs (>25 m depth). This study appears to be the first to use acoustic data to measure participant use of boating destinations, and highlights the utility of acoustic monitoring for the valuation of this important ecosystem service provided by artificial reefs. Spiegel Grove and USS Vandenberg) resulted in increased local participant use and associated economic benefits to area businesses [Artificial reefs can provide hard-structured habitat that is used by reef fishes and other fauna, and consequently can provide ecosystem services for recreational, economic, and societal benefits to various stakeholder groups. The valuation of ecosystem services, such as those provided by artificial reefs, has become a paramount goal of ecosystem-based management of marine resources. The first necessary step in the valuation process of any resource is to quantify its use by involved stakeholders. For artificial reefs, this has been accomplished in several ways. For example, the analysis of dive shop log books and on-the-water observations indicated that the presence of two large artificial reefs in the Florida Keys  autonomous recorders. DSG recorders are self-contained, low power consumption, high sample rate acoustic recorders that have been used to monitor fish, marine mammals, and boat noise , 14, 15.Each recorder was bottom mounted using concrete blocks . The recorders were attached to 2 m ropes with stainless steel hose clamps. Each rope had a galvanized thimble spliced onto one end, which was situated to be at the bottom end of the recorder. The thimble-end of the rope was attached with a shackle to the galvanized eye in the central concrete block, allowing the recorders to float in a vertical (hydrophone up) orientation. In all cases, the DSG recorders were deployed approximately 200 m to the east (landward side) of the reef structure. This was done to reduce the risk of the recorders being tampered with by divers or being caught by boat anchors. The offset location of the recorders was not expected to decrease the number of boats detected, as the majority of boats visiting the sites would be transiting from the east (therefore passing over the recorders), and the offset distance was negligible in relation to typical boat sound propagation distances .The large data set in this study made the manual inspection of spectrograms impractical, therefore the use of a boat detection algorithm was necessary. Boat noise is characterized by tonal harmonics caused by the cyclic properties of engine, shaft, and propeller rotation . These hThe first four steps of the boat detection algorithm were tested and optimized in an iterative process using a test data set of 2,742 non-sequential acoustic files collected in 2008 in the same area as the study  was a measure of how effective the 10 second per 10 minute duty cycle was at “capturing” boat noise from a boat in acoustic detection range. In order to determine this variable, several recorders were programed to begin recording continuously near the end of their scheduled deployments. Ten 5-day long continuous files were analyzed. Boat sounds were manually detected by inspection of spectrograms of the continuous files using Adobe Audition , and a 10 second per 10 minute duty cycle was simulated for each.The probability of a boat close to a reef that was actually visiting the reef (PV) was determined by field observations during recorder servicing cruises (approximately once every three months). At each reef site, the number of boats which stopped for any amount of time at the reef was recorded, as well as the number of boats which passed over or near the reef without stopping (within approximately 1 km) was recorded during each visit. From this data, the overall proportions of boats stopping at each reef were calculated .As sound propagation changes with water temperature and thermal stratification, as well as with depth , the potOnly full recording days were analyzed in this study; partial days of recording due to DSG recorder deployment or recovery were omitted from analysis in order to eliminate detections of the research vessel. Using the boat noise detection algorithm and the detections-to-boats algorithm, the mean number of boats per day at each reef was calculated for each month of the study.Boat detection rates were analyzed to determine if artificial reefs and natural reefs received different numbers of visiting boats during the study. It was expected that there would be considerable amounts of inter-month variability in boat visitations. Therefore, only comparisons between reefs using shared months (months recorded at both reefs) were used for statistical analysis. There was also expected that there would be more shared months of data between two recorders than between all eight; therefore, to maximize the number of shared months for analysis, individual two-tailed t-tests were performed on each artificial–natural reef pair. In order to determine if monthly boat visitation rates of each individual reef varied with month, we used Chi-squared goodness of fit tests to determine if the visitation rates were different than a null-hypothesis uniform distribution.The optimized values for the boat-detection algorithm were a 10-point median filter, a 500–7,000 Hz band-pass filter, an FFT average of 300 Hz, and a threshold for peak identification of four peaks. The amplitude threshold optimum was found using a five-file window  and 0.5 standard deviation above the mean RMS amplitude. Using these values, the algorithm had a correct detection rate of 11.8% and a correct rejection rate of 99.5%.For the variables in the detections-to-boats algorithm, the mean probability of boat detection using the duty cycle (PDC) was 0.543 (SD = 0.064). There was no significant difference in PDC between artificial reefs and natural reefs  or inshore and offshore reefs . Therefore, the mean value of 0.543 was used as the probability of duty cycle detection for all stations. For the probability of boats vising the reef (PV), values appeared to be reef specific, with artificial reefs generally being higher. Therefore, the algorithm used reef specific values for PV . There were no significant differences between the received levels in different seasons or different depths . Therefore, neither depth nor season were considered to be significant factors in detection probabilities, and SC and DC were not included in the calculations. This conclusion was supported by previous work that found that sound propagation varied little in the Gulf of Mexico over depths similar to the current study .Acoustic data were collected between April 2013 and March 2015, resulting in 4,585 days and 660,240 files for analysis see . Monthly2 = 518.3, p < 0.001, df = 19), 21 HS Ledge , St. Pete Beach Reef , and AC5 . In all cases, there was a tendency for increased boat visitation rates in warmer months, and greater within-month variation was observed during warmer months. Less variation in monthly boat visitation rates was observed in the four offshore reef sites. Significant variation was observed at Pinellas II , but not at Caves , Treasure Island II , or Florida Fisherman’s Ledge . At these offshore reef sites, large increases in boat visitation rates were observed in spring (February—April).Monthly boat visitation rates were significantly variable for all four inshore reef sites: Clearwater Reef  and 21 HS , Pinellas II  and Caves , St. Pete Beach Reef  and AC5 , and Treasure Island II  and Florida Fisherman’s Ledge . In every artificial–natural reef pair studied, significantly more boats visited the artificial reef site than the natural reef site. At the inshore locations, visitation rates at the artificial reef sites were approximately ten times higher than those at natural reefs. Differences in visitation rates were approximately eight times higher at the offshore artificial reef Pinellas II than the natural reef Caves; however, Treasure Island II artificial reef only had approximately twice the boat visitations than its paired natural reef, Florida Fisherman’s Ledge. The proportions of boats visiting the reefs PV in  also sugndenberg ), and bondenberg .Epinephelus morio [The high rates of boat visitation at artificial reefs in comparison to natural reefs in this study are likely due to increased recreational value perceived by sport fishers. Fishing was a very common activity among boaters at the artificial reefs in this study . Most boaters (64%) in the Tampa Bay area  fished during their boating trips, and the most common reason for selecting boating destinations was better fishing opportunities (35.6% of surveyed boaters ). The ecus morio ). Howeveus morio ,27, and us morio .Blackthorn and the Sheridan tug at Pinellas II, and two commercial fishing boats at Treasure Island II), which are popular destinations for local diver charters .Diving, and particularly spearfishing, may also be an important activity at the artificial reefs in the study region, and likely contributes to the increased boat visitation rates at these reefs. Dive flags and / or divers in the water were frequently observed at the artificial reef sites . Artificial reefs have been found to be popular with divers in several studies ,24,28,29www.floridagofishing.com [Another factor in the higher boat visitation rates at artificial reefs may be that the presence and locations of the reefs are simply better known than the natural reefs. Access to location information has been found to be an important factor in determining boater visitation patterns in several studies ,29. The hing.com ; howeverAll inshore reef sites had significant monthly variability, with visitation rates tending to peak in warmer months. This result generally reflects the temporal patterns observed in boating activity, which has previously been found to peak in April through August in the study region . At the Several improvements could be made to the methodology of this study for future efforts to quantify boat visitation rates using passive acoustics. The detection algorithm used to identify boat noise in acoustic files could potentially be improved with additional testing (with larger data sets for example) or a learning algorithm. The background noise levels in the study area were highly variable. In a previous study, ambient noise in the study area was found to be mainly due to boat noise and snapping shrimp, both of which increased during warmer months and in shallower waters . In thisThis study was one of the first to directly compare the use of artificial reefs to the use of nearby natural reefs, and the first to do so using passive acoustics. Using passive acoustic monitoring to measure boat traffic is a practical method in that it allows for synoptic-scale data collection. Multiple sites can be simultaneously monitored, “continuously” (with a given duty cycle in this case) for long time periods. This allowed for the assessment of boat visitation rates over large spatial and temporal scales. The results of this study indicate that boat visitation rates at four artificial reefs in the eastern Gulf of Mexico are significantly higher than boat visitation rates at nearby natural reef sites. Our results support a growing body of evidence indicating that artificial reefs provide considerable cultural ecosystem services . In addiS1 AppendixAppendix includes the station name, the folder name in the original analysis, the file number of the recording, the time (Eastern Standard Time) and date of the recording, the number of harmonic peaks detected by the algorithm, and the root-mean-square noise level . Each tab contains data from three months of the study.(XLSX)Click here for additional data file.S2 AppendixAppendix includes the station name, the folder name in the original analysis, the file number of the recording, the time (Eastern Standard Time) and date of the recording, the number of harmonic peaks detected by the algorithm, and the root-mean-square noise level . Each tab contains data from three months of the study.(XLSX)Click here for additional data file.S3 AppendixAppendix includes the station name, the folder name in the original analysis, the file number of the recording, the time (Eastern Standard Time) and date of the recording, the number of harmonic peaks detected by the algorithm, and the root-mean-square noise level . Each tab contains data from three months of the study.(XLSX)Click here for additional data file.S4 AppendixAppendix includes the station name, the folder name in the original analysis, the file number of the recording, the time (Eastern Standard Time) and date of the recording, the number of harmonic peaks detected by the algorithm, and the root-mean-square noise level . Each tab contains data from three months of the study.(XLSX)Click here for additional data file.S5 AppendixAppendix includes the station name, the folder name in the original analysis, the file number of the recording, the time (Eastern Standard Time) and date of the recording, the number of harmonic peaks detected by the algorithm, and the root-mean-square noise level . Each tab contains data from three months of the study.(XLSX)Click here for additional data file.S6 AppendixAppendix includes the station name, the folder name in the original analysis, the file number of the recording, the time (Eastern Standard Time) and date of the recording, the number of harmonic peaks detected by the algorithm, and the root-mean-square noise level . Each tab contains data from three months of the study.(XLSX)Click here for additional data file.S7 AppendixAppendix includes the station name, the folder name in the original analysis, the file number of the recording, the time (Eastern Standard Time) and date of the recording, the number of harmonic peaks detected by the algorithm, and the root-mean-square noise level . Each tab contains data from three months of the study.(XLSX)Click here for additional data file.S8 AppendixAppendix includes the station name, the folder name in the original analysis, the file number of the recording, the time (Eastern Standard Time) and date of the recording, the number of harmonic peaks detected by the algorithm, and the root-mean-square noise level . Each tab contains data from three months of the study.(XLSX)Click here for additional data file.


Surveillance of integrity of the basic elements of the cell including DNA, RNA, and proteins is a critical element of cellular physiology. Mechanisms of surveillance of DNA and protein integrity are well understood. Surveillance of structural RNAs making up the vast majority of RNA in a cell is less well understood. Here, we sought to explore integrity of processing of structural RNAs in relapsing remitting multiple sclerosis (RRMS) and other inflammatory diseases.We employed mononuclear cells obtained from subjects with RRMS and cell lines. We used quantitative-PCR and whole genome RNA sequencing to define defects in structural RNA surveillance and siRNAs to deplete target proteins. We report profound defects in surveillance of structural RNAs in RRMS exemplified by elevated levels of poly(A) + Y1-RNA, poly(A) + 18S rRNA and 28S rRNAs, elevated levels of misprocessed 18S and 28S rRNAs and levels of the U-class of small nuclear RNAs. Multiple sclerosis is also associated with genome-wide defects in mRNA splicing. Ro60 and La proteins, which exist in ribonucleoprotein particles and play different roles in quality control of structural RNAs, are also deficient in RRMS. In cell lines, silencing of the genes encoding Ro60 and La proteins gives rise to these same defects in surveillance of structural RNAs.Our results establish that profound defects in structural RNA surveillance exist in RRMS and establish a causal link between Ro60 and La proteins and integrity of structural RNAs.The online version of this article (doi:10.1186/s13059-015-0629-x) contains supplementary material, which is available to authorized users. Relapsing remitting multiple sclerosis (RRMS) affects approximately 0.1% of the population. RRMS is characterized by de-myelination of neurons but disease mechanisms are incompletely understood. Involvement of innate and adaptive arms of the immune system is detected in inflammatory demyelinating lesions implicating these in pathogenesis. Both genetic and environmental factors are also implicated in the origins of RRMS .Studies have demonstrated an expanding complexity of non-protein-coding RNAs (ncRNA) in higher eukaryotes . Non-verPolyadenylation of mRNAs at the 3’ end is a necessary step in their synthesis and maturation ,19,27-32It is fundamentally unknown how normal cellular processes or responses to extracellular stimuli may invoke polyadenylation and degradation of ncRNA substrates or if human disease processes exhibit defects in polyadenylation of ncRNA substrates as part of their pathogenesis. Here, our results demonstrate that mononuclear cells from subjects with RRMS exhibit pervasive increases in levels of polyadenylated ncRNAs including Y1 RNA, 18S and 28S rRNA, and U1, U2, and U4 snRNAs and these defects are unique to RRMS. Defects in expression of both Ro60 and La proteins in RRMS appear to contribute to increased polyadenylation of ncRNAs. Further, IFN-β1b, a common RRMS therapy , restoreTo initiate our studies, we determined levels of Y RNAs in different subject cohorts by quantitative PCR using either random hexamers or oligo-dT for synthesis reasoning that random hexamers should allow amplification of all Y RNAs independent of whether or not they were polyadenylated but that oligo-dT would allow amplification of only those Y RNAs that were polyadenylated consistent with previously established methodologies . We alsoWe used a similar approach to measure levels of total and polyadenylated 18S and 28S rRNAs. We found that levels of total 18S and 28S rRNAs were similar between the RRMS and CTRL cohorts when random hexamers were used for cDNA synthesis  from CTRL subjects, subjects with RRMS or with other inflammatory diseases. As above, synthesis of cDNA was performed using random hexamers and oligo-dT and transcript levels were determined by quantitative PCR. We found a modest increase in total levels of U1 RNA (random hexamers) in RRMS relative to CTRL Figure A. In conThe U1, U2, U4, U5, and U6 RNAs play critical roles in determining mRNA length and isoform expression via several mechanisms including protection from premature cleavage and polyadenylation of nascent pre-mRNAs ,26,34. DMBP, CSF1R, and NFATC1   Figure D. Thus, ) Figure E. All ni) Figure F. TranscTROVE2 (Ro60) and SSB (La) expression levels in blood samples harvested in PaxGene tubes from the following cohorts of subjects: CTRL, CIS-MS, RRMS, RA, SLE, NMO, and PD. We found that TROVE2 and SSB transcript levels were markedly reduced in the established RRMS cohort compared to CTRL. This difference was unique to RRMS and not observed in other autoimmune disease cohorts or in other inflammatory (NMO) or non-inflammatory (PD) neurologic conditions  + Y1 RNA and poly(A) + U1 snRNA were close to CTRL levels in RRMS subjects on betaseron therapy compared to RRMS subjects on either copaxone or no immunomodulatory therapy  + Y1 RNA and poly(A) + U1 RNA was very rapid. We hypothesize that signaling pathways either directly or indirectly activated by betaseron interfere with signaling pathways driving defects in polyadenylation of structural RNAs in and expression of TROVE2 and SSB in RRMS and that betaseron will be a useful tool to identify underlying mechanisms. Further, measurement of polyadenylated species of ncRNAs may provide a useful means to monitor responses to betaseron or other immunomodulatory therapies in RRMS.For all analyses above, RRMS subjects were either on copaxone or no immunomodulatory therapy. Thus, we compared these subjects to RRMS on stable betaseron therapy. We found that levels of y Figure A, B. We TROVE2 and SSB and transfected them into the human THP-1 monocyte or the Jurkat T cell line. Transfection with TROVE2 siRNA caused specific reduction of TROVE2 transcripts but not SSB transcripts  + Y1 RNA in THP-1 cells. We found that knockdown of either TROVE2, SSB, or the combination effectively increased levels of poly(A) + Y1 RNA  + 18S rRNA  + 18S RNA. In contrast, levels of poly(A) + 28S rRNA were largely unaffected by knockdown of either TROVE2 or SSB but were modestly increased by the knockdown of both TROVE2 and SSB. We also examined the impact of TROVE2 or SSB knockdown on levels of poly(A) + U1 RNA and found that knockdown of TROVE2, but not knockdown of SSB, caused a marked increase in levels of poly(A) + U1 RNA  + U1 RNA and alterations in levels of U1 RNA result in isoform switching, we determined if TROVE2 and SSB RNA knockdown was sufficient to alter mRNA lengths in a cell  in the United States with the assistance of the Accelerated Cure Project. In the established multiple sclerosis cohort, seven patients were receiving glatiramer acetate (Copaxone) and 15 were on no immunomodulatory therapy. Unless otherwise indicated, no established relapsing-remitting multiple sclerosis patients were receiving interferon-β1b (Betserson). RA and SLE samples were collected from three different sites in the USA . Relevant institutional board approval from all participating sites was obtained. All subjects provided written informed consent. Additional patient characteristics are summarized in Additional file  patients2. Jurkat T cells were obtained from the American Type Culture Collection . The THP-1 monocytic cell line was from Dr. Jacek Hawiger . Single, inventoried silencer select siRNAs (Ambion) against each mRNA were transfected into Jurkat T cells using the Amaxa Cell Line Nucleofector Kit V (Lonza) or into THP-1 cells by Lipofectamine RNAiMAX (Invitrogen).Cells were cultured in RPMI 1640 medium supplemented with fetal bovine serum (FBS) at 10% (Jurkat) or 20% (THP-1), 1% penicillin/streptomycin, and 1% L-glutamine at 37°C in a humidified atmosphere of 5% COPeripheral whole blood was drawn into PreAnalytiX PaxGene tubes . Cell cultures were treated with Tri-Reagent (Molecular Research Center). RNA was isolated following the supplied protocol and purified with the RNEasy MinElute Cleanup kit (Qiagen) and quantified using a Nano Drop 1000 spectrophotometer ,47. CompWestern blotting was performed as described previously ,49. BrieRIP analysis was performed as described previously ,51. BrieWe extracted RNA from healthy controls (N = 8) and established relapsing-remitting multiple sclerosis patients (N = 6) using PaxGene tubes according to the manufacturer’s protocol. Library preparation was then performed using the Illumina Tru-Seq RNA kit using oligo-dT primers. RNA-sequencing was conducted in the Vanderbilt Technologies for Advanced Genomics (VANTAGE) core. One hundred bp paired-end reads were generated with an Illumina HiSeq 2500. A quality control step was initially performed on the raw data to identify potential outliers before any advanced analysis using tools such as Fastx Toolkit and FastQC . The RNABioinformatics Toolbox  were employed to extract counts on basepair (bp) intervals specified by library GTF annotation files or by customized un-annotated locations along each chromosome in annotated exons and introns. These raw counts were normalized using the medians of the sets formed by the ratios of the raw counts and the non-zero geometric mean of the raw counts on each interval. These normalized counts were then used to perform the statistical analysis comparing the control and multiple sclerosis groups. In particular, the P values and adjusted P values for the difference of the means of the two groups were computed on each bp interval. The Benjamini-Hochberg procedure was used to find the adjusted P value and correct for FDRs from multiple sampling. Next, the log2 of the ratio of the mean normalized multiple sclerosis counts to the mean normalize control counts was computed for bp intervals that had non-zero geometric means. Lastly, bp intervals where the adjusted P value was less than a prescribed number  and the absolute value of the log2 ratio was larger than a second prescribed number  were then selected for further analysis. Output from MatLab calculations were inputted into Mathematica (version 9.0.1) whose GeonomeData database was used to further explore coding and non-coding regions along the chromosomes. We considered loss of one or more exons at the 5’ end of an individual mRNA transcript to be an example of 5’ mRNA shortening and loss of one or more exons at the 3’ end of an individual mRNA to be an example of 3’ mRNA shortening. Intron retention was defined as gain of specific RNA transcripts within introns.MatLab (version R2013a) was used to construct a matrix of counts from the data. In particular, MatLab functions included in the t-test using GraphPad Prism Software . P values <0.05 were considered significant.Data are expressed as the mean ± SD of three or more independent experiments. Significance was determined by Student’s The RNA-sequencing data used in this study are accessible through NCBI’s Gene Expression Omnibus using accession code GSE66573.


Relatively little is known about the range of RNA levels in human blood. This report provides assessment of peripheral blood RNA level and its inter-individual differences in a group of 35 healthy humans consisting of 25 females and 10 males ranging in age from 50 to 89 years. In this group, the average total RNA level was 14.59 μg/ml of blood, with no statistically significant difference between females and males. The individual RNA level ranged from 6.7 to 22.7 μg/ml of blood. In healthy subjects, the repeated sampling of an individual’s blood showed that RNA level, whether high or low, was stable. The inter-individual differences in RNA level in blood can be attributed to both, differences in cell number and the amount of RNA per cell. The 3.4-fold range of inter-individual differences in total RNA levels, documented herein, should be taken into account when evaluating the results of quantitative RT-PCR and/or RNA sequencing studies of human blood. Based on the presented results, a comprehensive assessment of gene expression in blood should involve determination of both the amount of mRNA per unit of total RNA (U / ng RNA) and the amount of mRNA per unit of blood (U / ml blood) to assure a thorough interpretation of physiological or pathological relevance of study results. The molecular composition of circulating blood reflects the physiological and pathological events in tissues and organs of the human body. The use of peripheral blood in diagnostic applications is therefore desirable for development of biomarkers due to its accessibility and the lower risk associated with its collection, as compared to organ biopsies , 2. AdvaDespite the frequent use of blood in gene expression studies, RNA content in human blood has not been thoroughly evaluated. In the literature there is only a limited assessment of blood RNA levels and of RNA recovery from human blood. In previous studies, RNA extracted from human peripheral blood has been reported to be in the range of 1–6 μg of total RNA per ml of blood –9. This Chesapeake Research Review, LLC. CIRBI Protocol # Pro00009509. The IRB specifically approved this study. Participants were provided written informed consent that was signed by the subject and a witness. The informed consent documents were retained. This consent procedure was approved by Chesapeake IRB.The study comprised 35 healthy individuals with no clinical history of autoimmune disease. The group included 25 females with an average age of 61.68 years (range from 50 to 82 years) and 10 males with an average age of 65 years (range from 52 to 89 years). The health status of blood donors was evaluated based on a questionnaire containing 53 health-related questions and a complete blood cell (CBC) analysis.® BD. The resulting blood/reagent mixture was thoroughly shaken to form a homogeneous lysate and stored at -20°C. The blood-RNAzol® BD lysates can be stored at -20°C for at least 1 year with no discernable degradation of RNA.Blood collection was done by New Horizons Clinical Research, Cincinnati, OH. After an overnight fast, venous blood was drawn into two 10 ml BD Vacutainer tubes with EDTA (K2) as anticoagulant (Becton Dickinson and Company). One tube was used for CBC analysis and the second one for RNA and DNA isolation. The CBC analysis included counts of white blood cell (WBC) types, red blood cells (RBC) and platelets by automated analysis  performe® BD reagent as described in the manufacturer’s brochure . The large RNA fraction contains high molecular weight RNA (>200 nucleotides), including ribosomal RNA (rRNA), messenger RNA (mRNA) and long noncoding RNA (lncRNA). The small RNA fraction (<200 nucleotides) contains 5S rRNA, tRNA, snoRNA, scaRNA, piRNA and other micro RNAs.The isolation of large RNA and small RNA fractions from frozen blood lysates was performed using RNAzol®BD lysates were weighed to determine the exact amount of blood in each sample assuming blood specific gravity of 1.06 g/ml [® BD volume and adding acetic acid, the blood lysates were incubated at room temperature for 10 minutes and mixed with 5% (v/v) of bromochloropropane. The mixture was centrifuged and the supernatant was transferred to a new tube. The bottom phenolic phase was immediately stored at -20°C for future DNA isolation. The RNA-containing supernatant was mixed with 0.35 volume of isopropanol to precipitate the large RNA fraction. The precipitate was isolated by centrifugation. The resulting supernatant was used to isolate the small RNA fraction by precipitation with an additional 0.65 volume of isopropanol. The precipitated large and small RNA fractions were washed in 75% ethanol and dissolved in water. For long-term storage, RNA samples were dissolved in FORMAzol  and stored at -20°C. RNA concentration was determined based on optical density (OD) at A260 nm, assuming that 1 OD of RNA dissolved in water corresponds to 40 μg RNA/ml, and 1 OD of RNA dissolved in FORMAzol corresponds to 44.9 μg RNA/ml. The average A260/280 and A260/230 ratios of the large RNA fraction from the 35 samples were 2.07 ± 0.01 and 1.69 ±0.26, respectively. Total RNA content in the peripheral blood was calculated as the sum of the large RNA and small RNA fractions. The isolated RNA samples were analyzed with the Agilent Bioanalyzer RNA Nano 6000 Kit .The blood-RNAzol.06 g/ml . After a® BD manufacturer’s brochure.Genomic DNA was isolated from the frozen phenolic fraction by extraction with guanidine thiocyanate at alkaline pH as described in the RNAzolFor validation of the RNA isolation protocol, we determined the variability of RNA recovery in multiple RNA extractions (n = 12) from a single blood draw. For this study, about 50 ml of peripheral blood was collected from two individuals with low and high blood RNA levels, respectively. Aliquots of 2.5 ml of whole blood were added to 5 ml of RNAzol-BD and the resulting lysates were frozen and used for RNA extraction over a period of several months. RNA yield for these two individuals was 9.37 ± 0.33 and 23.17 ± 0.45 μg of total RNA / ml blood with a respective coefficient of variation (CV) of 3.54% and 1.94%. Thus, for these blood samples, the mean methodological CV associated with extractions of RNA from aliquots of the same blood draw was 2.74%.In the preliminary experiment, we examined blood RNA levels, over a 1 to 9 month period, in a group of healthy donors consisting of three women and three men. During this period of time there was no reported change in health status of the donors. Results in ® Green PCR Master Mix (Life Technologies). The cDNA equivalent to 5 ng of the large RNA fraction was used as template in each PCR reaction. As an additional precaution, all of the RNA samples were DNase-treated and the resulting RNA was tested with PCR to confirm the absence of DNA contamination. The thermal profile consisted of 1 holding stage of 95°C for 10 minutes and 40 cycling stages of 95°C for 15 seconds followed by 60°C for 1 minute. Melt curves were generated after 40 cycles of amplification. Expression of GAPDH, ACTB, and HPRT1 genes were assessed relative to the standard curves. Standard curves were generated using ACTB primers and control cDNA template ranging from 10 ng– 0.64 pg equivalents of the large RNA fraction. The use of DNA template as a universal external standard in qPCR allows for the assessment of differences in the transcript levels in RNA derived from different samples [Reverse transcription was performed with 1 μg of the large RNA fraction using the High-Capacity cDNA Reverse Transcription Kit (Life Technologies). Expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta actin (ACTB), and hypoxanthine phosphoribosyltransferase 1 (HPRT1) was analyzed by Quantitative Real-Time PCR using primers listed in  samples .Measured quantities for each sample were subjected to standard statistical analysis and the results are presented in the form of mean ± 1 standard deviation. To evaluate correlations among variables of interest, software packages R and Matlab  were useFor small and selected subsets of data, for which the standard parametric approach may result in statistical bias, we performed Monte Carlo sampling (resampling) to estimate the variability of means , 16. Thi6 cells/ml of blood. In the studied group, no correlation between total RNA level and age was observed . The collected blood samples were used for CBC analysis and for RNA and DNA isolation. ® BD protocol enables blood RNA to be fractionated into large RNA (>200 nucleotides) and small RNA (<200 nucleotides) fractions. Over 90% of the large RNA fraction is rRNA and the rest is mRNA and lncRNA [The RNAzold lncRNA . The smaThe Bioanalyzer electrophoretic profiles of RNA from all 35 samples showed a similar distribution of rRNA in samples with high and low RNA levels. This is illustrated in Electrophoretic patterns of blood RNA samples shown in To identify factors in human blood that contribute to total RNA level, we performed statistical correlation analysis on the data obtained from the full set of 35 samples . In eachIn order to further examine factors that may contribute to the 3.4-fold inter-individual differences in the blood RNA level, we selected for analysis two subpopulations (n = 5) at the extremes of the blood RNA level. We analyzed these samples with respect to: RNA and DNA levels, and WBC counts, neutrophil, lymphocyte, monocyte, RBC and platelet cell numbers . EosinopAs expected, blood DNA levels shown in In the evaluation of gene expression by qPCR it is a common approach to use samples containing a fixed amount of RNA. This approach, however, does not take into account variable levels of RNA in blood derived from various donors. To determine how low- and high-blood RNA levels affect quantitation of gene expression, we quantitated housekeeping gene mRNAs in blood samples previously described in In addition, it is important to note that the results presented in ® BD immediately after the blood draw. Processing of blood in RNAzol® BD allows for quantitative recovery of RNA [The analysis of gene expression in human peripheral blood is considered to be an attractive resource for assessing the metabolic and pathological status of a blood donor. To monitor gene expression in peripheral blood, it is important to consider ex vivo RNA degradation and the effectiveness of RNA recovery from the blood. It was shown that some RNA transcripts are relatively stable but hundreds of RNA transcripts are sensitive to ex vivo room temperature storage before RNA extraction , 20, 21.y of RNA . Quantity of RNA –9. In thThe total RNA level in blood from various individuals ranged from 6.7 to 22.7 μg per ml of blood. The 3.4-fold inter-individual differences in total RNA level in blood from healthy donors are documented in Figs The analysis of all 35 samples  and the The average RNA level of 14.58 μg per ml of human blood documented in our study indicate that in previous studies only a small portion (15–26%) of blood RNA was extracted and analyzed for gene expression. It is unknown whether lower-yield extractions proportionally recover all lncRNA and mRNA species that are present in whole blood. It is of interest that electrophoretic RNA profiles presented in this report  and in pThe extent to which RNA levels in other human tissues parallel inter-individual variations of the RNA level in blood is not known. Our observations with animal tissues indicate that the 3–4 fold differences in the blood RNA level is likely limited to blood. Over many years, we have performed numerous RNA extractions of rat and mouse tissues including: liver, muscles, kidney and brain derived from dozens of animals. The amount of RNA isolated from tissues of healthy animals was relatively unchanged and variations in the RNA level per mg of tissue were within the limit of ± 15% (unpublished observation). It is of interest to note that a recent study using rat whole blood reported a large inter-animal variation (5-fold) in the amount of total RNA extracted from blood .The diversity of factors that may contribute to the inter-individual differences of total RNA level in blood is reflected by the fact that, among the measured variables in this study , there wThe complex relationship between inter-individual differences in the blood RNA level and blood components is also illustrated in It remains to be investigated if there are physiological consequences of the low or high levels of RNA in the circulation. However, our results indicate that the level of RNA in blood is an important parameter for quantitation of gene expression. Currently, the most commonly used denominator for calculating gene expression in blood is the quantity of total RNA or large RNA fraction used for analysis. The data presented in Figs Similar to RT-qPCR, the inter-individual differences in blood RNA level may affect quantitation of mRNA in RNA sequencing. In RNA sequencing, a fixed amount of total RNA or the large RNA fraction containing rRNA and mRNA is used for analysis. The rRNA- and globin-depleted RNA is converted to cDNA and amplified, and an equal amount of amplified cDNA is used for sequencing , 18, 33.The significance of taking into account the blood RNA level in the interpretation of gene expression data is illustrated in S1 FigThe red circles and blue inverted triangles represent the female and male donors in the sample group. The blue dashed line represents the 95% prediction interval for the sample. A cluster of 12 (34%) individuals ranging in age from 55–60 years of age display mean blood RNA concentrations that span the entire range of values reported in this study.(EPS)Click here for additional data file.S2 FigThe Null-hypothesis is rejected if any of the means (red dot) lie outside the decision line (green).(TIF)Click here for additional data file.S3 FigThe Null-hypothesis is rejected if any of the means (red dot) lie outside the decision line (green).(TIF)Click here for additional data file.S4 FigThe Null-hypothesis is rejected if any of the means (red dot) lie outside the decision line (green).(TIF)Click here for additional data file.S1 TableM, male donors.(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


The mathematical study of the set of medians is complicated by the facts that breakpoint distance is not a metric but a pseudo-metric, and that it does not define a geodesic space.The breakpoint median in the set We introduce the notion of partial geodesic, or geodesic patch between two permutations, and show that if two permutations are medians, then every permutation on a geodesic patch between them is also a median. We also prove the conjecture that the input permutations themselves are medians. Among the common measures of gene order difference between two genomes, the edit distances, such as reversal distance or double-cut-and-join distance, contrast with the breakpoint distance in that the former are defined in a geodesic space while the latter is not. Another characteristic of breakpoint distance that it does not share with most other genomic distances is that it is a pseudometric rather than a metric.k ≥ 3 given genomes, and second, to estimate the ancestral genome of these k genomes. We have shown that the second goal becomes unattainable as n → ∞, where n is the length of the genomes, if there are more than 0.5n mutational steps changing the gene order  and contains exactly two permutations, π1n , ..., πand n, ..., ππ1. The number of classes is thus n!/2. For any π, we denote the other element of [π] by nS, denoted by This distance is not a metric on x and y instead of [x] and [y], and/or drop the superscript n.Where there is no risk of ambiguity, we can simplify the notation by using n − 1 when they have no common adjacencies. Bp distance is symmetric on n Sand hence on x, y, z. We haveIt is clear that the maximum possible bp distance between two permutation classes is ThereforeBut G if for any nS.We say a pseudometric (or a metric) Definition 1 Given a set {x1k, . . . , x} ⊆ S and a pseudometric space ρ on S, a median for the set is µ ∈ S such that is minimal.S, ρ) is a geodesic space if for any two points x, y ∈ S there exists a finite subset of S containing x, y that is isometric with the discrete line segment . Any subset of S with this property, and there may be several, is called a geodesic between x and y. For example, all connected graphs are geodesic spaces. In a geodesic space the medians of two points x and y consist of all the points located on geodesics between x and y.A discrete metric space  of the line segment . For any two points x, y in an arbitrary metric space  there exists at least one geodesic patch between them because x, y is isometric to {0, ρ}. In addition, any geodesic is a geodesic patch. Any point z on a geodesic patch between x, y satisfies:What can we say when the space is not a geodesic space? To answer this, we extend the concept of geodesic by introducing the concept of a geodesic patch. A geodesic patch between x and y must lie on a geodesic patch between them. We denote the set of all permutations lying on geodesic patches connecting x, y ∈ nSby Therefore all the medians of two points id and π := 1 2 x1 x2 ... xn−4 n − 1 n when x1 x2 ... xn−4 is a non-identical permutation on {3, ..., n − 2}. The smallest change to id is to cut one of its adjacencies, say {i, i + 1}, and rejoin the two segments in one of the three possible ways: 1 to n, 1 to i + 1 or n to i. Now if we cut the adjacencies {1, 2} or {n − 1, n} in id the distance of the new permutation to both id and π increases. If on the other hand we cut one of the other adjacencies in id all the ways of rejoining, which increase the distance to id, either increase or leave unchanged the distance to π, since {1, n}, {1, i + 1} and {n, i} are not adjacencies in id to π.Although id and π. Note d = 5, the maximum possible distance in is a geodesic between k permutations with maximum pairwise distances. As we shall see later, this situation is very similar to the case of k uniformly random permutations. Let  be a pseudometric space.In this section we investigate the bp median problem in the case of x ∈ S to a finite subset ∅ ≠ B ⊆ S is defined to beThe total distance of a point B, x ∈ S, that isThe median value of ∅ ≠ B ⊆ S. We define a multiplicity function B nfrom B to B n(x) = xn. We call A =  a set with multiplicities. We define the total distance of a point x ∈ S to A to beWe can extend this definition to sets with multiplicities. Let A. When S is finite then the total distance function takes its minimum on S and "inf" turns into "min" in the above formulation. The points of the space S that minimize the total distance to A are called the median points or medians of A and the set of all these medians is called the median set of A, denoted by S,ρM (A).The definition of median value in Equation (8) can be extended in an analogous way to the median value of a set with multiplicity B and A =  be a subset and a subset with multiplicities of nS. We define [B] to be the set of all permutation classes of n Sthat have at least one of their permutations in B. That isLet B, B′ ⊆ n Sare said to be equivalent, denoted by B ~ B', if [B] = [B′]. Also we define [Bn] to be a function from [B] to Two nonempty subsets A] is straightforward:Then the definition of  = [A′]. In fact [A] is the equivalence class containing A. We call [A] a subset of ~ " for all the above concepts without restriction.and we say two nonempty subsets of A ~ A′ and x ~ x′, we haveWith these definitions we can readily verify that in the context of bp distance, for d as both a metric on nS. Therefore we can conclude thatRecall that we use and similarlynm(A) and nM(A), respectively. Also for a subset [A] of nm([A]) and nM([A]) instead of n.Henceforward, we will simplify by replacing the notation Proposition 1 Suppose  = n − 1 for any i ≠ j, i ≤ i, j ≤ n. Then the bp median value of × is (k − 1)(n − 1). Moreover, m∗ is a median of X, m∗∈ M (X), if and only if Proof Let ≤ i, j ≤ k, we have ThereforeHenceπ = i xfor any 1 ≤ i ≤ k. This proves the first part of the proposition. For the second part we know that ∗ m∈ M (X) is equivalent with the fact that the total distance of m∗ to X is (k − 1)(n − 1), and this is equivalent to The equality holds letting Lemma 1 Let x, y, z be three permutation classes in 1 from each other. Then for any  = n − 1.Proof Having w A⊂ x A∪ yA. Also we know that i, xj xin the set X in the proposition above X is (k − 1)(n − 1).The above lemma simply indicates that for any two points Corollary 1 Suppose such that d = n − 1 for any i ≠ j. Then X that are not in What more can we say about the median positions? The notion of "accessibility" will help us to keep track of some other medians of the set Lemma 2 Let Then if and only if Proof We know d + d = d. On the other hand we can write z Aas followswhere the pairwise intersection of the sets in the right hand side is empty. We can also writeandFurthermoreandNow for "sufficiency", we haveTherefore by Equation (23) we havex,y| |A= x,y,z| |Aand hence in x,y A⊂ zA. Otherwise the inequality in (26) will be strict, which is impossible. On the other hand the inequality in (26) shows This results in For "necessity", we havez A⊂ x A∪ y Aand Equation (23). But since x,y A⊂ z A⊂ x A∪ y Awe have x,y| |A= x,y,z| |Aand we can replace x,y| |Aby x,y,z| |Ain the left hand side of the last equality. This finishes the "necessity" proof.This is true because of Definition 2 Let × := {x1k, ..., x} be a subset of 1-accessible from X if there exists an m ∈ a finite sequence y1m where yi , ..., y∈ X and z1m, where , ..., z1 = y1m , z= z and Figure We denote the set of all 1-accessible points of X by Z(X). We define Z0(X) := X. Also for r ∈ by induction, we define Zr+1(X) to be Z(rZ(X)) and we call it the set of all r+1-accessible permutation classes. That is Z1(X) = Z(X), Z2(X) = Z(Z(X)) and so on. It is clear that Zr+1(X) r includes Z(X) and also ∈ such that z ∈ rZ(X). We denote the set of all accessible points by z from iy's must be in r0) and z1m , ..., zwhere z1 = y1, m z= z and Note that Proposition 2 Suppose  = n−1 for any i ≠ j. Then for any permutation class  between z and × is (k −1)(n−1) and hence 1, m2 ∈ M (X) then Proof Suppose m1, m2 ∈ M (X) and ∗m∈ M (X). Now it suffices to show that for any r ∈ IN ∪ {0}, r Z(X) ⊂ M (X). We prove this by induction. For r = 0 this follows from Corollary 1. Suppose r Z(X) ⊂ M (X). By definition we have Zr+1(X) = Z(rZ(X)). That is for z ∈ Zr+1(X) there exists an m ∈ y1m , ..., y∈ r Z(X) and z1m, ..., z, where z1 = y1, m z= z and z1 ∈ M (X) since y1m , ..., y∈ r Z(X) ⊂ M (X). Continuing this we conclude that z1, z2m , ..., z= z ∈ M (X). Hence Zr+1(X) ⊂ M (X). This finishes the proof.Conjecture 1 Every median point of X is accessible from X, that is k random permutationsThe median value and medians of k independent random permutation classes uniformly chosen from k random permutations sampled from nS. All the results of this section carry over to permutations without any problem.In this section we study the median value and median points of n − 1. Xu et al.  Let × and y be two independent random permutation classes (irpc) chosen uniformly from Thenx, y byDefine the error function for the distance of Corollary 2 Suppose × and y are two irpc's sampled from the uniform measure and is an arbitrary sequence of real numbers diverging to +∞. Then converges to zero asymptotically -almost surely (a.a.s.), that isProof The proof is straightforward from [ard from  and Chebk irpc's. Let [A] be a subset of k elements. DefineNow we are ready to study the median value of Theorem 1 Let be a set of k irpc in sampled from the measure Then their breakpoint median value tends to be close to its maximum after a convenient rescaling with high probability, that is for any arbitrary sequence in -probability where Proof Let π be an arbitrary point of nS. Let i,j εn. On the other hand nm(Xn) ≤ (k − 1)(n − 1). The reason is the same as has already been discussed in the proof of Proposition 1. Therefore subtracting (k − 1)(n − 1) we havewhere n go to ∞ the result follows from the last corollary.Dividing by Theorem 2 Let be a set of k irpc's in sampled from the measure Then for any permutation class the total distance of zn) (to × is close to (k −1)(n−1) with high probability after a convenient rescaling. More explicitly, for any arbitrary sequence of real numbers ThereforeFurthermore if then for any Proof The structure of the proof is similar to the proof of Proposition 1. Suppose ThereforeandFrom Theorem 1 we haveHencei = 1, 2. On the other hand we have It suffices to show that ThereforeThereforesinceThe statement follows from the last inequality.We have shown that the median value for a set of random permutations tends to be close to its extreme value with high probability. Also it has been shown that every permutation accessible from a set of random permutations can be considered as a median of that set asymptotically almost surely, and conjectured that the converse is true, that every median is accessible from the original set in this way.Further work is needed to characterize the existence and size of non-trivial geodesic patches, in order to assess how extensive the set of medians is.The authors declare that they have no competing interests.All authors participated in the research, wrote the paper, read and approved the manuscript.


Acropora, produce cellular reefs. The purest reef growth forms arise in sheltered lagoons dominated by a single type of reef builder, as in the branching Acropora-dominated lagoons of the Abrolhos. In these situations reef morphology can be considered a phenotype of the predominant reef building organism. The capacity to infer coral type from reef morphology can potentially be used to identify and map specific coral habitat in remotely sensed images. More generally, identifying ecological mechanisms underlying other examples of self-generated reef morphology can potentially improve our understanding of present-day reef ecology, because any ecological process capable of shaping a reef will almost invariably be an important process in real time on the living reef.The three-dimensional form of a coral reef develops through interactions and feedbacks between its constituent organisms and their environment. Reef morphology therefore contains a potential wealth of ecological information, accessible if the relationships between morphology and ecology can be decoded. Traditionally, reef morphology has been attributed to external controls such as substrate topography or hydrodynamic influences. Little is known about inherent reef morphology in the absence of external control. Here we use reef growth simulations, based on observations in the cellular reefs of Western Australia’s Houtman Abrolhos Islands, to show that reef morphology is fundamentally determined by the mechanical behaviour of the reef-building organisms themselves—specifically their tendency to either remain in place or to collapse. Reef-building organisms that tend to remain in place, such as massive and encrusting corals or coralline algae, produce nodular reefs, whereas those that tend to collapse, such as branching  Coral reefs are large organic structures constructed over centuries to millennia by relatively small individual organisms. The anatomy of coral reefs varies along a continuum from ‘framework’ reefs consisting primarily of coral skeletons in growth position  to ‘garbThe primary influences on reef morphology differ across spatial scales, from the inherent forms of reef-building organisms at the small scale to the configuration of continental shelves at the large scale. Over intermediate scales (metres to kilometres) reefs exhibit a great diversity of forms, reflecting the multitude of interacting processes affecting them. But within the diversity is a subset of globally recurring forms, indicating that there are some consistent influences governing reef morphology worldwide . Traditi1reticulum: a network or net-like structure. However, the term reticulate has been applied to a variety of lagoon reef forms that probably develop through different mechanisms  reefs, which subsequently extend laterally and coalesce to surround enclosed depressions. The third, proposed by While the seismic and coring has proven beyond reasonable doubt that the cellular reefs of Mataiva and the Abrolhos have grown into their present configuration, it has not provided a generally accepted growth mechanism. Four alternative mechanisms have been proposed. The first, developed independently by Acropora, with a few tabular Acropora appearing as the reefs approached sea level . Water circulation within the intervening depressions is further reduced and the water column is often stratified and stagnant. Live coral is consequently restricted to shallow depths, often less than 15 m in the more enclosed depressions. The shallow subtidal reef slopes and ridges remain dominated by live branching Acropora  and occaMontipora, Goniastrea, Favia, Favites, Merulina, Astreopora, Montastrea, Mycedium, Echinophyllia, Cyphastrea, Alveopora and Lobophyllia. Several apparent developmental stages of this community are present, initiating as a discontinuous cover of small colonies on dead Acropora branches  gradually fill with sediment to the level of the surrounding reef flat. As they fill, the fringe of live Acropora beneath the vertical walls migrates upward, eventually overgrowing the walls and encroaching over the depression floors.In the platform stage  the trends in water quality and coral distribution that were established in the stellate reefs develop further: most depressions are rimmed by vertical walls of massive and encrusting coral, live Based on the survey results described above, the Abrolhos cellular reefs appear to exhibit a straightforward developmental sequence. However, they provide little direct insight into the origin of cellular morphology because the cellular form is already present at the haystack stage. Given the high coral cover on the haystack reef tops and ridges, subsequent growth will inevitably enclose the depressions. Although the haystacks must have progressed through earlier stages to reach their present configuration, the existing space-for-time sequence does not extend back to the earlier stages, presumably because coral colonisation of the Last Interglacial surface ceased when it became deeply submerged and covered by sand in the mid Holocene. If this is the case, even the youngest haystacks probably initiated more than 4,000 years ago. Several processes appear to be suppressing live coral cover, and therefore accretion, within the present-day haystack reef depressions, including reduced water circulation and macroalgal colonisation. But in the absence of earlier stages in the space-for-time sequence it is impossible to determine whether these processes could have initiated the depressions or whether they are consequential. In this situation, computer simulation provides a potential means of investigating the early stages of reef development.Acropora.The model we describe below is configured as a cellular automaton: an array of identically-programmed interacting cells ; Data S2The basic model is initialised by defining the seafloor depth, the array dimensions and the number of coral recruits. We used a default configuration of 30 m depth , a 160 × 160 cell array , and 64 randomly-spaced coral recruits, occupying 0.25% of the array. The 160 × 160 m recruitment array was centered within a larger array of 250 × 250 m, giving the reefs a 45 m margin for lateral growth. We chose 30 m as the default depth, rather than the 40 m of the Maze, because cellular reefs elsewhere appear to be thinner than the Maze; those at Mataiva, for example, are only 10 to 20 m thick . The horGrowth from the initial coral recruits is effected by assigning two growth probabilities to each cell in the array in each iteration: a vertical probability representing the likelihood of the cell growing upward itself and a neighbour probability representing the likelihood of the cell being overgrown by a neighbouring coral . The verAcropora was guided by the output of the basic model  of two metres, such that corals growing to project two metres above any neighbouring cell are prevented from growing upward until the deeper cell grows. Although they cannot grow upward, projecting corals contribute to the growth probability of neighbouring cells in two ways: first, they ‘support’ neighbouring corals, ensuring they are unrestricted by the collapse limit; and second, they may ‘collapse into’ deeper neighbouring cells . Although this representation of collapse involves no subtraction of height from the projecting colony, it remains valid because it is equivalent to the projecting colony growing a metre then collapsing back a metre during the iteration. Because the imposition of the collapse limit slows reef growth, the time represented by each iteration is reduced to 40 years. This gives a mean vertical reef accretion rate of 9 mm/yr, approximating that of the Abrolhos cellular reefs  but within their recorded range. Acropora reefs, demonstrating the emergence of their egg box morphology. The key process is the formation of ridges between adjacent patch reefs. This process begins when the patch reefs meet, whereupon the valleys between them grow rapidly upward to become saddle-shaped ridges  growth. Cellular reefs are more complex because the patch reefs transform as they merge, eventually becoming linked by a network of ridges. This transformation results from the high frequency of collapse in the branching Acropora model. However, it is not simply the frequency of collapse that produces ridges; more important is the distribution of collapse. Because the valleys between merging patch reefs are low points in the reef structure, coral colonies in the valleys are less likely to project above their neighbours than corals elsewhere. Consequently, they are relatively unrestricted by the collapse limit and are therefore more likely to remain in place as they grow, and less likely to collapse, than colonies elsewhere  of branching Acropora. Second, the proportion of in-situ colonies should be highest in the valleys and ridges between adjacent patch reefs, and lowest on reef slopes. Both predictions are supported in the Abrolhos, where Acropora slopes average 37° ± SD 6°  and only require 70 more iterations  to completely fill the platform, whereas the Maze reefs, in 40 m depth, reached present sea level in approximately 4,500 years . Closer examination of these and other cellular reefs is required to determine whether the predominance of branching Acropora is universal, and whether the reef slope gradients and the distribution of collapsed colonies conform to the Abrolhos example.We have separated Mataiva Atoll from the list above as rphology . However Mataiva . We sugg Red Sea , where Pabundant . Howeverabundant . Some ReAcropora  before they merge with adjacent patch reefs, and transitional to nodular after they merge in shallow water  to create cellular reefs, observations worldwide (listed above) suggest it is almost exclusively Acropora: A. cervicornis in the Atlantic and multiple species in the Indo-Pacific. This is probably because branching Acropora have the ultimate strategy for rapid pre-emption of space in lagoon environments. Acropora branches not only grow quickly , emerging from the innate behaviour of branching Acropora colonies just as colony morphology emerges from the innate behaviour of polyps.The default collapse-prone reef builders on modern reefs are branching Acropora holds, the distinctive shapes of cellular reefs in remotely sensed images can potentially be used to identify and map branching Acropora habitat. This could be useful in reef conservation, as the sensitivity of branching Acropora to environmental conditions makes them something of a canary in the coral reef coalmine (Acropora-dominated lagoons will rarely be straightforward, because natural self-limitation and community succession can drastically reduce Acropora cover and vitality in the mid to late stages of reef development. If the relationship between cellular reef morphology and branching coalmine . HoweverOur simulations indicate that reef morphology is fundamentally determined by the extent to which reef-building organisms either remain in place or collapse. This control is best expressed in lagoons, where diminished hydrodynamic and substrate influences allow reefs to grow into their inherent forms. The purest growth forms arise in sheltered lagoons dominated by a single type of reef builder, as in the cellular reefs of the Abrolhos. In these situations, reef morphology can be considered a phenotype of the predominant reef-building organism.While the propensity for collapse appears to explain the nodular to cellular spectrum of lagoon reef morphology, many more relationships between reef ecology and morphology remain to be discovered. Many of the recurrent patterns in reef morphology are likely to be ecological phenomena . QuantifInvestigation of the relationships between reef morphology and ecology is benefiting from advances in the availability, resolution and processing of remotely sensed imagery. However, the single most important research technique remains careful and objective underwater observation. Any consistent correlations between reef morphology and underwater survey data, such as coral type, can be considered potential causal relationships warranting closer examination.In surveying modern reefs, it should be recognised that a reef’s current state may not represent its formative state, particularly if the reef has reached sea level. While seismic and coring can access the history stored within such reefs, both techniques are logistically demanding and expensive. The complementary methods we employed in the Abrolhos, space-for-time substitution and computer simulation, are relatively simple and inexpensive but can provide a comprehensive reconstruction of a reef’s history and a sound basis for extrapolating its future development.10.7717/peerj.935/supp-1Data S1Click here for additional data file.10.7717/peerj.935/supp-2Data S2Click here for additional data file.