Background: Mesothelioma is a rare and aggressive tumor. Bone metastasis often occurs in the later stages of this disease along with poor quality of life. Thus, it is important to explore the tumorigenesis and bone metastasis mechanism of invasive mesothelioma. For this purpose, we established two nomograms based on tumor-infiltrating immune cells and ceRNA networks to describe the molecular immunity and the clinical prediction of mesothelioma patients with bone metastasis.Method: The expression profiles of mRNAs, lncRNAs, and miRNAs of 87 primary mesotheliomas were obtained from the TCGA database; there were four patients with bone metastasis and 83 patients without. We constructed a ceRNAs network based on the differentially expressed RNAs between mesothelioma and bone metastasis. CIBERSORT was used to distinguish 22 immune cell types from the tumor transcriptomes. Kaplan–Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of each factor. Prognosis-associated immune cells and ceRNAs were applied to establish prediction nomograms. The receiver operating characteristic curves (ROC) and calibration curves were utilized to assess the discrimination and accuracy of the nomogram.Results: Differential analysis revealed that 20 lncRNAs, 15 miRNAs, and 230 mRNAs were significantly different in mesothelioma samples vs. bone metastasis samples. We constructed the ceRNA network to include 10 protein-coding mRNAs, 8 lncRNAs, and 10 miRNAs. Nine of 28 ceRNAs were found to be significant in the Kaplan–Meier analysis. Out of the 22 cell types, the fraction of dendritic cells resting (P = 0.018) was significantly different between the bone metastasis group and the non-bone metastasis group. The ROC and the calibration curves, based on ceRNA networks and tumor-infiltrating immune cells, respectively, suggested acceptable accuracy . Notably, based on the co-expression patterns between ceRNAs and Immune cells, we found that the hsa-miR-582-5p, CASP9, dendritic cells resting, ANIX2, T cells CD8, and T cells CD4 memory resting might be associated with the mesothelioma bone metastasis.Conclusion: Based on ceRNA networks and patterns of immune infiltration, our study provided a valid bioinformatics basis in order to explore the molecular mechanism and predict the possibility of mesothelioma bone metastasis. Mesothelioma (MESO) is a rare and aggressive tumor with male predominance  network and immune cell subtypes may predict the prognosis and bone metastasis, we applied the CIBERSORT algorithm to gene expression profiles acquired from the cancer genome atlas (TCGA) to evaluate the cell fraction and establish a ceRNA network. Then, prognosis-associated immune cells and ceRNAs were applied to establish prediction nomograms. Moreover, we assessed the connection between immune cells and ceRNA networks to provide a bioinformatics basis for the discovery of possible molecular pathways.https://tcga-data.nci.nih.gov/tcga/) database, including mRNA, lncRNA, and miRNA. We collected both HTseq-count and Fragments per kilobase of exon per million reads mapped (FPKM) profiles of 87 samples, comprising 83 primary mesotheliomas without bone metastasis and four primary tumors with bone metastasis. Demographic information and the survival endpoints of patients were also retrieved. After filtering non-mesothelioma specific expression genes , differences in the expression of each RNA between mesothelioma and bone metastasis were analyzed using the DEseq2. With a false discovery rate (FDR) adjusted P value < 0.05, the log(fold-change) > 1.0 or < −1.0 was defined as a downregulated or upregulated gene, respectively. The relevant data provided by TCGA are publicly available.Expression profiles of the primary mesothelioma and of bone metastasis were downloaded from TCGA's (http://mirtarbase.mbc.nctu.edu.tw/)  was used to predict lncRNA–miRNA interaction  and calibration curves were utilized to assess the discrimination and accuracy of the nomogram.http://cibersort.stanford.edu/), with LM22 signature and 1,000 permutation applied to the algorithm. Only cases with CIBERSORT P < 0.05 were considered eligible for subsequent Kaplan-Meier analysis.To estimate the proportion of infiltrating immune cells, standard annotated gene expression data were uploaded to the CIBERSORT web portal  curve was used to assess the sensitivity and specificity of the diagnostic and prognostic model, and these were quantified by the area under the ROC curve (AUC). The predictive accuracy of the nomogram was assessed by calibration curve and the concordance index (C-index). The relationships among 22 immune cells and between ceRNAs and immune cells were calculated using Pearson correlation coefficients.P value < 0.05 was considered to be of statistical significance. All statistical analysis was implemented with R version 3.5.1 software  [Package: GDCRNATools  > 1.0 or < −1.0 and FDR <0.05 as cutoffs to identify differential RNA profiles. The baseline characteristics of all the patients available from the TCGA are described in P = 0.001), AXIN2 (P = 0.001), AC017104.1 (P = 0.002), RASSF8-AS1 (P = 0.008), CGN (P = 0.008), MIR4458HG (P = 0.011), hsa-miR-125b-5p (P = 0.012), linc01105 (P = 0.014), and CASP9 (P = 0.012) were found to be significant in the Kaplan–Meier analysis  was significantly different between the bone metastasis group and the non-bone metastasis group.P = 0.027, P = 0.017, P = 0.044, P = 0.080, P = 0.003) was found to be significantly associated with overall survival  and positively associated with AXIN2 expression . The fraction of dendritic cells activated is positively associated with the expression of CASP9  and RASSF8-AS1 . The fraction of T cells CD8 is negatively associated with the expression of AXIN2  and RASSF8-AS1  (Significant co-expression patterns between proportions of immune cells  and ceRN= 0.020) .Malignant mesothelioma, deriving from mesothelial cells, is a tumor type with aggressive invasion and poor prognosis. Distant metastasis, especially for bone metastasis, is often found at the late stage, but its molecular mechanism is still unclear. During tumor initiation and metastasis, molecular, and cellular characteristics play a crucial part and are regarded as prognostic factors . Subsequently, the correlation analysis between ceRNAs and tumor-infiltrating immune cells discovered that both CASP9 and RASSF8-AS1 were positively correlated with Dendritic cells activated. Thus, we focused on CASP9 and RASSF8-AS1 in our study.According to the hypergeometric testing and correlation analysis, the results of the ceRNAs network revealed that hsa-miR-582-5p (miRNA), CASP9 (protein-coding RNA), and RASSF8-AS1 (LncRNA) were correlated  transcribed from the opposite strand of the RASSF8 gene. Divergent lncRNAs can perform biological processes related to the protein through the regulation of the transcription of adjacent protein-coding genes, which is a widely existing new model of gene expression regulation  of the target mRNAs in a complementary base-pairing manner, which has been demonstrated to contribute to the suppression and induction of oncogenesis  was highly expressed in various tumors compared to normal tissue, while CASP9  showed higher expression only in medulloblastoma in the Oncomine .Publicly available datasets were analyzed in this study. This data can be found here: The relevant data provided by TCGA (TM, ZH, and JZ conceived and designed the study. RH performed the bioinformatic analysis. GW, DS, and PY performed the data interpretation. JW and ZZ wrote the manuscript. HY and PH conducted the literature search. XZ, HW, and QL contributed to the revision of the manuscript draft. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


C.elegans while observational studies confirm the phenotypic phenomenon in wild populations of humans and other species including birds. Prominently, paternal age in house sparrows affects offspring fitness, yet the mechanism is unknown. This study provides a first reference of house sparrow sperm small RNA as an attempt to uncover their role in the transmission of the effects of paternal age on the offspring. In this small-scale pilot, we found no statistically significant differences between miRNA and tRNA fragments in aged and prime sparrow sperm. These results indicate a role of other epigenetic information carriers, such as distinct RNA classes, RNA modifications, DNA methylation and retained histones, and a clear necessity of future studies in wild populations.Mammalian sperm RNA has recently received a lot of interest due to its involvement in epigenetic germline inheritance. Studies of epigenetic germline inheritance have shown that environmental exposures can induce effects in the offspring without altering the DNA sequence of germ cells. Most mechanistic studies were conducted in laboratory rodents and  Previously, sperm cells were thought to contribute little but the paternal haplotype to the zygote . Along tOne example of a non-genetic, intergenerational effect is paternal age on offspring fitness, often referred to as the Lansing effect. The Lansing effect describes decreased offspring lifetime fitness with increased paternal age. Previous studies have suggested an epigenetic underpinning of this effect , 24. A LPasser domesticus), small passerines considered an important model species in ecology and evolution [The Lansing effect has previously been demonstrated in wild house sparrows  we produced small RNA libraries of the sparrow sperm RNA and investigated their nucleotide composition and RNA class identity. Collapsing all reads from aged and prime sperm RNA libraries respectively revealed a distinctive size profile between 18 and 50 nt . Both agThe mapping of sequencing reads to the zebra finch genome and the respective mirBase entries identified the presence of 334 miRNAs in sparrow sperm . A compade novo discovery of miRNA annotation by the miRDeep2 algorithm [Since the sparrow miRNA sequences might deviate from the zebra finch miRNA sequences, we explored an alternative analysis of sparrow small RNA based on lgorithm . This apTo investigate the potential impact of sparrow age, we used the DESeq2 package to test for the differential transcript abundance in miRNAs identified with zebra finch miRBase as a reference and those identified with miRDeep2. Neither analysis revealed significant difference in miRNA abundance between aged and sparrows in their prime  and B.To further study the identity of sparrow sperm RNA, we also quantified the sequencing reads mapped to tRNA annotated regions of the zebra finch genome. We detected a variety of reads mapping to tRNA fragments that originate primarily from the 5 prime half of tRNA fragments  in aged The current study provides the first analysis of sperm small RNA in the house sparrow, with the long-term goal to enable comparative studies of environmentally exposed sperm and its contribution to epigenetic germline inheritance.We isolated a pure sperm population from sparrow cloacal sperm samples, as assessed by the RNA size profile obtained from the bioanalyzer . The absThe pure population of sparrow sperm yielded enough RNA  to proceed to small-RNA library generation for NGS. The obtained data were analysed using the zebra finch, a phylogenetically closely related relative of the sparrow, as a reference genome, as to obtain miRNA annotations available from miRBase.The small-RNA library size profile of both aged and prime sparrow sperm samples demonstrated peaks at 32 nt, indicative of tRNA fragments or piRNAs . IdenticThe potential size difference between prime and aged sperm at 22 nt does further not persist when examining unique reads only, indicating no generic difference in miRNA size. Further normalization of read counts of each size by library size does confirm no apparent difference in reads lengths between aged and prime sperm samples .Overall, the RNA size profile is similar to what can be observed in mouse sperm, where the dominating small-RNA class is around 32 nt long . MappingThe second approach of miRNA analysis using miRDeep2 also revealed a range of miRNAs with a seed sequence shared with known zebra finch miRNAs. This could potentially implicate overlapping functionality in the regulation of mRNA targets. However, zebra finch sperm RNA has not been investigated yet, hence the comparison is restricted to somatic zebra finch miRNAs. It is also important to note that the miRDeep2 algorithm was trained and benchmarked using miRNA sequencing datasets derived mostly from somatic cells. The small-RNA population in somatic cells is fundamentally different from sperm cells, due to the active fragmentation of ribosomal RNA  and the The observed high abundance of tRNA fragments in sperm also suggests a role of this RNA class post-fertilization reminiscent of what has been shown in mice , 11. In House sparrows hold great promise for studies on RNA-based epigenetic germline inheritance since sperm can be sampled repeatedly and non-invasively. Sampling from the same individual multiple times as it ages might be a crucial refinement to not only overcome the increased variability due to the outbred background, but also to observe longitudinal effects that take place as individuals senesce. Future analysis of the contribution of miRNAs in gene regulation in sparrows will benefit strongly from this first description of sperm RNA content. Specifically in a germline context with relevance for transgenerational effects, our study paves the way for refined study design to overcome the hurdles of non-inbred animals with higher genetic complexity. Finally, our analysis has implications for ecology, as the first sparrow mirBase entries provide a new tool to study avian miRNA-mediated gene regulation in a non-laboratory species.Passer domesticus (n = 209), were kept at Silwood Park, Imperial College London in seven aviaries, each holding reproductively active sparrows of mixed sex and age. We identified individuals by uniquely numbered and coloured leg rings [ad libitum. Sparrows breed seasonally in spring and summer, and undergo dramatic gonadal development in response to length of photoperiod and air temperature [ad libitum.Captive house sparrows, eg rings . The ageeg rings . Sparroweg rings ). We properature . Therefoperature . Four CBn = 9) or as ‘prime’, 1–4 years young (n = 8), because in the wild, sparrows increase their reproductive output early in life and plateau at 4 years of age, after which reproductive senescence sets in [We classified male sparrows as either ‘aged’, 8–13 years old , followed by centrifugation at 3000 Total RNA was extracted from purified sperm using a slight modification of a standard Trizol protocol (Thermo Fisher Scientific). In brief, sperm samples were resuspended in 1000 μl TriSURE (Bioline) and further homogenized by passing through a syringe (26G) 2–3 times. Subsequently, 200 μl of chloroform (Hi-Media) was added and was mixed vigorously. After 2–3 min, the samples were then centrifuged for phase separation. The aqueous phase was then retrieved and mixed with an equal amount of chloroform. Again, the upper aqueous phase was recovered after centrifugation. For precipitation, equal volume of isopropanol (Hi-Media) and 2 μl of glycogen (20 mg/ul) were added to the aqueous phase. The RNA pellet was washed twice with 75% ethanol (Hi-Media). The pellet was kept to air dry and resuspended in 10 μl nuclease-free water. miRNA quantity was assessed using a Qubit fluorometer . RNA purity was determined by Agilent 2100 Bioanalyser .Total RNA samples were used to create small-RNA libraries for NGS. Small-RNA libraries were generated using the TruSeq Small RNA Sample Prep Kit (Illumina) with slight modification to the manufacturer’s instructions. An average of 29 ng of sparrow sperm total RNA and 20 ng of mouse sperm RNA was used for library preparation according to the manufacturer’s recommendations with the following modifications: 3′ and 5′ adapters were diluted in a 1:4 ratio with RNase-free water and PCR amplification was increased to 18 cycles. The concentration of the libraries of cDNA was analysed using Qubit 2.0 Fluorometer (dsDNA HS Assay), followed size assessment using Agilent 2200 TapeStation . Sequencing was performed on an Illumina HiSeq2500 with 50-bp single-end read length.We carried out a quality analysis of the reads before and after adapter trimming using FastQC v.0.11.2 . After aTaeniopygia guttata  using the STAR aligner [We mapped the trimmed reads to the reference genome of  aligner , 54 with aligner  and/or G aligner , 56 was  aligner .The mapped reads were then analysed for differential expression between the two age classes using DESeq2 workflow v.1.11.22 . AnnotatPasser domesticus genome (v.1.0) [Reads were mapped to  (v.1.0)  with theMouse sperm small-RNA sequencing reads were trimmed as described above and aligned to the mouse genome (mm10) using the STAR aligner  with folAnalysis on 3′ and 5′ derivation of reads mapping to tRNAs was conducted using deepTools .https://www.ebi.ac.uk/arrayexpress/).All data were deposited to and are available under accession numbers E-MTAB-7603 and E-MTAB-7617 at ArrayExpress (dvz007_Supplementary_DataClick here for additional data file.


Structural complexity underpins the ecological functioning of coral reefs. However, rising ocean temperatures and associated coral bleaching threaten the structural integrity of these important ecosystems. Despite the increased frequency of coral bleaching events, few studies to date have examined changes in three-dimensional (3D) reef structural complexity following severe bleaching. The influence of local stressors on reef complexity also remains poorly understood. In the wake of the 2015-2016 El Niño-induced mass coral bleaching event, we quantified the effects of severe heat stress on 3D reef structural complexity across a gradient of local human disturbance. Using Structure-from-Motion photogrammetry we created 3D reconstructions of permanent reef plots and observed substantial declines in reef structural complexity, measured as surface rugosity and terrain ruggedness, and a detectable loss of habitat volume one year after the bleaching event. 3D reef complexity also declined with increasing levels of human disturbance, and with decreasing densities of branching and massive corals. These findings improve our understanding of the effects of local and global stressors on the structural foundation of coral reef ecosystems. In the face of accelerating climate change, mitigating local stressors may increase reef structural complexity, thereby heightening reef resilience to future bleaching events. This is particularly true in highly-complex aquatic habitats such as coral reefs, given the unique physical challenges of the aquatic environment2. The physical structure provided by living corals, and the underlying topographic complexity of the substrate itself, play a critical role in the maintenance of biodiversity in coral reef ecosystems4. Complex structure supports high reef fish abundance and diversity7, mediates the effects of competition and predation on coral reefs8, facilitates the settlement of reef fish9, and may even make reefs more resilient to severe disturbances10. However, the increasing frequency and intensity of severe heat stress and associated coral bleaching events11 threatens the structural integrity of these vital ecosystems13.Habitat complexity has long been known to play an important role in structuring natural communities15, disrupt the relationship between corals and their endosymbiotic algae (Symbiodiniaceae), resulting in expulsion of the algae from the coral tissue16 and heightening the chances of coral mortality17. In 2015-2016, heat stress associated with an extreme El Niño triggered the third major global coral bleaching event18, resulting in severe coral bleaching and mortality throughout all three tropical ocean basins12. Beyond impacts on live coral cover, extreme warming events such as this may also affect the three-dimensional (3D) structure of coral reef ecosystems. Mass coral bleaching can severely reduce reef carbonate budgets19, shifting reefs to a state of net erosion and limiting their ability to recover lost structure. This loss is compounded by increases in the abundance of common bioeroding organisms20 following mass bleaching, and the proliferation of dead coral substrate, which is more easily eroded21. Declines in reef structural complexity were reported following the 1998 mass coral bleaching event23, however these descriptions were primarily qualitative. To date, relatively few studies have quantified changes in 3D reef structural complexity following a severe heat stress event.Coral bleaching occurs when environmental stressors, such as elevated water temperatures24, however this technique is time-intensive, measures a small spatial area, and suffers from high variation due to the particular placement of the chain on the reef25. Recent advances in the quantification of coral reef structural complexity, most notably Structure-from-Motion (SfM) photogrammetry28, enable more precise and robust measures of fine-scale complexity, including a wider array of biologically-relevant structural complexity metrics29. However, the application of SfM photogrammetry to answering questions about reef structural change has so far been relatively limited (but see34). The few studies that have used SfM to quantify changes in reef complexity following coral bleaching have employed these techniques within only one32 to three30 reef plots, limiting our ability to understand the effects of bleaching on reef structure at a wider scale. Gaining a comprehensive understanding of the effects of bleaching-associated coral mortality on the physical structure of reefs and associated ecological processes will require quantification of these changes across environmental gradients and levels of human impact35 , using modern techniques capable of capturing fine-scale structural changes.Quantitative measures of structural complexity on coral reefs have traditionally been made using the ‘chain-and-tape’ method for assessing linear rugosity39  in the central equatorial Pacific Ocean were subjected to globally unprecedented levels of heat stress and suffered over 80% loss of coral cover by the end of the bleaching event . Kiritimati also presents the opportunity to examine the effects of local stressors on coral reef structural complexity: the atoll is characterized by a gradient of local human disturbance with the majority of its population concentrated on the northwest side of the island39 and proximity to local villages . At each site we established three 4 m x 4 m permanent photoquadrats (PPQs) along the 10–12 m isobath to document temporal changes in coral reef benthic composition and structure. Stainless steel stakes were installed to demarcate each of the PPQ’s corners at all sites, and PPQs were spaced approximately 10 m apart from each other. We surveyed all three PPQs at each site in both time points, except at one low disturbance site (where inclement weather conditions only permitted sampling two PPQs in the first time point), for a total of 26 PPQs.To examine changes in 3D reef structure following the 2015-2016 El Niño-induced mass coral bleaching event, we surveyed nine forereef sites around Kiritimati  in the central equatorial Pacific Ocean  were placed at each corner of the plot at known depths and locations to enable accurate orthorectification of the resulting 3D models. A calibration grid and scale bar were also placed along the margin of the PPQ to validate the spatial accuracy of the 3D models. Photographs of the benthic substrate were collected while swimming in a boustrophodonic pattern approximately 3 m above the substrate. All images had 70–80% overlap and were taken from both planar and oblique angles. All photos were taken using a Nikon D750 digital SLR camera with a 24 mm lens  and an Ikelite housing with an 8-inch hemispheric dome port .We captured images of the reef substrate in each PPQ using SfM photogrammetry techniques for modelling coral reef habitats, described by Burns 26. See Burns et al.26 for a detailed description of the model construction process.We used Agisoft PhotoScan software  to process the images and produce spatially accurate 3D reconstructions and 2D orthophotomosaics of the surveyed PPQs. The 3D reconstruction workflow used photogrammetric techniques to produce a dense 3D point cloud, which represents the structure of the PPQ using data points in three-dimensional space, and a digital elevation model (DEM), which is a raster representing the 3D elevation of the reef substrate as a grid of squares. The orthophotomosaics are orthorectified, high-resolution images created using the source photos. The mosaics and DEMs are projected using the same local coordinate system so they can be layered to perform identification and measurement of individual coral colonies33. For each PPQ, the DEM was layered with the accompanying orthophoto and the area of the PPQ was digitized using the editor function in ArcMap. DEM cell size was set to 1.0 cm to capture fine-scale changes in the 3D physical structure of the reef. First, we quantified surface rugosity40 by using the ‘add surface information’ tool to quantify the ratio of 3D to 2D surface area for each PPQ. This metric is related to the traditional measure of linear reef rugosity but uses the ratio of 3D to 2D surface area rather than the ratio of contour to linear distance. Second, we used the ‘benthic terrain modeler’ toolbox to obtain a measure of terrain ruggedness for each PPQ, using a vector ruggedness measure. This metric quantifies surface complexity by measuring the three-dimensional dispersion of vectors orthogonal to the surface of the DEM. Vectors orthogonal to each grid cell are decomposed into their x, y, and z components, and a resultant vector is calculated within a 3 × 3 cell window centered on each cell. Larger values indicate areas of highly complex terrain. This metric simultaneously captures variation in both slope and aspect, and produces a measure of terrain ruggedness that is not strongly correlated with slope41. Third, we used the ‘curvature’ tool to quantify curvature values across the surface of each DEM. Curvature is measured by calculating the second derivative of the slope for each cell within a 3 × 3 window composed of the focal cell and its eight surrounding neighbours. This metric is a quantitative representation of convexities and concavities, which enables the identification of distinct topographic features such as peaks, ridges, channels, and planar regions43. Positive and negative curvature values represent upwardly concave or upwardly convex surfaces, respectively. Because both positive and negative values can correspond to high structural complexity, we used absolute values of curvature for our analysis.We quantified three structural complexity metrics using the 3D analyst and spatial analyst tools in ArcMap . These geospatial metrics were specifically selected to capture variability in topographic complexity that is known to affect the biodiversity and abundance of marine organismsTo calculate volumetric change, we compared the 2015 and 2017 3D point clouds for each PPQ using the open-source software CloudCompare . We aligned point clouds from the same PPQ by performing a rough alignment using the point pairs picking tool to select at least four matching pairs of points in each cloud, followed by a fine registration using the Iterative Closest Point (ICP) algorithm modified to allow for adjusted scaling of the source cloud to the reference cloud. Four permanent pins were installed at the corners of each 4 m x 4 m PPQ prior to this study. The pins from each PPQ were used as matching pairs to ensure the co-registration between point clouds was performed with invariant features that would not confound the temporal analysis. We then computed the volumetric change between the 2015 and 2017 point clouds using the 2.5D volume calculation tool, allowing us to quantify how much the volume of available reef habitat changed over time.https://coraltraits.org/) and Veron44. For each PPQ, we calculated the density of each of these three coral growth forms  by dividing the abundance of each growth form by the area of the PPQ, to account for variation in PPQ size. We also measured the maximum diameter of each coral colony using the ‘measure’ tool and used these measurements to calculate ‘coral cover’ for each growth form in each PPQ. However, we found that coral abundance, coral density, and coral cover were highly correlated (r = 0.83–0.99) for each of the three growth forms, so we proceeded with a single metric  for statistical modelling.To quantify changes in benthic composition, we imported the two-dimensional orthophotomosaics of each PPQ into ArcMap for benthic analysis. For each PPQ, we identified all non-encrusting, intact hard coral colonies >5 cm in diameter to the lowest possible taxonomic level and assigned them a unique label so that the status of each colony could be tracked through time. In total, we enumerated 6655 individual coral colonies. To examine the relationship between the prevalence of different coral morphologies and changes in structural complexity, we also assigned each coral taxon to one of three coral growth forms . Heat stress was modelled as a categorical variable with two levels , because in situ temperature loggers  deployed at sites around Kiritimati indicated a consistent change in water temperature around the atoll throughout the heat stress event .We conducted all statistical analyses using the statistical software R version 3.3.149. We used AIC corrected for small sample sizes (AICc)50 to compare models with and without residual variance structures, and identify metrics for which inclusion of a variance structure improved model fit. For the surface rugosity and absolute curvature models, we applied a varIdent residual variance structure to account for unequal variance between time points (before vs. after) in the full model. The same variance structure was tested for terrain ruggedness but did not improve model fit.To account for unequal variance in the residuals of our full model, we also employed residual variance structures. Variance structures provide a means of modelling identifiable structure in the residuals without the penalty of adding additional parameters to the modelc to compare models and produce a top model set comprised of all models within 4 ΔAICc of the best model. Within this top model set, we calculated multi-model-averaged parameter estimates and 95% confidence intervals for each predictor variable. We also determined the relative variable importance (RVI) for each predictor retained in the top model set, calculated as the sum of Akaike weights across all models containing that variable. RVI is a metric that allows for the ranking of predictor variables, with the most important variable having a maximum possible value of 1.046. Assumptions of normality and homogeneity of variance were evaluated graphically for all models retained in the top model set, and in each case appeared to be reasonable fits. Models were fit using the R package nlme51, and model selection was performed using the package MuMIn52.For each structural complexity metric, we evaluated 40 models by fitting every combination of variables, including a two-way interaction between heat stress and local human disturbance. To perform model selection, we used AIC33. The structural complexity metrics used in this study are quantified in 3 × 3 cell windows of the DEM, thus our approach detected changes in topographic complexity occurring at a 3.0 cm resolution.Ground sampling distance (GSD) is the distance between two consecutive pixel centers and serves as a standard measure of spatial resolution in remote sensing. Smaller GSD values correspond to greater spatial resolution and ability to detect intricate spatial features. The GSD (resolution/pixel) of all 3D reconstructions was less than 0.01 m/pix. All DEMs were rendered with a cell size of 1.0 cm, which is within the range of the GSD computed for all models. DEM cell size was set to 1.0 cm as this resolution has been shown to be effective at capturing temporal changes in coral reef habitat complexityOur models indicated that reef structural complexity was determined by a combination of local human disturbance, heat stress, and the densities of branching and massive corals. However, the strongest predictors varied between surface rugosity, terrain ruggedness, and curvature, and no predictor consistently explained 3D reef structural complexity across all three metrics. While the interaction between heat stress and local human disturbance appeared in the top model sets for both surface rugosity and terrain ruggedness, the effect of this predictor was fairly weak  was located at one of the low disturbance sites  and found that declines in both rugosity and terrain ruggedness were detectable only a year after the 2015-2016 El Niño and mass coral bleaching event, with some PPQs losing close to 1 m3 of reef substrate , we are unable to pinpoint the precise mechanism behind this pattern. Possible drivers include high levels of sedimentation associated with coastal infrastructure that limit coral recruitment53, overfishing of important functional groups such as herbivores that help to maintain the reef in a coral-dominated state54, and direct physical damage to coral structure by fishing gear55 or boat anchors. However, more research will be needed in the future to tease apart and test the impacts of each of these factors on reef structural complexity.Despite the influence of baseline levels of structural complexity on reef resilience and recovery capacity56, we found that branching coral density had a positive impact on all three structural complexity metrics, with this effect being particularly strong for terrain ruggedness. Massive coral density was also positively related to all three metrics, with the strongest effects for terrain ruggedness and curvature. This strong influence of massive coral density on reef structural complexity may seem counterintuitive, given the domed structure of many massive coral genera. However, the massive coral assemblage on Kiritimati includes corals with a variety of finer-scale morphologies that may contribute to increased structural complexity. For example, certain submassive coral species such as Favites pentagona and Goniastrea stelligera can take on columnar forms, while Pavona duerdeni may produce ridge-like structures. The contribution of massive corals to measures of reef complexity may also depend both on the scale at which complexity is measured and the particular metrics that are quantified. For example, massive corals, with their rounded surfaces, may achieve higher values for metrics based on vector dispersion (e.g. terrain ruggedness) or changes in slope (e.g. curvature) compared to plating corals, which have large planar surfaces. These predictions reflect the findings of previous research on reef structure suggesting that complexity is higher for larger massive and branching coral colonies than for plating colonies58.The abundance of particular coral growth forms  is also thought to be an important factor driving levels of 3D structural complexity on coral reefs. Here, we found evidence that levels of reef structural complexity are heavily influenced by the densities of both branching and massive corals Fig. , Table 159 and the high surface-area-to-volume ratio of these species, which would be expected to contribute to high levels of surface rugosity. However, our results may be an artifact of the type of 3D model used in our analysis. While digital elevation models (DEMs) are commonly used in photogrammetry to represent 3D surfaces, including in other coral reef studies57, DEMs are projected from one planar angle and thus are not truly three-dimensional, since they cannot model multiple z points at a single  coordinate60. Given that the structural function of plating corals relies largely on the existence of sheltered spaces beneath the coral plates61, analyzing reef structure from 2.5D DEM projections may underestimate the complexity of foliose and tabulate coral morphologies, compared to other coral growth forms40. These effects may be especially severe in areas where plating corals form complex, multi-tiered structures. As such, we suggest that future studies work towards developing methods to extract complexity data from true 3D digital surface models.The lack of a relationship between plating coral density and reef structural complexity is nevertheless surprising, given the recognized role of tabular corals as “keystone structures” on coral reefs62 as well as physical damage from storms63. Based on our PPQ benthic data, the density of live branching corals on Kiritimati declined by 95% following the mass coral bleaching event. Although many dead branching coral skeletons remain and continue to provide structure to the reef, it is likely that these structures will soon erode, resulting in further declines in reef complexity. While our results, and those of previous studies64, suggest that massive corals may play an important structural role on coral reefs, it remains to be seen how the increased dominance of this coral growth form will affect the ecological functioning of the reefs around Kiritimati. Shifts in coral assemblages from complex reef-building species to smaller, weedy coral species have previously been shown to result in substantial declines in coral reef calcification rates and linear rugosity values65. Continued monitoring of the reef system will be necessary to determine the magnitude of the shift in benthic composition following the mass coral mortality event, and how this change impacts both the physical structure and ecological function of the reef over the long term.The strong dependence of 3D reef structural complexity on the presence of branching corals has implications for the maintenance of reef structure under future climate change. Branching corals, such as acroporids and pocilloporids, are highly susceptible to heat stress and bleaching-associated mortality66. This problem is of particular concern for local communities on small, isolated islands such as Kiritimati, where the majority of people depend on reef fisheries for income and subsistence and opportunities for alternative livelihoods are scarce67. A key question for future research will be to determine how levels of structural decline similar to those observed here, as well as those occurring in subsequent years with further degradation of the reef substrate, impact the structure of reef fish assemblages and populations of other ecologically and socioeconomically-important reef organisms.Given the many important ecological processes and ecosystem services facilitated by coral reef structural complexity, the loss of reef structure has negative implications for both marine organisms and human coastal communities. Declines in reef complexity are expected to compromise coral reef fisheries, potentially leading to up to a three-fold decline in fisheries productivity in severe cases23. While it is vital to understand the effects of severe levels of reef degradation on coral reef communities, studying multiple time points along the trajectory of reef degradation will allow us to gain a better understanding of the rates and impacts of fine-scale structural change, and detect threshold levels of structural complexity below which the ecological functioning of the reef is impaired. As such, it is vital that coral reef monitoring programs incorporate modern methods for quantifying structural complexity into their standard reef monitoring protocols. Photogrammetry techniques provide a time- and cost-effective approach that can be used to this end to objectively quantify multiple measures of fine-scale structural complexity on coral reefs.Prior to the advent of SfM photogrammetry techniques, studies documenting bleaching-induced changes in reef complexity using conventional methods usually took place several years after the end of a severe disturbance event, and documented the extreme and highly-visible collapse of reef structure68. Our results provide further evidence for the negative effects of ocean warming on coral reef ecosystems, demonstrating the need for drastic reductions in greenhouse gas emissions and support for the development of sustainable low-carbon infrastructure.Coral reefs are currently being impacted at multiple scales by a suite of natural and anthropogenic disturbances. In this study we have shown that levels of 3D structural complexity, a vital component of healthy coral reef ecosystems, are impacted by both local and global stressors. Our finding that local human disturbance is a strong predictor of structural complexity suggests that chronic local stressors may have indirect impacts on reef recovery potential through their influence on reef structure. As such, management should focus on mitigating local stressors in order to maintain reefs at ecologically functional levels of structural complexity. However, action at the local level will also need to be accompanied by policy changes at the global scale. Recent research suggests that limiting global warming to 2 °C, the upper limit of the recent Paris Agreement, will still result in annual severe bleaching on the majority of coral reefs within the next few decadesSupplementary_Materials


Orthobunyavirus (family Peribunyaviridae). All viruses in this genus have tripartite, single-stranded, negative-sense RNA genomes, and the three RNA segments are designated as small, (S), medium (M) and large (L). A 559 nt. region of the M RNA segment of LOKV has been sequenced and there are no sequence data available for its S or L RNA segments. The purpose of this study was to sequence the genome of LOKV.Lokern virus (LOKV) is a poorly characterized arthropod-borne virus belonging to the genus The genome of LOKV was fully sequenced by unbiased high-throughput sequencing, 5′ and 3′ rapid amplification of cDNA ends, reverse transcription-polymerase chain reaction and Sanger sequencing.The S and L RNA segments of LOKV consist of 952 and 6864 nt. respectively and both have 99.0% nucleotide identity with the corresponding regions of Main Drain virus (MDV). In contrast, the 4450-nt. M RNA segment has only 59.0% nucleotide identity with the corresponding region of MDV and no more than 72.7% nucleotide identity with all other M RNA segment sequences in the Genbank database. Phylogenetic data support these findings.This study provides evidence that LOKV is a natural reassortant that acquired its S and L RNA segments from MDV and its M RNA segment from an undiscovered, and possibly extinct, virus. The availability of complete genome sequence data facilitates the accurate detection, identification and diagnosis of viruses and viral infections, and this is especially true for viruses with segmented genomes because it can be difficult or even impossible to differentiate between reassortants and their precursors when incomplete sequence data are available. Orthobunyavirus (family Peribunyaviridae). The genus has previously been separated into serogroups, with LOKV classified in the Bunyamwera (BUN) serogroup [Culex tarsalis mosquitoes in California in 1962 [Culicoides spp. midges [Lokern virus (LOKV) is a poorly characterized virus in the genus erogroup . Other v. midges . SerologOrthobunyavirus possess a tripartite, single-stranded, negative-sense RNA genome [S in overlapping reading frames [M [All viruses in the genus A genome . The thrA genome , 9. The g frames . The M Rrames [M . The L Rrames [M .The exchange of RNA segments (genome reassortment) between closely-related orthobunyaviruses can occur when a vector or host is co-infected with two or more viruses , 14. ReaThe genome of LOKV has not been fully sequenced. A 559 nt. region of the M RNA segment that spans part of the 5′ untranslated region (UTR) and Gn coding region has been sequenced (Genbank Accession no. AY593736.1) and no sequence data are available for the S or L RNA segments. The objective of this study is to fully sequence the genome of LOKV and to determine its genetic and phylogenetic relationships with other BUN serogroup viruses.LOKV (isolate FMS 4332) was obtained from the World Reference Center for Emerging Viruses and Arboviruses at the University of Texas Medical Branch in Galveston, TX. The virus had been passaged 13 times in suckling mouse brains prior to receipt and underwent an additional passage in African Green Monkey kidney (Vero) cells at Iowa State University.Chlorocebus sabaeus using Bowtie 2 in order to identify host cell sequences [Total RNA was extracted from subconfluent monolayers of LOKV-infected Vero cells using Trizol Reagent , fragmented using RNase III  and assessed for quality using an Agilent 2100 Bioanalyzer . Libraries were constructed using the Ion Total RNA-Seq Kit v2 (ThermoFisher) and assessed for quality and analyzed at the Genomic Technologies Facility at Iowa State University using an Ion Proton Sequencer (ThermoFisher). Ion-Torrent reads were mapped to the genome of equences . Unmappeequences . All remequences . Contigsequences . AlignmeTaq polymerase (ThermoFisher) in accordance to the manufacturer’s instructions. Primers were designed from the sequences generated by high-throughput sequencing and are available upon request. RT-PCR products were purified using the purelink gel extraction kit (ThermoFisher) and sequenced using a 3730 × 1 DNA sequencer .Reverse transcription-polymerase chain reaction (RT-PCR) and Sanger sequencing were performed to verify LOKV sequences generated by high-throughput sequencing and to close gaps between contigs. Complementary DNAs were generated using Superscript III reverse transcriptase (ThermoFisher) and PCRs were performed using high-fidelity The 5′ and 3′ ends of each RNA segment were identified using 5′ and 3′ rapid amplification of cDNA ends, respectively. Briefly, 20 pmol of pre-adenylated DNA adaptor (5′-rApp/TGGAATTCTCGGGTGCCAAGGT/ddC-3′) was ligated to 1 μg of viral genomic and anti-genomic RNAs using 200 U of T4 RNA ligase 2 (New England BioLabs) and incubated for 1 h at 25 °C. Ligated products were recovered by ethanol precipitation and complementary cDNAs were created using Superscript III reverse transcriptase and an adapter-specific primer. PCRs were performed using adapter- and gene-specific primers, and RT-PCR products were purified and subjected to Sanger sequencing.http://www.ebi.ac.uk/Tools/msa/clustalo/) in order to calculate percent nucleotide and amino acid identities between select sequences.Nucleotide and predicted amino acid sequences of LOKV were aligned with other sequences in the Genbank database by application of BLASTn and BLASTp, respectively. Alignments were also performed using Clustal Omega . The S RNA segment consists of 952 nt., and is most closely related to the corresponding region of MDV with 99.0% nt. identity, followed by NRIV and NORV with 85.0 and 84.6% nt. identity, respectively Table . The S RM proteins of 286, 866 and 173 amino acids, respectively. The deduced amino acid sequence of the polyprotein has greatest identity (77.9 to 78.4%) with the corresponding regions of CVV and its variants . However, NORV and LOKV have overlapping geographic distributions and vector and host ranges. NORV occurs in the western half of the United States and Canada, and has been isolated from rabbits and other rodents as well as mosquitoes [The M RNA segment of LOKV was assumed to be acquired from an undiscovered virus because it has no more than 72.7% nt. identity to the M RNA segments of all other known orthobunyaviruses. Another, less likely, explanation is that the M segment donor is a known virus but the reassortant event was not recent, thus providing sufficient time for a considerable amount of sequence divergence to occur by genetic drift. Of all viruses positioned basally to LOKV in Fig. squitoes , 25.Our data support earlier studies which have shown that the most common reassortment event among BUN serogroup viruses is the M RNA segment exchange. In a classic study, heterologous crosses were performed in the laboratory using BUNV/BATV and BUNV/MAGV, with all resulting recombinants acquiring their S and L RNA segments from the same parental virus . FurtherOrthobunyavirus is the presence of complementary nucleotide sequences at the 5′ and 3′ termini of each RNA segment [A characteristic feature of the genomes of all viruses in the genus  segment , 31–33.  segment , 30. An To conclude, the sequence and phylogenetic data presented in this study provide evidence that LOKV is a reassortant that arose from an M RNA segment exchange. We consider it most likely that LOKV acquired its S and L RNA segments from MDV and its M RNA segment from an unrecognized virus, but additional explanations are also provided. Complete genome sequence data is needed for the accurate detection and identification of viruses, particularly when the virus in question has a segmented genome because reassortants and their precursors could otherwise be mistaken for one another.


The Program for Medical Key Department of Shanghai (no: ZK 2015B17);2. The Key Disciplines Group Construction Project of Pudong Health Bureau of Shanghai (no: PWZxq2017–11).


PIM1 mRNA reacted with rates and specificities comparable to those of short oligonucleotide substrates. The replacement of DNA by DNA/LNA mixmers further increased the cleavage rate. Tris(2-aminobenzimidazoles) were designed to interact with phosphates and phosphate esters. A cell, however, contains large amounts of phosphorylated species that may cause competitive inhibition of RNA cleavage. It is thus important to note that no loss in reaction rates was observed in phosphate buffer. This opens the way to in-cell applications for this type of artificial nuclease. Furthermore, we disclose a new synthetic method giving access to tris(2-aminobenzimidazoles) in multigram amounts.Oligonucleotide conjugates of tris(2-aminobenzimidazole) have been reported previously to cleave complementary RNA strands with high levels of sequence and site specificity. The RNA substrates used in these studies were oligonucleotides not longer than 29-mers. Here we show that ~150–400-mer model transcripts derived from the 3′-untranslated region of the  We deveds (PNA)  or PNA ods (PNA) ,14, theyds (PNA) . PIM1 isds (PNA) . Its mRNds (PNA)  and inclds (PNA) . In addisequence ) has beesequence . Here, w6 suffered from the use of HgO to affect the formation of benzimidazoles from thiourea precursors -end-labeling, 60 pmol of dephosphorylated RNA was incubated with 10 U T4 polynucleotide kinase  and 2 µL [γ32P]-ATP  in 15 µL 1× reaction buffer A (50 mM Tris-HCl (pH 7.6 at 25 °C), 10 mM MgCl2, 5 mM DTT, 0.1 mM spermidine) for 60 min at 37 °C. The reaction was stopped by the addition of an equal volume of 2× RNA sample buffer  bromophenol blue, and 0.02% (w/v) xylene cyanol), and samples were purified by 8% denaturing (8 M urea) PAGE in the case of the 155-mer 14. The labeled RNA was localized by autoradiography and eluted overnight in 1 M NaOAc (pH 5.0) at 8 °C. After filtration , the RNA was precipitated with ethanol, redissolved in ddH2O, and stored at −20 °C. In the case of the 412-mer 15, the gel purification step was omitted, as elution of the radiolabeled RNA from the gel was inefficient. Here, the transcript was passed over Illustra Microspin™ G-25 columns  to remove low molecular weight components.RNAs 8, 9, and 17 were executed as described previously [32P]-labeled RNAs were refolded by preincubation in 50 mM Tris buffer pH 8.0 . Immediately after preincubation, the 5′-[32P]-RNA substrate  and varying amounts of conjugate 12 (2 to 300 nM) were combined at 37 °C in a total volume of 20 µL 50 mM Tris buffer (pH 8.0) to initiate the cleavage reaction. Aliquots of 5 µL were withdrawn after 1, 4, 16, and 20 h of incubation at 37 °C and stopped by the addition of 5 µL 2× RNA sample buffer (see above). Samples without conjugate or containing the noncomplementary conjugate 16 (Cleavage studies with dye-labeled RNAs eviously . For expugate 16  served a12. The 5′-32P-end-labeled primer (5′-GGGCCTGGGATCTGG-3′) was annealed to the uncleaved and cleaved RNA transcripts according to the SuperScriptTM III Reverse Transciptase protocol supplied by the manufacturer (Thermo Fisher Scientific). Reverse transcription was then performed for 50 min at 55 °C following the protocol of the manufacturer. Reactions were stopped by the addition of 20 µL 2× RNA sample buffer (see above) and separated on a 20% denaturing (8 M urea) PAA gel.Primer extension analysis was performed with 1–2 µg of the respective T7 in vitro transcripts (155-mer or 412-mer). Beforehand, RNAs were incubated for 20 h either in the presence or absence of 12.5 µM conjugate 15, the dye-labeled oligonucleotide 5′-Cy5-TTTTTTTTTTCGCCGAUC-3′, and the DNA splint oligonucleotide 5′-GAGGTGGACCGATCGGCG-3′ (both from Eurogentec) were mixed at a ratio 1:1:1.5 in ligase buffer (50 mM Tris-HCl buffer (pH 7.5) containing 2 mM MgCl2, 1 mM DTT, and 400 µM ATP). Poly(vinylsulfonic acid) , an inhibitor for RNase A [A GMP-primed analog of 412-mer  RNase A , was add RNase A .


Nitrogen is essential for all living species and may be taken up from the environment in different forms like nitrate or peptides. In plants, members of a transporter family named NPFs transport nitrate and peptides across biological membranes. NPFs are phylogenetically related to a family of peptide transporters (PTRs) or proton-coupled oligopeptide transporters (POTs) that are evolutionarily conserved in all organisms except in Archaea. POTs are present in low numbers in bacteria, algae and animals. NPFs have expanded in plants and evolved to transport a wide range of substrates including phytohormones and glucosinolates. Functional studies have shown that most NPFs, like POTs, operate as symporters with simultaneous inwardly directed movement of protons. Here we focus on four structural features of NPFs/POTs/PTRs that have been shown by structural and functional studies to be essential to proton-coupled symport transport. The first two features are implicated in proton binding and transport: a conserved motif named ExxER/K, located in the first transmembrane helix (TMH1) and a D/E residue in TMH7 that has been observed in some bacterial and algal transporters. The third and fourth features are two inter-helical salt bridges between residues on TMH1 and TMH7 or TMH4 and TMH10. To understand if the mechanism of transport is conserved in NPFs with the expansion to novel substrates, we collected NPFs sequences from 42 plant genomes. Sequence alignment revealed that the ExxER/K motif is not strictly conserved and its conservation level is different in the NPF subfamilies. The proton binding site on TMH7 is missing in all NPFs with the exception of two NPFs from moss. The two moss NPFs also have a positively charged amino acid on TMH1 that can form the salt bridge with the TMH7 negative residue. None of the other NPFs we examined harbor residues that can form the TMH1–TMH7 salt bridge. In contrast, the amino acids required to form the TMH4–TMH10 salt bridge are highly conserved in NPFs, with some exceptions. These results support the need for further biochemical and structural studies of individual NPFs for a better understanding of the transport mechanism in this family of transporters. Arabidopsis thaliana from a mutant that conferred resistance to the herbicide chlorate and resulted in a decreased nitrate uptake (NRT1/PTR Family)   . NPFs we Family) . Thus, Ae (TCDB) .Drosophila and Caenorhabditis elegans; four in humans). In algae, NRT1s are present in one or two copies, with some algae having none at all. In contrast, plant genomes contain a much larger number of NPFs, from as low as 20 members in the moss Physcomitrella patens to as high as 115 in Glycine max  revealed several new NPFs that were not included in the previous Mt3.5 sequence release, bringing the total number of NPFs to 92, as reported in To recover the NPF sequences, we used the locus identification numbers reported in Multiple sequence alignments of amino acid sequences were performed using the Clustal Omega program via the Web Services interface at the European Bioinformatics Institute (EMBL-EBI) . We remo4  with the first glutamic acid in the motif, Glu41, occupying the 11th position of the TMH1. Therefore, the motif is located slightly closer to the cytoplasmic side of the membrane. The ExxER/K motif in AtNPF6.3 is conserved and when Glu41, Glu44 or Arg45 were mutated to an alanine, a loss of both nitrate uptake activity was observed in yeast- and oocyte-based nitrate uptake essays  have a completely conserved ExxER/K motif  NPFs completely lack any chargeable amino acids in the TMH1 region that aligns with the motif.After aligning 2383 NPF sequences from 42 plants, we identified the ExxER/K motif in each transporter using the AtNPF6.3 sequence as a reference. To help correctly identify the TMH1 in multiple sequence alignment, we included two conserved glycine residues in positions -1 and -2 with respect to the first amino acid of the AtNPF6.3’s TMH1. Both glycine residues located N-terminal to the TMH1 are conserved in 1455 NPF sequences and at least one of the two glycine residues is conserved in all but 72 NPFs, allowing us to unambiguously identify the residues corresponding to TMH1 and the ExxER/K motif  are distributed into two distinct subfamilies, similar to what was observed for the NPF2 members from A. thaliana. The NPF2a subfamily contains a total of 94 transporters (including AtNPF2.1–2.7)  are located in a larger clade , and two (M. acuminata and A. comosus) belong to the Bromelliaceae family, that diverged from the grass family 100 million years ago. The O. sativa genome contains ten NPF7s, seven of which, belong to the NPF7a subfamily. Some OsNPF7a members have been studied. OsNPF7.2 was functionally characterized in oocytes and shown to be a low affinity nitrate transporter that is not pH dependent  are a highly diverse group of photosynthetic eukaryotes from which the terrestrial plant lineage emerged > 1 billion years ago and therefore are phylogenetically closely related to plants . The algChromochloris zofingiensis contains two NRT1s, while Chlorella variabilis NC64A and Coccomyxa subellipsoidea both contain one full and one partial NRT1; Bathycoccus prasinos, Chlamydomonas reinhardtii, Dunaliella salina, Ostreococcus lucimarinus, Ostreococcus tauri, and Volvox carteri have a unique NRT1; Micromonas pusilla sp. RCC299 contains one NRT1, while Micromonas pusilla sp. CCMP1545 does not contain any NRT1. None of the algal NRT1s has been functionally characterized so far.As previously reported, sequenced genomes from Chlorophytes contain one or two NRT1s, or none at all . ChromocB. prasinos, M. pusilla, O. lucimarinus and O. tauris, which are all Mamiellophyceae, a class of green algae. These latter NRT1s also have an arginine on TMH1 that can form the TMH1-TMH7 salt bridge with the TMH7 glutamate. When aligned with our collection of plant NPFs sequences, the Mamiellophyceae NRT1s form a distinct subgroup with two NPFs from the moss P. patens, PpatNPF8.5 and PpatNPF8.6 (Supplementary Figure Chargeable amino acids in the ExxER/K motif as well as the oppositely charged amino acids required to form the TMH4–TMH10 salt bridge are conserved in all algal NRT1s (Supplementary Table NPF transporters are part of the major facilitator superfamily of secondary active transporters and are phylogenetically related to POT, PTR, NRT1 transporters of bacteria, animals and algae . Among tIn this study, we analyzed 2383 NPF sequences from 42 plant genomes including those from monocot and eudicot land plants as well as aquatic plants. The NPFs were surveyed for sequence conservation of four structural features implicated in the proposed proton-driven transport mechanism: the chargeable amino acids in the ExxER/K motif on TMH1, the D/E residue on TMH7, and the oppositely charged amino acids responsible for forming the inter-helical salt bridges between TMH1 and TMH7 and between TMH4 and TMH10.A. thaliana members of the NPF2a subfamily have been functionally characterized and shown to be passive nitrate or chloride transporters implicated in root-to-shoot efflux (Our analysis shows that the chargeable amino acids of the ExxER/K motif are conserved in only 62% of plant NPFs (Table t efflux . We specA. thaliana, O. sativa, and Z. mays NPF7 transporters in this group that have been studied, with observations of proton-driven nitrate symport as well as proton-potassium antiport (Another interesting case is represented by the NPF7 subfamily. When we examined the sequences in this subfamily, we observed that most NPF7s completely lack chargeable amino acids in the ExxER/K motif. There is controversy over the biochemical activities displayed by the several antiport . Our datOverall, our results suggest there exists a correlation between the presence of an ExxER/K motif and secondary active transport; i.e., transport of a substrate driven by the concomitant transport of protons down their energetic gradient. NPF transporters lacking the ExxER/K motif have lost one of the key elements of the proposed mechanism for active transport and thus function as passive transporters.St, proton driven uptake was affected (The glutamate/aspartate residue located on TMH7 is available as a proton-binding site in the outward-open conformation in some bacterial POTs. Although this residue is not strictly conserved in bacterial POTs, when it was mutated in PepT1affected . The coraffected . AnalysiTwo sets of inter-helical salt bridges have been described in POTs that are important for the alternating-access mechanism. One salt bridge has been observed in the inward-open conformation between residues located on TMH1 and TMH7; a second salt bridge is predicted to form in the outward-open conformation between THM4 and THM10 Figure  Newstea. Amino aS. oneidensis, S. thermophilus, Y. enterocolica, and X. campestris (The difference in level of conservation of the residues involved in the formation of the two salt bridges is in agreement with the observation that the largest conformational change is from outward-open to occluded, rather than from occluded to inward-open . Thus, tmpestris , the TMHP. patens which are the only plant NPFs to harbor the residues forming the TMH1-TMH7 salt bridge (Supplementary Figure We examined the four structural features implicated in NPF/POT/PRT proton-driven substrate transport in algal NRT1 transporters and found that the TMH1 ExxER/K motif and the residues contributing to the TMH4–TMH10 salt bridge are strictly conserved in all algal NRT1s. In contrast, the glutamate on TMH1 and the residues contributing to the TMH1–TMH7 salt bridge are only conserved in NRT1s from Mamiellophyceae algae. These NRT1s align with two NPFs from None of the algal NRT1s has been functionally characterized so far, but based on the conservation of two essential structural features, we speculate that, most likely, the NRT1s actively transport substrate in a proton-driven symport mechanism. This is significant because it suggests that these features are ancient and that during algal evolution the mechanism of proton-driven transport has been preserved.NPFs, like POTs, PTRs, and NRT1s, belong to the MFS superfamily that comprises facilitators, symporters, and antiporters, which move substrates across membranes via facilitated diffusion, cotransport, or exchange, respectively . POTs haAnother question, not addressed in our study, is how these transporters evolved to be able to transport such a variety of substrates. So far no direct connection between the protein sequences and the substrate transported has been established, but as we learn more about this family of proteins we may in the future be able to predict which substrate each NPF can transport based on its sequence. More crystal structures are needed of NPFs bound to different substrates. In the meantime, structural modeling of NPFs can help identify important differences between NPFs belonging to different families and transporting different substrates.In summary, our analysis of NPF sequences together with the available functional studies suggest that NPFs are versatile transporters. As symporters, they evolved from their initial role of proton-driven nitrate transporters to transport a variety of substrates, potentially using the same mechanism. As they expanded they acquired new roles: some evolved to passively facilitate nitrate or chloride efflux blurring the distinction between a transporter and a channel; others seem to have evolved to function as proton/potassium antiporters. This appears unique to NPFs.We observed that while many NPFs in plants have maintained elements or structural features that are at the basis of a conserved mechanism for proton-powered substrate transport, a significant number of NPFs lack such elements. In particular, we found that some plants contain NPFs that lack these elements and are passive nitrate or chloride efflux transporters. Other NPFs lack these elements but can still actively transport protons, suggesting the evolution of a different and still unknown mechanism for proton binding and transport. Our genome survey of plant NPFs combined with results from structural studies of NPF/POT transporters makes the case for extending the investigation to NPFs transporters from different subfamilies and plants. Since the function and mechanisms of NPFs cannot yet be easily inferred by sequence similarity, additional biochemical and structural studies as well as investigations of expression patterns and subcellular localization are needed to unravel the fascinating evolution of this family of plant proteins. Finally, although we did not address substrate recognition, we make the case for additional structural studies on NPFs in complex with the variety of different substrates that this family of proteins can transport.AL collected and analyzed the NPF/PTR/POT/NRT1 sequences and crystal structures. NWM conducted the phylogenetic analysis. AL and RD wrote the manuscript. All authors read the manuscript and approved it.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Studies in epitranscriptomics indicate that RNA is modified by a variety of enzymes. Among these RNA modifications, adenosine to inosine (A-to-I) RNA editing occurs frequently in the mammalian transcriptome. These RNA editing sites can be detected directly from RNA sequencing (RNA-seq) data by examining nucleotide changes from adenosine (A) to guanine (G), which substitutes for inosine (I). However, a careful investigation of such nucleotide changes must be conducted to distinguish sequencing errors and genomic mutations from the genuine editing sites. Building upon our recent introduction of an easy-to-use bioinformatics tool, RNA Editor, to detect RNA editing events from RNA-seq data, we examined the extent by which RNA editing events affect the binding of RNA-binding proteins (RBP). Through employing bioinformatic techniques, we uncovered that RNA editing sites occur frequently in RBP-bound regions. Moreover, the presence of RNA editing sites are more frequent when RNA editing islands were examined, which are regions in which RNA editing sites are present in clusters. When the binding of one RBP, human antigen R , was quantified experimentally, its binding was reduced upon silencing of the RNA editing enzyme adenosine deaminases acting on RNA (ADAR) compared to the control—suggesting that the presence of RNA editing islands influence HuR binding to its target regions. These data indicate RNA editing as an important mediator of RBP–RNA interactions—a mechanism which likely constitutes an additional mode of post-transcription gene regulation in biological systems. The life of RNA is more complex than previously thought. Upon transcription from DNA, RNA is modified by a variety of enzymes, resulting in over 140 types of RNA modifications ,3,4 to rADAR1, ADAR2, and ADAR3). Although ADAR3  is considered to be catalytically inactive and a less important player in RNA editing events  by using primer pairs targeting their 3’-UTR regions, which overlapped between the RNA editing islands and HuR-binding regions , suggesting that, to a certain extent, the existence of RNA editing islands is necessary for the efficient binding of HuR. To investigate this finding further, RNA-seq experiment was performed upon silencing of ADAR in HEK-293 cells. When the RNA editing sites were compared for the six genes above, the decreased numbers of RNA editing sites were detected for CYP20A1, GNL3L, LYRM7, and MAVS, especially in 3’-UTR regions  A. To tes islands . The siRand p150 B. For thand p150 . RNA imm regions C. The re regions . HoweverADAR affected the binding of argonaute RISC catalytic component 2 (AGO2). As ADAR was knocked down, it could be possible that the expressions of these genes were reduced, which caused alterations in binding of RBPs to these genes. However, this was not the case, as all genes showed no statistically significant difference in their expressions between the control and silencing of ADAR . As shown in  of ADAR E. These In this study, the extent to which RNA editing events affect RBP binding was investigated. Building upon our recently introduced RNAEditor bioinformatics tool to detect RNA editing sites and islands from RNA-seq data, we examined the overlap between RNA editing events and RBP-bound regions. Here, we experimentally validated the necessity of ADAR RNA editing enzyme to the binding of the RBP HuR in HEK-293 cells. The major findings of this study were (1) many RNA editing sites can be found in the regions that RBPs bind to; (2) RNA editing islands can be used to investigate the influence of RNA editing events to RBP-bound regions; and (3) experimentally, the lack of RNA editing islands upon silencing of RNA editing enzyme ADAR reduces HuR binding compared to the control.ELAVL1 gene) bound the most often to RNA editing sites. Given that HuR stabilizes RNAs to regulate gene expression [CTSS) in endothelial cells [Ctn RNA) [Utilizing CLIP-seq data in HEK-293 cells, we examined 27 RBP and their binding to RNA editing sites. Among these RBPs, by far, HuR  ,63. As wOver three-quarters of the RNA editing sites are found in the intronic regions. Thus, it is more likely that RNA editing affects splicing patterns. It is interesting to note that identifying RNA editing sites in spliced exons and junctions is challenging, as the sequencing reads could be mapped beyond the corresponding exon boundaries ,15,17,28


The ability to form an intramolecular structure plays a fundamental role in eukaryotic RNA biogenesis. Proximate regions in the primary transcripts fold into a local secondary structure, which is then hierarchically assembled into a tertiary structure that is stabilized by RNA-binding proteins and long-range intramolecular base pairings. While the local RNA structure can be predicted reasonably well for short sequences, long-range structure at the scale of eukaryotic genes remains problematic from the computational standpoint. The aim of this review is to list functional examples of long-range RNA structures, to summarize current comparative methods of structure prediction, and to highlight their advances and limitations in the context of long-range RNA structures. Most comparative methods implement the “first-align-then-fold” principle, i.e., they operate on multiple sequence alignments, while functional RNA structures often reside in non-conserved parts of the primary transcripts. The opposite “first-fold-then-align” approach is currently explored to a much lesser extent. Developing novel methods in both directions will improve the performance of comparative RNA structure analysis and help discover novel long-range structures, their higher-order organization, and RNA–RNA interactions across the transcriptome. Eukaryotic RNA processing is remarkably complex. Nascent pre-mRNA transcripts are spliced, edited, capped, cleaved, and polyadenylated . All theThe structure of RNA molecules is believed to comprise two levels: the secondary structure, which is formed by proximate regions in the primary sequence, and the tertiary structure, which also includes long-range interactions . That isIn vivo, the structure of a pre-mRNA is critically important for its processing. Native RNAs are folded co-transcriptionally with the aid of RNA binding proteins (chaperones) or by forming structural intermediates that help to avoid traps in dysfunctional conformations ,8. To daThis review consists of two parts. The first part discusses the existing examples of functional long-range structures in eukaryotic RNAs and outlines molecular mechanisms related to their function. The second part targets bioinformatics readership. It summarizes the current state of the art in the field of comparative RNA structure prediction, with its advances and limitations, and discusses the perspectives and directions where it could next develop. The second part is not designed to be a complete review of all RNA structure predictions methods; thus I cite only the selected computational works that contain the most references to other papers in the field.Functional long-range base pairings in RNAs are known throughout the tree of life . They arThe classic RNA structure probing method based on the difference in reactivities of single-stranded and double-stranded residues, even in its modern high-throughput incarnation , is not Dscam in Drosophila , of which the most fascinating example is Down’s syndrome cell adhesion molecule ila  contains a long-range RNA structure in a constitutive intron preventing intron retention that leads to a lethal frameshift [DST gene undergoes mutually inclusive splicing, a scenario opposite to that of mutually exclusive exons, in which either all exons in the array, or none of them are included in the mRNA. This pattern is likely due to a pair of complementary sequences, which flank the exon cluster and lead to its exclusion by forming an RNA helix that exposes the entire cluster in a loop [While mutually exclusive exon choice is a peculiar splicing pattern, long-range RNA structure is important for coordination of other types of alternative splicing events. To name a few, the osophila . Human sameshift . A clustn a loop .KIF21A and an actin regulator ENAH, each contain a distal intronic site that is bound by Rbfox1 and Rbfox2 in the mouse brain and Rbfox2 in human 293T cells. However, these sites act as splicing enhancers only when brought in proximity of the target exon via the formation of a long-range RNA bridge, a duplex which spans over 10Kb (hence the name) [TERT also depends on the long-range RNA pairing between repeat clusters, which approximates exons 6 and 9 and suppresses exons 7 and 8, thereby promoting the so-called “minus beta” splicing [Several mechanisms were proposed to explain the impact of long-range RNA structure on splicing ,40. Amonhe name) . Splicinsplicing .SMN2) exon 7, the skipping of which is linked to spinal muscular atrophy, a hereditary infant disease leading to early death [PLP1, a gene responsible for X-linked leukodystrophy Pelizaeus–Merzbacher disease, is also regulated by a long-distance interaction between two highly conserved complementary intronic elements [Long-range RNA structures contribute to human disease, including neurological disorders and other pathologies . In partly death ,44. Alteelements . Antisenelements ,48. In tSeveral recent computational and experimental studies independently concluded that long-range base pairings in eukaryotic RNAs are abundant ,27,28,39The universe of RNA structure prediction tools can be broadly divided into methods predicting intramolecular vs. intermolecular structure, on one hand, and methods based on single-sequence (de novo) vs. comparative sequence analysis on the other hand. The majority of de novo intramolecular methods implement dynamic programming for free energy minimization. Dynamic programming is effective only for unknotted structures, and its use for long RNA folding is limited because long-range interactions become shunted by local nested base pairings .Although long-range RNA structure is intramolecular, it could be considered intermolecular from the prediction standpoint. Though intermolecular methods are generally as complex, many of them model RNA–RNA interactions as disassembly of the local structure followed by intermolecular hybridization, i.e., as interaction of pre-formed helical domains (see ,50 for rWhen single-sequence analysis fails, comparative methods provide a powerful alternative. The advantage of comparative methods is twofold. First, they confine the search space to evolutionarily conserved regions, which at least partly reduces the complexity and improves specificity. Second, at least hypothetically, they gain statistical power through observing compensatory changes in covarying positions ,54,55,56n is the length of each sequence [In eukaryotic genomes, however, the comparative formulation becomes intricate when it meets large distances and complex organization of the genes. The conservation rates in exons and introns are fundamentally different, introns may not always be aligned, or the alignment may not be unique. While some methods use phylogenetic substitution models to fold protein-coding sequences ,56, muchsequence . One cansequence . In appl,000 see . It coulDscam went differently in Drosophila and in Chelicerata [Nmnat remained remarkably conserved [Eukaryotic long-range RNA structures listed in licerata , while tonserved . Most ofmost long-range RNA structures are well-conserved;the core of a long-range RNA structure is a long, nearly-perfect complementary match;elements of long-range RNA structures are located in syntenic regions.In sum, functional long-range RNA structures have the following characteristic properties:The first property justifies the so-called “first-align-then-fold” limit of Sankoff’s method , in whicRP11-439A17.4 long non-coding RNA, which is located in antisense to HIST2H2BA gene and overlaps a transcription factor binding site, which also occurs in almost all human histone genes in sense orientation, resulting in a seeming compensatory pattern [The advantage of folding a multiple sequence alignment compared to folding its consensus sequence is the covariation statistics. In general, it is possible to gain statistical power from observing covarying positions only when sequences mutate, e.g., in rapidly evolving viral genomes . HoweverThe second and the third property suggest the opposite, “first-fold-then-align” route  which isThe diagram in Intramolecular structure of eukaryotic RNAs is not limited to hairpins and can span thousands of bases. Recent high throughput experimental assays confirmed that distant interactions in the human transcriptome are very abundant. However, the computational identification of long-range RNA structure remains problematic because the interacting parts are separated by long distances.The principles of computational identification of RNA structure by comparative methods span between two extremes. On one side are the so-called “first-align-then-fold” methods, which essentially look for complementary regions in multiple sequence alignments. They are powerful for well-conserved sequences, but hardly applicable to non-conserved regions in eukaryotic genes that often harbor functional RNA structure elements . On the Characteristic features of long-range RNA structures that are outlined in this review demonstrate that, with additional assumptions, both types of methods are computationally tractable at the scale of eukaryotic genes. Further development in these both directions will expand the capabilities of comparative RNA structure analysis and lead to a discovery of many novel long-range RNA structures, their higher-order organization, and RNA–RNA interactions in the human transcriptome.


It has long been suggested that Darwinian evolution may have started at the molecular level and subsequently proceeded to a level with membrane boundary, i.e., of protocells. The transformation has been referred to as “the first major transition leading to life”. However, so far, we actually have little knowledge about the relevant evolutionary mechanisms – and even about the plausibility – of such a transition. Here, based upon the scenario of the RNA world, we performed a computer simulation study to address this issue.First, it was shown that at the molecular level, after the spread of one ribozyme (RNA replicase), another ribozyme (nucleotide synthetase) may emerge naturally in the system, and the two ribozymes would cooperate to spread in the naked scene. Then, when empty vesicles absorb the two ribozymes via “cytophagy”, the resulting protocells may spread in the system and substitute the naked ribozymes. As for the driven power of such a transition, it was demonstrated that the membrane boundary’s roles to ensure the cooperation between the two ribozymes and to prevent invasion of parasites are important. Beyond that, remarkably, it was found that another two factors may also have been significant: a possibly higher mobility of the raw materials in the environment (free water) and the protocells’ potential capability to move around actively. Finally, the permeability of the membrane to raw materials was shown to be a major problem regarding the disadvantage for the cellular form.The transition from the molecular level to the cellular level may have occurred naturally in early history of evolution. The evolutionary mechanisms for this process were complex. Besides the membrane boundary’s roles to guarantee the molecular cooperation and to resist parasites, the greater chance for the protocells to access raw materials – either due to the diffusion of raw materials outside or the protocells’ active movement, should also be highlighted, which may have at least to an extent compensated the disadvantage regarding the membrane’s blocking effect against raw materials. The present study represents an effort of systematical exploration on this significant transition during the arising of life. The emergence of life on our planet is attractive to scientists because it represents one of the most amazing phenomena in nature. However, related experimental researches turn out to be difficult. Indeed, people’s ambitious intention to demonstrate similar processes in laboratory is still far from success – endeavors along this line were initially marked by the famous Miller-Urey experiment in 1950s . That isIf we do not consider the so-called “chemical evolution”, the evolutionary aspect of the origin of life is just about the preliminary stage of Darwinian evolution. Indeed, Darwinian evolution is an essential feature of the life phenomenon –8, and tThe idea of the RNA world hypothesis , which hIn fact, the RNA world provides a concrete scenario concerning the origin of Darwinian evolution, and is a best target for theoretic work in this area . Even ifIn the scenario of the de novo origin of the RNA world, it was popularly assumed that a functional RNA that can catalyze its own replication (by template-directed RNA synthesis) – may have emerged first from a prebiotic nucleotide pool , 19–21. No matter how, these two functional ribozymes, which favors the replication of RNA from different aspects, may have both emerged in the “prebiotic pool”, and cooperated with each other. This possibility was first explored by our group  and thenTherefore, an interesting event – in the next step – is about the emergence of protocells containing the two ribozymes from the naked scene with the two ribozymes. As mentioned above, the transformation from the molecular form to the cellular form was suggested to have been the first major evolutionary transition in life’s history , 12. ObvThe present model is of the same type as those used in our previous studies on the scenario of the RNA world , 38–43. We assume a two-dimensional system, with an N × N square grid , in which molecular objects are distributed, including the raw materials to synthesize nucleotides , nucleotides, and RNAs. Only molecules within the same “grid room” are possible to interact with each other within one time step.A nucleotide precursor may transform to a nucleotide , and a nucleotide may also decay into a nucleotide precursor. Nucleotides may join to each other, forming RNAs. Phosphodiester bonds within an RNA chain may break. A nucleotide residue at the end of an RNA chain may decay into a nucleotide precursor. An RNA may act as a template and attract substrates (nucleotides or oligomers) by base-pairing. Substrates aligned adjacently on the RNA template may be ligated to each other. The substrates (or the products) and the template may detach from each other if the base pairs between them separate. A molecule may move to an adjacent  grid room. Notably, in the simulation, because each event in the model system occurs with a certain probability within one time step, such a step is also called a “Monte Carlo step” .An RNA containing a characteristic catalytic domain (presumed arbitrarily) may function as a ribozyme. The RNA replicase (REP) and the nucleotide synthetase ribozyme (NSR) have different catalytic domain sequences. A short specific sequence  at 5′ end of an RNA is assumed to be a tag. REP may bind onto a template by recognizing the tag sequence, and act as a template-directed polymerase . In fact, a REP recognizes the tag sequence by base-pairing, and thus, in addition to its catalytic domain, it should also contain a tag-recognizing domain, which is complementary to the tag sequence (see Methods for details).Based on the model for the naked scene, three new kinds of objects, amphiphiles , the raw materials to synthesize the amphiphiles  and protocells, are introduced into the system. Each protocell occupies a single grid room.An amphiphile precursor may transform to an amphiphile, and an amphiphile may decay into an amphiphile precursor. Amphiphiles (with a lower limit of quantity) may assemble into membrane at the edge of a grid room, encompassing molecules within it and forming a “protocell”. A protocell may also break, with its membrane components turning into free amphiphiles. A free amphiphile may join into a membrane, and an amphiphile on a membrane may leave as well. An amphiphile on a membrane may also decay into an amphiphile precursor, which then leaves the membrane. Nucleotides and RNAs are assumed to be impermeable, whereas nucleotide precursors and amphiphile precursors may diffuse across the membrane. A protocell may engulf the substance  in an adjacent room. A protocell may divide into two, and two adjacent protocells may also fuse into one. A protocell may move to an adjacent room. When a protocell divide or move towards an adjacent room, the molecules within the latter are pushed away. Figure Here, it should be mentioned that we do not insist either REP or NSR should have emerged first in the naked scene, and the focus of our study is the emergence of protocell from the naked scene with the two ribozymes. However, choosing a route in which one ribozyme emerged and the other followed is helpful for us to envision a complete story about the origin of the RNA world. Additionally, modeling from the very beginning will provide clues for our later analysis on the emergence of protocells, e.g., about the movement of molecules, which is an important factor associated with the effect of membrane boundary (as we will see in the following). To this end, here we followed the classic scenario: “the REP first”, the same as adopted in the work of Higgs’ group . Note thTNPB; see Table PNF), and the nucleotides, in turn, may decay into nucleotide precursors (with PND). A balance between nucleotides and their precursors may be reached soon  and form RNAs. One REP molecule may appear this way by chance; and then by non-enzymatic template-directed copying, derive its complementary chain. The separation of the two chains would give rise to a ribozyme and a template (the complementary chain), thus launching the REP-catalyzed template-directed replication. Theoretically, the template (the REP’s complement or a second REP) molecule may also appear by random ligation, though less likely. No matter how, such de novo emergence of ribozymes has been demonstrated in our previous modeling studies to be feasible , where n is the quantity of inner impermeable ions, including nucleotides and RNA (measured by the number of nucleotide residues), and b is the quantity of amphiphiles within the membrane. Wherein, b/2 (there are two layers in the membrane) is a “scale” representation of the surface area of the membrane. Consequently, (b/2)3/2 is a scale representation of the cellular space. Thus, n/(b/2)3/2 is a representation of the concentration of the ions. Further, FOP × n/(b/2)3/2 represents the consideration for the “osmotic pressure effect”: a higher concentration of the inner impermeable ions would cause the protocell to be more swollen, and thus amphiphiles on the membrane are less likely to leave – this effect was demonstrated experimentally [The probability of an amphiphile leaving the membrane is assumed to be mentally .PNPP × (b/LAM) / [1 + FDE × n/(b/2)3/2], where n is the quantity of inner impermeable ions and b is the quantity of amphiphiles within the membrane. The element (b/LAM) represents the consideration of the constraining effect of the cellular space on the influx of nucleotide precursors. That is, when b increases, meaning that the cellular space increases correspondingly, the probability of a nucleotide precursor permeating into the protocell would become greater. FDE × n/(b/2)3/2 represents the consideration of the effect of Donnan’s equilibrium [The probability of a nucleotide precursor permeating into a protocell is assumed to be ilibrium  – simplyilibrium  for a dePCD × (1–2 × LAM/b), where b is the quantity of amphiphiles within the membrane. When b is no more than twice that of LAM, the probability is no more than 0, i.e., the protocell could not divide. This assumption considers the fact that the larger the protocell, the more probably it would divide, on account of the physical instability.The probability of protocell division is assumed to be PMV/m1/2, where m is the mass of the RNA, relative to a nucleotide. This assumption represents the consideration of the effect of the molecular size on the molecular movement. The square root was adopted here according to the Zimm model, concerning the diffusion coefficient of polymer molecules in solution [The probability of the movement of an RNA molecule is assumed to be solution .In a time step, for a grid room, collision is checked for a certain number of times. Each time, RNA molecules (including nucleotides) in the grid room are grouped pairwise, simulating collision events. Upon a collision, one of the following three events may occur: the first, they may ligate end to end with each other; the second, if one of them is REP and the other contains a 5′-tag, the REP may bind onto the tagged RNA molecule (as a template); the third, the longer RNA molecule may act as a template and attract the shorter one (as a substrate) based on the rule of base-pairing.PRD when the copying has not yet been completed. The bound polymerase ribozyme on the template also risks chain-breaking with PBB. Certainly, when the ribozyme’s chain breaks, the resulting pieces would drop from the template. A single strand RNA would become a template when a REP binds onto it, or may turn to a template with PRTT when attracting a substrate in the absence of REP.Only unpaired nucleotide residues at the ends of an RNA chain (either at 3′- or 5′-end) may decay. Only when an RNA molecule reaches 5-nt length may it act as a template . A bounIn the light of recent experiments concerning the in vitro molecular evolution of RNA polymerase ribozymes , 29 (als