Both RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging.Here we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3′ shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation.The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility.The online version contains supplementary material available at 10.1186/s12864-021-07381-z. RNA sequencing (RNA-Seq) is a ubiquitous method, used to answer a wide range of biological questions. Methods for aligning, quantifying, normalizing, and analyzing expression data are available through popular packages including Tophat, STAR, cufflinks, SVA, RUV, Combat, DESeq2, edgeR, Kallisto, Salmon, and BWA-MEM –11. EachMany steps in sample processing, e.g. sample storage conditions including temperature and the use of stabilitizing reagents, may specifically decrease sample quality by inducing transcript degradation , 15. NonStandard sample quality control often relies on RNA integrity number (RIN), which quantifies the 28S to 18S rRNA ratio . RIN-basHere, we tested the susceptibility of poly(A)-enriched RNA-Seq results after multiple freeze-thaw cycles. We assessed sample quality independently of RIN by simulating read count variability to capture the noise between technical replicates. We found that each additional freeze-thaw cycle increased the random counts between technical replicates by approximately 4%. Subsequently, differential expression reproducibility approached zero after three freeze-thaw cycles. These effects are not captured by RIN. We find that these effects are reflected in increased 3′ bias in read coverage when combining poly(A)-enrichment with freeze-thaw, a phenomenon that appears to be generalizable to publicly available datasets.We first examined public data to establish initial evidence of the incompatibility of poly(A)-enrichment and frozen samples. Specifically, we analyze the gene-coverage distribution in libraries prepared from frozen samples by either poly(A)-enrichment or ribosomal depletion. Since evidence exists that freeze-thaw enhances transcript degradation, and since poly(A)-enriched samples select mRNA by hybridization to the poly(A)-tail, we expect increased read coverage on the 3′ end of transcripts--3′ bias--when these two factors are combined. To test this expectation, we compared gene body coverage from the 5′ to 3′ end between poly(A)-enriched and ribosomal RNA depleted samples with and without freezing. Specifically, we examined the median coverage percentile, the transcript length-normalized nucleotide percentile at which median cumulative coverage for a given sample is achieved Fig. a.Fig. 1p ≤ 3.99e-9). In an additional study examining the impact of RNA extraction in frozen tissue from the UNC and TCGA tumor tissue repositories gnatures , 54  values between DE runs. Similarity is calculated as the Spearman correlation in the LFC between a pair of subsets of the same size Fig. S; we measAverage RIN and freeze-thaw were measured for all subset pairs. Resulting distributions for all collected values from similarity analyses are displayed in Fig. SNext, subsets of each size were split into two quantile bins for each sample quality metric separately. High sample quality bins (low average freeze-thaw cycles or high average RIN) were compared to low sample quality bins. High sample quality subsets were tested for higher similarity than low sample quality subsets using a one-sided Mann-Whitney U test.Additionally, three linear regressions were fit to quantify the contribution of sample quality metrics to the change in similarity for DE results across subsets. We fit one model to predict similarity from freeze-thaw and RIN, while also accounting for the improvement in reproducibility due to increase in subset size (Similarity ~ Freeze-Thaw + RIN + Subset Size). We also fit two models predicting similarity from freeze-thaw or RIN alone.We adapted a measure of concordance to measure discordance, or the lack of reproducibility, between differential expression results . AverageWe do not use the original concordance at the top (CAT) metric because we are not comparing our results to a gold standard dataset. Instead, we use gene-wise LFC standard deviation across subsets as a measure of discordance. Thus, the average LFC for each gene across DE runs is analogous to the gold standard, and the dispersion from this average indicates a lack of reproducibility. At each combination of subset size and sample quality bins, we calculate discordance and compare it to the gene-wise median effect size Fig. S. We measWe used a linear regression to predict discordance from effect size at each subset size. Additionally, in a separate linear regression, we account for the interaction between effect size and sample quality  at each subset size. Here, sample quality is a dummy variable, assuming a value of 0 for low quality and 1 for high quality. We did not include a term for subset size because regressions were fit within each subset size.geneBody_coverage.py from the RSeQC (v3.0.0) package [th percentile nucleotide is calculated as nucleotidei/lengthgene. Coverage at the ith percentile nucleotide is normalized across all genes within a sample.The distribution of read coverage over each gene body was measured using  package . We measFor a given sample, the median coverage percentile is defined as the nucleotide percentile at which median cumulative coverage is achieved; cumulative coverage is aggregated from the 5′ end to the 3′ end. The larger the median coverage percentile value, the larger the 3′ bias in coverage. We include 9 industry standards in our analysis--six of which had undergone five freeze-thaw cycles and three of which had undergone one freeze-thaw cycle--to explore the impact at higher freeze-thaw counts. We also include ribosomal RNA depleted samples as a negative control.We conducted a meta-analysis of read coverage bias on ten publicly available blood tissue RNA-seq datasets. These datasets were either queried from SRA using pysradb (v0.11.1)  or manuaAdditional file 1.Additional file 2.


But, it is necessary to do some further research to demonstrate the LncRNAs particular regulatory mechanism of inhibiting the metastasis of NSCLC and explore new drugs targeting LncRNAs.Non–small‐cell lung cancer (NSCLC) has become the most lethal human cancer because of the high rate of metastasis. Hence, clarifying the molecular mechanism underlying NSCLC metastasis is very important to improve the prognosis of patients with NSCLC. Long non‐coding RNAs (LncRNAs) are a class of RNA molecules longer than 200 nucleotides, which can participate in diverse biological processes. About 18% of human LncRNAs were recently found to be associated with tumours. Many studies indicated that aberrant expression of LncRNAs played key roles in the progression and metastasis of NSCLC. According to the function in tumours, LncRNAs can be divided into two classes: oncogenic LncRNAs and tumour‐suppressor LncRNAs. In this review, we summarized the main molecular mechanism of LncRNAs regulating NSCLC metastasis, including three aspects: (a) LncRNAs interact with miRNAs as ceRNAs; (b) LncRNAs bind with target proteins; and (c) LncRNAs participate in the transduction of different signal pathways. Then, LncRNAs can exert their function to regulate the metastasis of NSCLC through influencing the progression of epithelial‐mesenchymal transition  Hence, our main purpose is to review recent literature on the relationship between LncRNAs and the metastasis of NSCLC.22.1Many researchers have proved that LncRNAs could regulate the level of miRNAs, which in turn regulate the expressions of miRNA's target genes. LncRNAs might compete with miRNAs as miRNA sponges in tumour progression. Competing endogenous RNA (ceRNA) theory indicated that RNA transcriptions included LncRNAs communication through a new manner mediated by microRNA response elements (MREs).2.1.1LncRNA histocompatibility leucocyte antigen complex P5 (HCP5), which is transcriptional regulated by SMAD3 in NSCLC cells, contains miR‐203 response elements. MiR‐203 expression was down‐regulated in NSCLC and negatively correlated with clinical tumour‐node‐metastasis (TNM) stages.LINC01436 is a long intergenic non‐coding RNA located at chromosome 21q22.12, and its high expression is significantly associated with poor overall survival of patients with NSCLC. Yuan et al found that abnormally high expression of LINC01436 promoted metastasis of NSCLC. LINC01436 functioned as a ceRNA via competitively binding miR‐30a‐3p and regulated target gene endothelial PAS domain‐containing protein 1 (EPAS1).KCNQ1OT1, known as potassium voltage‐gated channel subfamily Q member 1 (Kcnq1) overlapping transcript 1 or Kcnq1 opposite strand/antisense transcript 1, is an antisense LncRNA. Recently, increasing evidence suggested that KCNQ1OT1 played an important role in tumorigenesis and metastasis of different cancers.ZNFX1 antisense RNA 1 (ZFAS1), a LncRNA, originally identified a regulator of mammary development.HMMR‐AS1, as a carcinogen, is involved in advanced TNM staging, greater tumour volume and positive lymph node metastasis because of its high expression. Cai et al found HMMR‐AS1 functioned as a ceRNA of miR‐138, and the high expression of miR‐138 caused the repression of its endogenous target SIRT6.SUMO1P3, located at human chromosome 1q23.2, is firstly identified for its high expression in gastric cancer tissues.LncRNA H19 was the first identified LncRNA in 1990.LncRNA MAF BZIP Transcription Factor G Antisense RNA 1 (MAFG‐AS1) had significantly higher expression in NSCLC than the corresponding normal tissues. But, the mechanism of MAFG‐AS1 in NSCLC progression was still not yet explored. Jia et al uncovered that up‐regulation of MAFG‐AS1 promoted the migration, invasion and EMT of NSCLC cell through serving as a miR‐339‐5p sponge to positively regulate the expression of MMP15.Long non‐coding RNA NNT‐AS1, as a new LncRNA, was transcribed in the opposite direction of nicotinamide nucleotide transhydrogenase (NNT). Shen et al showed that the expression of NNT‐AS1 was up‐regulated in NSCLC tissues and cell lines. High NNT‐AS1 expression was associated with advanced tumour stage and lymph node metastasis of patients with NSCLC.In addition to the above LncRNAs, many studies revealed that other oncogenic LncRNAs, which could play their roles as ceRNAs, were associated with the NSCLC TNM stage and had higher expression in NSCLC than normal tissues. In Table 2.1.2LncNONHSAT081507.1 (LINC81507) was first identified by Peng et al using Agilent Human LncRNA Array.The LncRNA growth arrest‐specific transcript 5 (GAS5), a tumour suppressor gene, was significantly down‐regulated in NSCLC tissues and cell lines, and elevated expression of GAS5 inhibited cell proliferation and induced apoptosis in NSCLC cells.2.2LncRNAs can promote or inhibit the metastasis of NSCLC by directly binding with the target proteins.2.2.1BCYRN1 , a 200‐nucleotide LncRNA, was found highly expressed in some carcinomas of the breast, cervix, oesophagus, lung, ovary, etc, but normally not detectable in the corresponding normal tissues.LncRNA SBF2 antisense RNA 1 (SBF2‐AS1), a 2708 nt antisense RNA to SBF2, was significantly up‐regulated in NSCLC tissues compared with the corresponding non‐tumour tissues. Lv et al found SBF2‐AS1 was a positive factor to promote the metastasis of NSCLC cells. RNA immunoprecipitation discovered that SBF2‐AS2 could bind with a core component of polycomb repressive complex 2, SUZ12. Other chromatin immunoprecipitation assay demonstrated that, after silencing SBF2‐AS1, the enrichment of SUZ12 and trimethylation of histone 3 lysine 27 decreased at the promoter region of P21.MUC5B‐AS1, a new long non‐coding antisense transcript for MUC5B, was significantly increased in NSCLC tissues. Many studies have explored the relationships between MUC5B expression and clinicopathological characteristics, which found that overexpression of MUC5B was associated with early post‐operative metastasis and poor overall survival (OS) in patients with NSCLC.LINC00852 had a positive regulatory role in the progression, migration, invasion and metastasis of NSCLC cells. S100A9 is a calcium‐binding protein. Studies had revealed that tumour cells could secrete S100A9 to recruit myeloid‐derived suppressor cells (MDSCs), thereby promoting cancer growth and forming a special pre‐metastatic immunosuppressive niche.LINC00511 was a newly identified LncRNA, which was up‐regulated in human breast cancer as an oncogene.In addition to these above LncRNAs, there were many other oncogenic LncRNAs, which could play their inducing NSCLC metastasis by indirectly influencing signal pathways transduction via binding with proteins. In Table 2.2.2SPRY4 intronic transcript 1 (SPRY4‐IT1), derived from an intron of SPRY4 gene,LncRNA NORAD was discovered to exploit a novel mechanism for regulating protein function.LncRNA AK126698, a tumour suppressor in NSCLC progression, was found that its expression was lower in cisplatin‐resistant A549/DDP cells compared with parental A549 cells.LncRNA CASC2, as a tumour suppressor, had been discovered in many human tumours. Wang et al reported that CASC2 expression was down‐regulated in NSCLC tissue samples and cells, and significantly associated with lymph node metastasis. Overexpression of CASC2 inhibited the expression of SOX4, which acted as an oncogene and induced EMT process of cancers.LINC00961 was 1546nt in length and located in chromosome 9. Jiang et al uncovered that LINC00961 was significantly down‐regulated in NSCLC tissues, and decreased LINC00961 predicted poor prognosis for patients with NSCLC. Further experiments demonstrated that LINC00961 could act as a tumour suppressor partially via affecting β‐catenin expression.2.3Signal pathways play very important role in regulating progression and metastasis of tumour, such as Wnt/β‐catenin, PTEN/AKT and Akt/mTOR. Recent studies confirmed that many LncRNAs could exert their regulating NSCLC metastasis function by influencing the transduction of different signal pathways.2.3.1LINC01288, located on chromosome 8p12, was first reported by Bian et al as an oncogenic factor for NSCLC.LncRNA tyrosine kinase non‐receptor 2 antisense RNA 1 (TNK2‐AS1), a potential oncogenic LncRNA, was frequently up‐regulated in NSCLC tissues and cell lines. Angiogenesis is one of the hallmarks for tumorigenesis and prognosis.2.3.2Clinically, Wnt/β‐catenin pathway activation predicts increased risk of tumour recurrence in patients with NSCLC.MIR31HG was a LncRNA, identified as >2166 nucleotides in length.Brain cytoplasmic RNA 1 (BCYRN1) was a 200‐nucleotide LncRNA that had been reported to be up‐regulated in many malignant tumours, such as lung cancer, breast cancer and ovary carcinoma.NEAT1 is a novel long non‐coding RNA which considered as a crucial regulator in many kinds of tumours.2.3.3PTEN/AKT signal pathway is a kind of classic intracellular transduction pathway, and the abnormal activation of this pathway is also related with the development of diseases, such as tumours, autoimmune diseases and diabetes mellitus.LncRNA ASAP‐IT1, located in chromosome 8q24.21 in whole length of 1179bp, was initially found in ovarian cancer for its abnormal expression.Focally amplified lncRNA on chromosome 1 (FAL1), a new identified LncRNA located at 1q21.2, had been demonstrated to be up‐regulated and to promote development in several kinds of tumours, especially ovarian cancer.2.3.4GHET1, a long non‐coding RNA, was found to act as an oncogene in some kinds of tumours. For example, GHET1 promoted gastric carcinoma cell proliferation through increasing c‐Myc mRNA stability, and knockdown of GHET1 inhibited cell proliferation and invasion of colorectal cancer.LncRNA small nucleolar RNA host gene 1 (SNHG1) was found overexpressed in diverse cancers, such as liver carcinoma, prostate cancer and NSCLC.MetaLnc9, as a metastasis‐related LncRNA, was identified to be obviously up‐regulated in highly metastatic cells and NSCLC tumour tissues, and its expression correlated with distant metastasis and TNM. Yu et al indicated that MetaLnc9 could interact with the glycolytic kinase PGK1 and prevent its ubiquitination in NSCLC cells, resulting in activation of the oncogenic AKT/mTOR signalling pathway.NF‐κB interacting LncRNA (NKILA), encoded by a gene at chromosome 20q13 just near by the prostate transmembrane protein androgen induced 1 (PMEPA1),FER1L4, a novel LncRNA, was first identified that its down‐regulated expression in human gastric cancer.33.1The EMT is closely related to the high invasiveness and metastasis of cancer cells, including NSCLC.LncRNA HOX transcript antisense RNA (HOTAIR) exhibited significantly higher expression in the tumour tissues than the adjacent non‐tumour tissues in patients with NSCLC.LncRNA activated by TGF‐β (ATB) was first identified in liver cancer.Besides, many other oncogenic LncRNAs were also confirmed to promote the metastasis of NSCLC by inducing EMT, such as LncRNA colon cancer‐associated transcript 1 (CCAT1), LncRNA HNF1A‐antisense 1 (HNF1A‐AS1) and LncRNA LINC00460.3.2Cancer stem cells (CSCs) play an important role in maintaining capacity of tumour metastasis, invasion and recurrence.DGCR5, also known as Linc00037, was a kind of LncRNA involved in lung cancer, Huntington's disease neurodegeneration and hepatocellular carcinoma.Linc00662, a newly discovered LncRNA, had a strong correlation with lower overall survival rate and higher lymph node metastasis rate of patients with lung cancer. Lin28, as an RNA‐binding protein and a reprogramming factor, can promote the tumourigenesis and progression in many human cancers.4LncRNAs have become a new therapeutic target for inhibiting NSCLC metastasis. Up to now, there are more and more LncRNAs been discovered relating with NSCLC metastasis. In brief, the number of oncogenic LncRNAs is much more than tumour‐suppressor LncRNAs. In this review, we only have summarized the main molecular mechanism and regulating manner of some representative LncRNAs to influence metastasis of NSCLC. Besides the LncRNAs mentioned in this review, there are many other LncRNAs associated with the NSCLC metastasis, such as prostate cancer‐associated transcript 1 (PCAT‐1), linc01433, LncRNA HEIH, LncRNA Homo sapiens TatD DNase domain containing 1 (TATDN1) and LncRNA CASC9.5. But, the molecular basis is still unknown. So it is necessary to do some further research.According to our review, the molecular mechanism of LncRNAs regulating NSCLC metastasis mainly includes three aspects Figure : (a) LncAll authors declare that they have no conflict interests.Lixin Wang and Shiguo Zhu conceived and designed the manuscript. Cheng Fang, Lixin Wang and Chenyuan Gong drafted the manuscript. Wenbin Wu collected literature. Shiguo Zhu and Chao Yao contributed to the revised version of the manuscript. All authors confirmed the final version of the manuscript for submission.


Thus modular architecture plays an essential role, in addition to sequence motifs, in determining the specificity of RBP binding and m6A modification. Together, this work builds a comprehensive structure-function model for the XIST RNA-protein complex, and suggests a general strategy for mechanistic studies of large ribonucleoprotein assemblies.Long noncoding RNAs are thought to regulate gene expression by organizing protein complexes through unclear mechanisms. XIST controls the inactivation of an entire X chromosome in female placental mammals. Here we develop and integrate several orthogonal structure-interaction methods to demonstrate that XIST RNA-protein complex folds into an evolutionarily conserved modular architecture. Chimeric RNAs and clustered protein binding in fRIP and eCLIP experiments align with long-range RNA secondary structure, revealing discrete XIST domains that interact with distinct sets of effector proteins. CRISPR-Cas9-mediated permutation of the Xist A-repeat location shows that A-repeat serves as a nucleation center for multiple Xist-associated proteins and m The long noncoding RNA XIST plays a central role in sex-specific gene expression in humans by silencing one of two X chromosomes in female cells. Here the authors show that higher order secondary structure creates the modular domain structure of XIST ribonucleoprotein complex and spatial separation of functions. The list of functional lncRNAs is growing rapidly as more studies are conducted in a wide variety of systems. lncRNAs are distinguished from mRNAs in their processing and ultimate mechanisms of action11. Accumulating evidence suggested that lncRNAs often serve as flexible scaffolds to recruit and coordinate multiple protein complexes to execute specific functions. For example, the yeast telomerase RNA recruits multiple proteins, and relocation of the protein-binding motifs does not disrupt the function of the telomerase complex12. The lncRNA HOTAIR recruits two distinct histone modification complexes, LSD1 and PRC2, to specify combinatorial patterns of histone modifications13.Long noncoding RNAs (lncRNAs) play essential roles in many aspects of gene expression in development and disease14. These two approaches have been applied to several in vitro transcribed lncRNAs, such as XIST, HOTAIR, COOLAIR, and Braveheart18. In vivo DMS-seq on the XIST RNA suggested functional local structure elements but did not reveal high-level organization19. These studies reported vaguely defined domains in these long transcripts, but it remains unclear whether these domains are relevant in physiological conditions. Computational modeling based on chemical probing is prone to errors, especially for long transcripts21.Several new experimental strategies have been developed and applied to lncRNAs to determine structures and interactions that underlie their functions, in particular Selective 2’ Hydroxyl Acylation analyzed by Primer Extension (SHAPE) and dimethyl sulfate sequencing (DMS-seq) probe nucleotide (nt) accessibility, a proxy for RNA base pairing probability9. Several studies used biochemical and genetic screens to find new players in XIST functions26. Xist associates with at least 81 proteins through direct RNA–protein and indirect protein–protein interactions. If occurring all together, the XIST ribonucleoprotein (RNP) is many times the size of the ribosome. A number of XIST-associated proteins mediate critical steps in XCI. For example, the RNA-binding protein (RBP) SPEN binds the A-repeat of XIST and recruits the SMRT-HDAC3 complex to repress transcription27. RBM15/RBM15B recruit the WTAP-METTL3-METTLE14 (WMM) RNA methyltransferase complex to install m6A, which are required for XIST function28. LBR recruits the XIST-coated Xi to nuclear lamina for efficient silencing29, HNRNPU family proteins and CIZ1 attach the XIST RNA to the inactive X chromosome34. In particular, many of these studies have been performed in different systems , such as somatic cells or mouse ESCs (mESCs), and the specific roles of these complexes in each biological context have not been resolved. A key question that remains is how these numerous proteins and the structured XIST RNA are assembled into a functional complex. Moreover, all of the above studies were done using mESCs, and how the human XIST RNP may be organized given substantial sequence divergence is not known.Female placental mammals have two X chromosomes while males only have one. The difference in gene dosage relative to autosomes is compensated by a mechanism called XCI, where one of the two X chromosomes in females is randomly silenced. XIST (X Inactive Specific Transcript) is an essential ~19 kb lncRNA that controls XCI by recruiting multiple proteins to deposit epigenetic modifications, remodel the X chromosome, and silence transcription in specific nuclear compartment27, formaldehyde RNA immunoprecipitation sequencing 35, enhanced crosslinking and immunoprecipitation  crosslinking)36, and PIRCh 37. Together, we find that the entire XIST RNA–protein complex is folded in a modular manner. XIST-associated proteins cluster together in the three-dimensional folded complex, instead of spreading along the linear sequence. The clustering of proteins on the XIST RNA structure predicts a modular organization of XIST functions. The folding of the RNA creates physical proximity that directs the m6A methylase and hence the modifications. Together, this analysis establishes a unifying model for XIST functions.In our prior work, we used three orthogonal methods, PARIS, icSHAPE, and conservation analysis, to demonstrate a modular architecture of the XIST RNA. We found that stochastic folding of the A-repeat domain serves as a multivalent platform to recruit SPEN. To determine the structural of the multi-functional XIST RNP complex, we develop and integrate several methods, including PARIS 6A modification of the RNA, which is required for XIST functions. Together, this study builds a comprehensive model for the XIST RNP and establishes a paradigm for studying the structural basis of lncRNA functions.Using CRISPR-Cas9 genome editing, we reorganize the architecture of the XIST RNA by moving the A-repeat domain to other locations. This reorganization is followed by relocation of XIST-associated proteins, demonstrating a role of the architecture in separating the chromatin-binding and nuclear membrane-binding regions of the XIST RNP, and a role of the domain architecture in guiding protein binding to the RNA, and the m27. While a handful of XIST-binding RBPs have been mapped to distinct regions on XIST, the overall organization of this RNP remains unknown. We reasoned that, in addition to psoralen, other chemical crosslinkers, together with proximity ligation, could capture the higher-order architecture of XIST, therefore providing additional lines of evidence for XIST structure and RNA–protein interactions.Using PARIS, icSHAPE, and structure conservation, we demonstrated that the XIST RNA is folded into modular and compact domains, each spanning hundreds to thousands of nts39. Briefly, cells were lightly crosslinked with formaldehyde to fix RNA–protein interactions, sonicated to small fragments, and then RNA–protein complexes were immunoprecipitated  or the added RNA ligases (in purified RNA) can join two fragments that are crosslinked together, resulting in chimeras. Subsequent paired end sequencing captures the two ends of, and we developed a pipeline to identify, such chimeras Fig. . Short-d27  against the average of input controls in 100-nt windows , and clustered enrichment profiles Fig. , providiTo automatically derive the domain definitions from the high-dimension data, we applied principal component analysis  to map the binding sites of XIST-associated proteinsSimilar to the fRIP-seq, the existence of background renders the transcript-wide enrichment less obvious. To detect binding sites on XIST, we performed unsupervised clustering and PCA analysis on eCLIP data for 121 RBPs in K562 cells Fig.  and 2b. 40. We have previously found that SPEN specifically binds the A-repeat27. In addition, eCLIP and fRIP nominates multiple additional proteins that bind the A-repeat . Seven proteins with strong binding sites on the E domain are discovered based on the eCLIP data Fig. . Two splces Fig. . TARDBP ces Fig. . ZNF622 ces Fig. .Similar to the large BCD domain, the center of the Exon6 domain is generally depleted of protein binding. The most significantly associated protein is HNRNPU, which was detected in both fRIP and eCLIP. Interestingly, the two sides, or feet, of the Exon6 domain associated with multiple proteins. This bimodal placement is consistent with the overall structure of the Exon6 domain, which brings the two ends to physical proximity.45. For HNRNPK and RBM22, the eCLIP read density closely follows motif density, while for KHSRP and TARDBP, there is very little correlation, suggesting that additional factors contribute to the RBP specificity , expressing Xist from the endogenous locus under a tetracycline-inducible promoter25 , suggesting that the overall Xist structure is conserved between human and mouse, despite the major differences in the size of repeats46. Therefore, we conclude that the overall architecture of XIST is conserved in evolution. One of the most highly conserved long-range duplexes, connecting the start and end of the BCD domain, is very stable, with 37 closely stacking base pairs ), and a derived cell line where the A-repeat region was deleted 40  to 3 locations, one in the middle of the large BCD domain , one in the middle of the Exon6 domain , and o ne at the end of the transcript  40 Fig. . Prolong6A-RIP-seq on all the six cell lines , two around the E domain (m6AD2 and D3), and one at the end of the transcript (m6AD4). In addition, we also quantified the modification near the insertion sites (m6AKI5 and m6AKI14). WT mouse Xist harbors m6A modification sites in a pattern very similar to the human XIST . The 5’end of the transcript is also modified at higher levels upon A-repeat insertion at the end of the transcript. These data support both hypotheses that the large domains are excluded from modifications and that regions in physical proximity are modified upon folding of the RNA , and LBR-binding sites are distributed along the XIST RNA Figs. . In part29. Deletion of the A-repeat region reduced LBR binding to the latter two sites, suggesting cooperative binding. These data suggest that sequence alone is insufficient to determine LBR binding. In particular, we found strong anti-correlation for LBR binding and PIRCh enrichment in the E-domain ain Fig. . This paain Fig. . In lighain Fig. .2. Again, we found that the insertions in the middle of the large domains (KI5 and KI14) resulted in local spreading of SPEN near the A-repeat insertion sites, similar to what we observed for m6A modification and LBR binding. The KI17 allele showed spreading around the 17-kb insertion site and modest enhancement of binding at m6AD3. Most distant sites such as m6AD1 and m6AD2 were not affected.To test the role of A-repeat as a nucleation center, we performed infrared CLIP (irCLIP) on mESCs expressing the A-repeat relocation alleles Fig. . A-repea6A 50, phylogenetic conservation of structures, crosslink and proximity ligation of protein-bound RNA structures , and unsupervised clustering and PCA analysis of RBP-binding profiles on RNAs. These orthogonal approaches reveal the organization principles for large RNPs, and their associated functions, from the base pair level to the domain level (hundreds to thousands of nts). Proximity ligation is the basis of PARIS, and chimeric reads between microRNAs and mRNAs have previously been used to pinpoint microRNA targets52. In this work, the crosslink and proximity ligation principle that has been successfully employed in the analysis of chromatin structures and RNA interactions and structures can be extended to the analysis of other RBPs on any RNA of interest, as long as these proteins are crosslinkable. The application of these methods will be instrumental in the analysis of other RNP complexes. More importantly, the discovery of compact RNP domains set the stage for focused in vitro studies of these domains through purification reconstitution and structure analysis using physical methods53.Long RNAs, including the protein-coding mRNAs and lncRNAs, make up the majority of the transcriptome. The dynamic interactions and structures of these RNAs and their protein partners are essential for the exquisite control of gene expression, yet pose major challenges for structure and function analysis. Most of these large RNP complexes are heterogeneous and contain many weak interactions and therefore cannot survive the harsh conditions of purifications for in vitro analysis by crystallography, nuclear magnetic resonance (NMR), and cryo-electron microscopy. Building upon the PARIS method57. Duszczyk et al. determined the in vitro solution structure of a partial A-repeat unit (14 nts out of the 24-nt unit) using NMR and found a stable stemloop structure56. Two studies used chemical probing to measure nt flexibility of the in vitro transcribed A-repeat region and found a number of inter-repeat and intra-repeat duplexes57. These conflicting in vitro models have not been reconciled. Two additional studies used chemical probing in living cells to analyze XIST RNA structures19; however, these models may have limitations because (1) chemical probing reports whether each nt is base paired or constrained by protein binding and does not directly capture the base pairing relationship and (2) the secondary structure modeling is based on the faulty assumption that only one stable conformation exists and thus misses alternative conformations and long-range structures27.Multiple previous studies have analyzed the XIST RNA, either in part or in its entirety, using various methods55. These studies firmly established the modular architecture of the entire RNP complex. Sequence inside the module are more likely to base pair with each other and also interact with similar RBPs, while sequences outside of the module may be excluded for interactions.Using a combination of five orthogonal methods, we have built a comprehensive model of the XIST RNP complex. In our previous study, we have applied PARIS, icSHAPE, and phylogenetic analysis to determine the overall structure of the XIST RNA. In the current study, we incorporated systematic analysis of RNA–protein interactions data based on fRIP-seq and eCLIP and also examined the proximally ligated reads from RNA–protein interactions. Recently, Moore and colleagues analyzed RNA regions associated with the exon junction complex long-distance RNA structures in XIST similar to the PARIS-derived modular domains57. We have used several methods to establish a stochastic inter-repeat duplex model27. Using CLIP and gel shift assays, we found that the A-repeat region forms a multivalent platform to bind the SPEN adapter protein. Importantly, our model has been confirmed by a more recent rigorous phylogenetic analysis of noncoding RNA structure conservation58. In the current study, we further extended the model of the essential A-repeat domain by identifying additional potential interactions, including RBM15/RBM15, SRSF1, U2AF1, and LBR. This multitude of high-affinity A-repeat-associated proteins suggests that the A-repeat serves as a nucleation center for recruiting XIST-binding proteins. Using the A-repeat relocation XIST alleles and CLIP experiments, we showed that the A-repeat is indeed sufficient in spreading physically local RBP binding and m6A modifications .In the present study, we have found that the XIST-associated functions are spatially separated on the RNA structural scaffold. For instance, the A-repeat domain together with the physically close regions of the RNA located at the feet of the other domains associate with proteins involved in transcriptional silencing, m12. The arms of the RNA scaffold can be relocated without affecting their functions. Thus the individual domains in TERC RNA are like words on a billboard; their presence rather than exact order convey most of the message. Here our studies revealed a more complex picture for the flexible domain architecture of XIST: the scaffold serves to insulate certain regions and bridge other regions. The role of structure-induced proximity in guiding RBP binding and m6A modification is similar to the concept of chromatin conformations guiding gene expression regulations. Chromatin structures can either insulate regions from surrounding epigenetic environment or induce spatial proximity to bring regulatory elements to gene promoters. For example, the structure of the X chromosome guides the spreading of XIST to spatially close sites59. The specific order of domains in XIST suggests the existence of grammar rules in lncRNA function. The nature of this grammar, whether for stable RNP assembly or RNP function, remains to be determined.Previous studies on the yeast 1.2-kb long telomerase RNA revealed an RNA scaffold that contains several essential protein-binding sites40, and all generated TXY knockin cell line derivatives  were cultured and treated for 48 h with 4 μg/ml doxycycline before RNA collection. HATX3 cells (XistTX/TXRosa26nlsrtTA/nlsrtTA) were grown under the same conditions before AMT crosslinking and RNA collection25. All mESCs were maintained on 0.2% gelatin-coated plates at 37 °C with mES media, which was changed daily: Knockout Dulbecco’s modified Eagle’s medium + 10% fetal bovine serum + 1% MEM NEAA + 1% GlutaMax + 1% Pen–Strep + 0.2% BME and 0.01% LIF.Male inducible TXY WT, TXY:ΔSX lines (gift from Anton Wutz)35. In the fRIP-seq experiments, formaldehyde crosslinked RNP complexes were sonicated so that the associated RNA fragments are around a few hundred nts, and the sequenced fragments are around 150 nts 36 and reported four proteins that bind XIST specifically (>2-fold enrichment). To determine the interactions between XIST and associated proteins, the eCLIP data were reanalyzed as follows. The total numbers of bigwig files used are 726 and 618, respectively, and the files are named as follows K562/HepG2_RBP_0/1/2_neg/pos.bw. See Supplementary Note The Yeo laboratory performed large-scale CLIP experiments to determine the transcriptome-wide binding sites of >100 proteins in female K562 cells and male HepG2 cells45.RBP motifs were derived from previous publications. These motifs are readily available from previous publications and mapped to the human XIST RNA: HNRNPK (CCCC), KHSRP (CCCC), TARDBP (GTRTG), and RBM22 (CGG)See 27. HATX mESCs were treated with 0.5 mg/ml AMT (Sigma) and crosslinked with 365 nm UV for 30 min in Stratalinker 2400. Cell lysate was digested with S1 nuclease and RNA purified using TRIzol and further fragmented with ShortCut RNase III. RNA was separated by 10% native polyacrylamide gel and then the first dimension gel slices were further electrophoresced in a second-dimension 20% urea-denatured gel. Crosslinked RNA above the main diagonal was eluted and proximity ligated with T4 RNA Ligase I. After ligation, samples were denatured and purified using Zymo RNA Clean & Concentrator and photo-reversed with 254 nm UV for 5 min. Proximity-ligated RNA molecules were then ligated to barcoded adapters and converted to sequencing libraries. Libraries were sequenced on the Illumina NextSeq.The PARIS protocol was performed as previously described with slight modificationsSee Supplementary Note Xist architecture in m6A modification specificity, the A-repeat domain was relocated to several regions in the mouse Xist gene using the CRISPR-Cas9 system. Synthesized, high-performance liquid chromatography-purified single-guide RNAs (sgRNAs) were purchased through Synthego. Donor plasmids were generated through overlap extension PCR of 3 fragments: the A-repeat sequence flanked by two 800 bp homology arms to each respective genomic region. The resulting PCR product was then ligated into the pCR-Blunt-II-TOPO vector, using the Zero Blunt TOPO PCR Cloning Kit according to the manufacturer’s instructions. Cas9 protein with NLS (PNA Bio) was complexed with sgRNAs in microfuge tubes for 10 min at 37 °C and immediately transferred to ice. In all, 1 × 106 TXY:dSX cells were nucleofected with pre-complexed CRISPR RNP and 20 μg of donor plasmid using an Amaxa nucleofector with the manufacturer’s recommended settings for mESCs. Cells were then grown for 72 h before low-density splitting into single-cell colonies. Colonies were picked using a light dissection scope and grown in 48-well plates to establish clonal populations. For genotype screening, genomic DNA was extracted using QuickExtract (Lucigen) and then subject to PCR with primers spanning the knockin site . Clones were screened by looking for a PCR product significantly higher in size than in that of non-targeted TXY:ΔSX cells. PCR products were then Sanger sequenced through Stanford ELIM using the forward PCR primer as a sequencing primer to verify knockin of the A-repeat.To test the role of the mouse To make the guide RNAs, DNA template was designed as follows: T7_promoter + sgRNA + scaffold (GAAATTAATACGACTCACTATAGG [sgRNA] GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTT). The following single-strand DNA were ordered from IDT DNA and used to make the duplex DNA for in vitro transcription.mmX.5136f (middle of BCD domain): GAAATTAATACGACTCACTATAGGAGTTAGAAAGATGTGACCTGGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTmmX.5136r (middle of BCD domain):AAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAACCAGGTCACATCTTTCTAACTCCTATAGTGAGTCGTATTAATTTCmmX.14575f (middle of Exon6 domain):GAAATTAATACGACTCACTATAGGTAAAGCCGGGACCTAACTGTGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTmmX.14575r (middle of Exon6 domain):AAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAACACAGTTAGGTCCCGGCTTTACCTATAGTGAGTCGTATTAATTTCmmX.17623f (after Exon6 domain):GAAATTAATACGACTCACTATAGGTATGTGATCAAAGCAGATGAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTmmX.17623r (after Exon6 domain):AAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAACTCATCTGCTTTGATCACATACCTATAGTGAGTCGTATTAATTTCThe following PCR primers were used for cloning and testing :5KI_LHA_F: GAGAAAGCTTGACTTCCAGAGACATAGAATTTCACTTTG5KI_LHA_R: CCCCGATGGGCAAGAATATATAAACAATGAAGGGCGATAGCACCCATGAC5KI_repA_F: TGTCATGGGTGCTATCGCCCTTCATTGTTTATATATTCTTGCC5KI_repA_R: ATCTCCATCAGTTAGAAAGATGTGACCTGACTCACAAAACCATATTTCC5KI_RHA_F: GGTGGATGGAAATATGGTTTTGTGAGTCAGGTCACATCTTTCTAACTG5KI_RHA_R: GAGAGAATTCTACAAATAAGTCTTCACCAGATG14KI_LHA_F: GAGAAAGCTTTGCCCAGGTCACATTATG14KI_LHA_R: CCCCGATGGGCAAGAATATATAAACAATGAAACAGTTAGGTCCCGGCTTTATAG14KI_repA_F: GTTCTATAAAGCCGGGACCTAACTGTTTCATTGTTTATATATTCTTGCC14KI_repA_R: AGAAAGTAATCACTGTTCACTGATAAAGCCAACTCACAAAACCATATTTCC14KI_RHA_F: GGTGGATGGAAATATGGTTTTGTGAGTTGGCTTTATCAGTGAACAG14KI_RHA_R: GAGAGAATTCTATATAATTCTTTAAAAATATTATTCACTCAG17KI_LHA_F: GAGAAAGCTTTCCTTACTATAATATACTCAAGGTGG17KI_LHA_R: CCCCGATGGGCAAGAATATATAAACAATGAATGGTAGGATGTGCTTAATTG17KI_repA_F: ATATTGCTACCAATTAAGCACATCCTACCATTCATTGTTTATATATTCTTGCC17KI_repA_R: GTACACAGTTCATTTATGTGATCAAAGCAGATGAACTCACAAAACCATATTTCC17KI_RHA_F: GGTGGATGGAAATATGGTTTTGTGAGTTCATCTGCTTTGATCACATAA17KI_RHA_R: GAGAGAATTCAGGGCCACTGAGTTAGAAACGenotyping primers5KI_Genotype_F and 5KI_Genotype_R: CCAGCCCTGTGTGCATTTAG, AGCCTTATCCAGTGTCCAGG14KI_Genotype_F and 14KI_Genotype_R: TTCCACCTCCTCAGTCAAGC, TGCTTTGGTGAGGCTCAGTA17KI_Genotype_F and 17KI_Genotype_R: AGCAAGCCTGACCCTAAAGT, TGGTGGGAAGATGACTCCAGsgRNA test primers5KI_gRNA_F and 5KI_gRNA_R: CCAGCCCTGTGTGCATTTAG, GGTTTGATTCCCCAGCACAG14KI_gRNA_F and 14KI_gRNA_R: GCCTGGTGTGCAATGACTTT, TGGGATTATCTCACTCTGGCC17KI_gRNA_F, and 17KI_gRNA_R: GAGCCAGGTTGAAGAGGTCT, AACTACCCCACCCACTCAACTotal mES RNA was subjected to one round of Poly(A)Purist MAG treatment to enrich for polyadenylated RNAs as per the manufacturer’s instructions (Ambion). RNA was then fragmented to 100-nt median-sized fragments using RNA Fragmentation Reagents (Ambion) and subjected to one round of m6A immunoprecipitation. For immunoprecipitation of RNA, 5 μg of m6A antibody (Millipore) was coupled to 40 μl Protein A Dynabeads (Novex) in 100 μl 1× IPP Buffer  overnight at 4 C. Beads were then washed twice in 1× IPP Buffer. Fragmented RNA was denatured at 70 °C for 2 min, cooled on ice, and bound to antibody beads in 185 μl 1× IPP Buffer for 3 h at 4 °C. Beads were then washed sequentially with (2×) 500 μl 1× IPP Buffer, (2×) 500 μl Low Salt Buffer , (2×) 500 μl High Salt Buffer , (1×) 500 μl TET Buffer . Beads were eluted with 50 μl RLT Buffer (Qiagen RNeasy Mini Kit) and incubated at 25 °C for 5 min and recovered with the RNeasy Mini Kit followed by concentrating with Zymo RNA Clean & Concentrator in 10 μl water. Ten nanograms of input RNA (before immunoprecipitation) and 10 ng of immunoprecipitated RNA were then used to prepare sequencing libraries using the SMARTer Stranded RNA-Seq Kit—Pico Input Mammalian (Clontech #634411), as per the manufacturer’s instructions. Sequencing libraries were pooled and sequenced on the Illumina MiSeq (files named *mar08* and *apr26*) and NextSeq (files named *jun24*).See Supplementary Note 47. Then the data were lifted to the mature XIST transcript coordinates (without introns) using custom python scripts.The miCLIP bedgraph files in GSE63753 were downloaded from GEO and lifted to hg38 using the liftOver tool from UCSC genome brower60. Briefly, mESCs with engineered Xist mutations were cultured with standard conditions and induced to express Xist (see earlier description on cell culture). Afterwards, cells were lysed, immunoprecipitated with antibodies for these RBPs and treated with S1 nuclease. RNP complexes were resolved on denatured polyacrylamide gels and regions above the protein size were excised for RNA extraction and library preparation.The irCLIP experiments were performed as described previouslySequencing output reads were processed by bbmap to remove duplication on fastq level. Remained reads were trimmed off the 3’ solexa adapter and against sequencing quality q20 by cutadapt (version 2.4). Trimmed reads were mapped first to RNA biotypes with high repetitiveness by bowtie2 (version 2.2.9) to our custom-built indexes: rRNAs , snRNAs (from Ensembl GRCm38.p6/mm10), miscRNAs (from Ensembl GRCm38.p6/mm10), tRNAs (from UCSC table browser GRCm38.p6/mm10), RetroGenes V6 (from UCSC table browser GRCm38.p6/mm10), and RepeatMasker (from UCSC table browser GRCm38.p6/mm10). Remained reads were mapped to mouse genome GRCm38/mm10 by STAR (version 2.7.1a) with junction file generated from mRNAs and lncRNAs by Genocode GRCm38.p6/mm10 GTF file. Only reads uniquely mapped to the mouse genome were included in the downstream analysis. The RBP-binding loci as suggested by the irCLIP method, was defined as 1-nt shift to the 5’ end of each mapped read. Each locus was extended 5 nts upstream and downstream to shape a local interval; only intervals overlapped between two replicates were included. Then five nts were trimmed from each side of the overlapped interval to shape the final cluster. Cluster annotation was processed against the Genocode GRCm38.p6/mm10 GTF file. Reads annotated to Xist gene were re-mapped to the Xist mini-genome. Normalization on Xist was processed in the same way as for m6A data.n. Asterisks define degree of significance as described in the figure legends. All Student’s t test and Mann–Whitney U test were analyzed as two sided. All the sequencing data were aligned to mouse and human genomes (mm10 and hg38) or custom-made mini-genomes (mmXist and hsXIST). Statistical analyses and graphics were performed using Python, R, and Microsoft Excel.In the relevant figures, figure legends denote the statistical details of experiments, including statistical tests used, kind of replicates, and the value of Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1


Chlorurus), adjacent habitat was more important in explaining fish density and biomass. Notably, wrasses were positively influenced by the amount of sand and macroalgae in the adjacent seascape. Adjacent habitat metrics with the highest relative importance were sand (positive), macroalgae (positive), and mangrove habitats (negative), and fish responses to these metrics were consistent across fish groups evaluated. The 500‐m spatial scale was selected most often in models for seascape variables. Local coral reef variables with the greatest importance were percent cover of live coral (positive), sand (negative), and macroalgae (mixed). Incorporating spatial metrics that describe the surrounding seascape will capture more holistic patterns of fish–habitat relationships on reefs. This is important in regions where protection of reef fish habitat is an integral part of fisheries management but where protection of nonreef habitats is often overlooked.Nonreef habitats such as mangroves, seagrass, and macroalgal beds are important for foraging, spawning, and as nursery habitat for some coral reef fishes. The spatial configuration of nonreef habitats adjacent to coral reefs can therefore have a substantial influence on the distribution and composition of reef fish. We investigate how different habitats in a tropical seascape in the Philippines influence the presence, density, and biomass of coral reef fishes to understand the relative importance of different habitats across various spatial scales. A detailed seascape map generated from satellite imagery was combined with field surveys of fish and benthic habitat on coral reefs. We then compared the relative importance of local reef  and adjacent habitat (habitats in the surrounding seascape) variables for coral reef fishes. Overall, adjacent habitat variables were as important as local reef variables in explaining reef fish density and biomass, despite being fewer in number in final models. For adult and juvenile wrasses (Labridae), and juveniles of some parrotfish taxa ( How do multiple habitats in a seascape influence the presence, density, and biomass of coral reef fishes? Although local coral reef habitat is the major influence, non‐reef habitats within 500 m can strongly affect reef fish distributions, especially juvenile fish and wrasses. In tropical coral reef ecosystems, nonreef habitats include, but are not limited to mangrove forests, seagrass meadows, and macroalgal beds. Though each habitat offers unique and essential ecosystem services, there is ample and increasing evidence that these nonreef habitats are important to coral reef fishes  and seascape (across patches) scales , Halodule pinifolia, H. uninervis, Thalassodendron ciliatum, Enhalus acroides, Halophila beccarii, H. minor, H. ovalis, H. spinulosa, and Thalassia hemprichii  collected in situ in 2016–2018 informed the maximum‐likelihood classification, with 70% of points used for training, and the remaining 30% used for validation of the classified map. The map was then manually reviewed and edited for obvious errors, smoothed using the majority filter in ArcGIS, and converted to polygons for spatial analysis. Map validation identified 72% accuracy of habitat classification using the maximum‐likelihood method.Remotely sensed satellite imagery paired with in situ georeferenced habitat data were used to create a marine benthic habitat map. Images from the GeoEye and PlanetScope satellite sensors were acquired from the Digital Globe Foundation, and Planet, respectively. The GeoEye satellite provides a spatial resolution of 1.84‐m, and Planet provides a 3‐m resolution, both across four spectral bands of blue, green, red, and near‐infrared (NIR) Figure . Both se2.4n = 16). Adjacent habitats used for spatial analysis were seagrass, macroalgae, sand, reef flat, and mangroves. For each location, distance to the nearest habitat type was measured using edge‐to‐edge distance between survey sites and each habitat. Because reef fish species respond to benthic habitat at varying spatial scales, we used a multi‐scale approach to measure the area of each habitat .Fish and benthic survey locations were overlaid onto the classified habitat map to calculate spatial statistics of the seascape surrounding each site n = 6. Adjace2.5Scarus and Chlorurus, where Scarus are scrapers and Chlorurus excavators. Hipposcarus was included in the "Scarus" group, and Cetoscarus was included in the "Chlorurus" group based on their feeding modes. Models for juvenile reef fish density were only possible for wrasses, and the parrotfish groups Scarus and Chlorurus, due to the lack of juveniles, observed from other families. Fish groups were also separated by coral reef zones, that is, reef crest and slope.Boosted regression trees  for each response variable. The "best" scale for each habitat type was selected as the radius with the highest relative importance, and only that scale was included for further analysis. Variables with correlation values >0.8  were run in a BRT, and only the variable with highest relative importance was selected for the remaining analysis. Full models were then run with these preselected variables with an interaction depth of 3 and bag fraction of 0.75 using the gbm.step method in the gbm package, and were calibrated for best results by altering the learning rate to achieve the optimal number of iterations between 1,000–10,000 trees, based on a 10‐fold cross‐validation procedure. The gbm.simplify process was used to reduce the number of variables by an iterative backwards stepwise removal of the least influential variables using k‐fold cross‐validation until the change in predictive deviance was minimized. The simplify process selected the nine most influential variables, and NTMR status was the tenth variable to evaluate any reserve effect. To account for stochasticity and incorporate uncertainty values for relative importance, models were bootstrapped (sampling with replacement) 100 times. Error in relative importance and deviance explained values were measured by 95% confidence intervals from the bootstrapping process. Cross‐validation deviance (CV deviance) was calculated by subtracting the CV deviance from the null deviance and dividing by the null deviance. Mean relative importance was used as an indicator for variable importance. Because models had 10 variables, relative importance values greater than 10% were considered influential as they were selected more frequently than expected by chance. The mean relative importance was summarized only for influential variables (>10% relative importance) and compared between variables categories  (Table 3p = .248) (Table p = .011) . The remaining taxa had no significant differences in mean relative importance between the two habitat categories. Juvenile fish density  had significantly higher mean relative importance for adjacent habitat variables   between local reef and adjacent habitat was similar  Figure . In cont) Figure . For wra) Figure .Individual variables with the highest mean relative importance were adjacent habitat metrics of sand and macroalgae Figure . Both adChlorurus juveniles were most influenced by adjacent sand in the seascape (positive relationship), and adjacent macroalgal habitat (positive)  and amount of sand within 500 m (positive relationship). Across all juvenile BRT models, percent cover of sand at the local reef scale was the most frequently selected variable (5 of 6 models) with a negative relationship, followed by a positive relationship with percent cover of fragile coral (4 of 6 models).For juvenile fish, adjacent habitat variables had higher relative importance compared to local coral reef variables Figure . Wrasse Appendix . Scarus Scarus juvenile density was positively affected by fragile coral cover at the local reef scale, but adult density and biomass was predominantly influenced by macroalgal cover at the local reef scale (mixed effects), and negatively influenced by distance to mangrove and seagrass (Appendix Chlorurus models (models 15–19), live coral cover on the local reef positively influenced adults, whereas juveniles were positively influenced by the amount of adjacent sand in the surrounding seascape. The area of seagrass in the surrounding seascape was also a common predictor in Chlorurus models, appearing in three of six models with a negative response to area of adjacent seagrass. For snapper presence (models 31–32), influential variables were almost all local reef. For surgeonfish (models 1–4), NTMR size was included as an influential predictor, positively affecting density and biomass of fish on the reef crest and was the only fish group to have an NTMR variable selected as influential. For damselfish density (models 27–28), the reef crest model was influenced by depth, whereas the reef slope model was influenced by distance to seagrass and mangrove. For grouper presence (models 31–32) on the reef crest there was high importance of adjacent habitat variables, whereas on the reef slope, the presence of groupers was influenced by local coral reef variables. Finally, for butterflyfishes (models 5–8), mangrove variables were present in all models, with a negative influence on density and biomass of fish.Wrasses were the only fish group to have higher relative importance of adjacent habitat spatial metrics for both adults and juveniles Figure . For wra4Chlorurus and wrasse were more strongly influenced by adjacent habitat metrics than local reef factors. For adjacent habitat, the relative amount of sand and macroalgae in the surrounding seascape were the strongest predictors of reef fish density and biomass, with seagrass and mangrove habitat having a lesser effect.Overall, reef fish presence, density, and biomass were affected primarily by local within‐reef attributes and were secondarily influenced by adjacent habitat in the seascape. Although local reef variables were selected most often in models, the mean relative importance for influential variables was similar between adjacent habitat and local reef. This implies that reef fishes in our seascape are responding to features beyond their immediate vicinity and that adjacent habitat measures of the seascape at the scale of hundreds of meters are important to consider. Other research comparing the influence of local coral reef and adjacent habitats on coral reef fishes has found that seascape‐scale habitat can be more important than local reef habitat   rather than by adjacent habitat. Juvenile Scarus parrotfish have been one of the more conspicuous taxa observed in nonreef habitats  is demonstrating multiple postsettlement habitat selection strategies and/or multiple ontogenetic habitat shifts. Though research has detailed how ontogenetic shifts occur by changes in diet  including their juveniles. We found that coral reef fishes responded to adjacent habitats across multiple spatial scales but measuring the surrounding seascape at a 500‐m scale obtained the best model results. Employing a multi‐scale approach better explained reef fish patterns and incorporation of multiple adjacent habitats across an island seascape may offer deeper insights into the structuring of coral reef fish assemblages. This is especially relevant for regions like the Philippines where nonreef habitats are heavily impacted by coastal development, fishing pressure, and pollution, and where juvenile fishes are often the direct or incidental targets of fisheries. When considering management strategies that are spatially focused, such as NTMRs, adopting a multi‐scale seascape‐level approach would consider other nonreef habitats that can often be overlooked in the management process ; Data curation ; Formal analysis (lead); Funding acquisition ; Methodology (lead); Writing‐original draft (lead); Writing‐review & editing . Eva C. McClure: Conceptualization (supporting); Data curation ; Funding acquisition ; Methodology (supporting); Writing‐review & editing . Rene Aberin Abesamis: Project administration ; Supervision (supporting); Writing‐review & editing . Garry R Russ: Funding acquisition ; Project administration ; Supervision (lead); Writing‐review & editing .Appendix S1Click here for additional data file.


Living cells genetically engineered to execute nucleic acid nanotechnology programs thus have outstanding potential to significantly extend the current limits of synthetic biology. This perspective discusses the recent developments and future challenges in the field of synthetic bionanotechnology. Thus far, researchers in this emerging area have implemented dozens of programmable RNA nanodevices that provide precise control over gene expression at the transcriptional and translational levels and through CRISPR/Cas effectors. Moreover, they have employed synthetic self-assembling RNA networks in engineered bacteria to carry out computations featuring up to a dozen inputs and to substantially enhance the rate of chemical synthesis. Continued advancement of the field will benefit from improved in vivo strategies for streamlining nucleic acid network synthesis and new approaches for enhancing network function. As the field matures and the complexity gap between in vitro and in vivo systems narrows, synthetic bionanotechnology promises to have diverse potential applications ranging from intracellular circuits that detect and treat disease to synthetic enzymatic pathways that efficiently produce novel drug molecules.Enabled by its central role in the molecular networks that govern cell function, RNA has been widely used for constructing components used in biological circuits for synthetic biology. Nucleic acid nanotechnology, which exploits predictable nucleic acid interactions to implement programmable molecular systems, has seen remarkable advances in  RNA plays a pivotal role in the molecular networks that govern cellular function ,2, serviin vivo RNA networks.In parallel with these developments, the field of nucleic acid nanotechnology, which exploits the predictable base-pairing properties of DNA and RNA for implementing molecular systems, has seen remarkable advances in nanoscale self-assembly and molecular computation. Using DNA-based self-assembly methods, it is now possible to construct three-dimensional structures of virtually any geometry with nanometer-scale resolution using hundreds or even thousands of programmed DNA strands –15. The in vivo could be used to construct synthetic organelles that localize enzymes and constrain reactant flux for highly efficient synthesis of valuable chemicals. Moreover, genetically encoded neural networks could lead to single cells with the computing power of multiple neurons and the ability to respond intelligently to changes in environmental conditions. Such information-processing systems also have the long-term potential for upload into human cells to passively scan the transcriptome and intervene only if cellular dysfunction is detected by producing a drug or silencing a pathway.The fact that these impressive feats of molecular design, assembly, and computation have made use of biomolecules suggests that cells themselves can be genetically engineered to take advantage of nucleic acid nanotechnology and significantly expand the existing capabilities of synthetic biology. For instance, nucleic acid nanostructures assembled in vivo. Thus, systems implemented in live cells have mostly relied on RNA molecular networks, which have distinct properties from their in vitro DNA counterparts. Second, nucleic acid nanotechnology systems often employ strands with well-defined 5′ and 3′ ends and partially double-stranded complexes. In vivo systems implemented with transcribed RNA, however, often require specific sequence elements at their ends, such as terminator hairpins and 5′ untranslated regions. Partially double-stranded complexes between two RNAs are also not guaranteed to assemble in the cell. Third, most self-assembled nucleic acid nanostructures are formed using annealing procedures [in vivo systems are limited to isothermal nucleic acid reactions. They may also need to integrate cotranscriptional folding pathways, which have been harnessed for in vitro assembly of RNA origami nanostructures [in vivo nucleic acid circuitry must be coupled to an observable cellular output that is genetically encoded, such as a reporter protein, enzyme, or aptamer, to assess circuit function and alter cell behavior. Most in vitro circuits rely on DNAs with chemically synthesized fluorophore/quencher pairs [Realizing these synthetic bionanotechnology systems, however, requires addressing multiple technical challenges. First, the bulk of work in nucleic acid nanotechnology has focused on DNA molecules with exposed single-stranded domains to initiate reactions ,24. DNA ocedures –15, wherructures . Fourth,er pairs  and in oer pairs  for outpin vivo. They found that the expression of optimized RNA scaffolds was associated with an increase in the rate of hydrogen production in a two-enzyme pathway by 48-fold in engineered E. coli cells. This work has since been extended for scaffolding a four-enzyme pathway [E. coli [One of the earliest major works using genetically engineered cells for RNA nanostructure self-assembly was reported by the Silver laboratory and employed RNA as a programmable scaffold for proteins . In this pathway  and the [E. coli .Beyond structural applications, our laboratory and others have demonstrated the use of synthetic bionanotechnology for sensing and logic in living cells. Much of this work has been enabled by the sequence design package NUPACK developed for nucleic acid nanotechnology systems ,31. NUPAde-novo-designed RNA-based prokaryotic translational regulator called the toehold switch [E. coli with stronger thermodynamics and improved kinetics, helping them achieve changes in gene expression above 600-fold, a level similar to protein-based regulators and over an order of magnitude improvement compared with previous riboregulators. The design of the toehold switch allowed the sensors to detect virtually any trigger sequence, which we exploited to detect endogenous transcripts and construct libraries of dozens of sensors with low cross-talk. Using toehold-mediated designs, the Lucks group has also demonstrated that nucleic acid nanotechnology principles can be applied to transcriptional regulation. Computer-designed Small Transcription Activating RNAs (STARs) employ toehold-mediated interactions to prevent formation of a transcriptional terminator, enabling programmable control of downstream transcription (Using NUPACK, we developed a fully d switch  inspiredcription . These dE. coli [E. coli and mammalian cells.The ability of CRISPR/Cas enzymes to regulate gene expression with a wide dynamic range in hosts ranging from bacteria to humans has made them an important focus area for synthetic bionanotechnology. Recently NUPACK has been used for generating several conditional guide RNA implementations for the enzymes Cas9 and Cas12a that respond to binding of a cognate trigger RNA. Siu and Chen demonstrated toehold-mediated activatable guide RNAs for Cas9 that operated in E. coli , while OE. coli  (Figure E. coli . These sE. coli and are considerably more genetically compact than equivalent protein-based circuits. These ribocomputing circuits employ toehold switches, strand-displacement, and RNA self-assembly to encode AND, OR, and NOT logic functions that operate successfully in the cytoplasm (E. coli [Having an established set of orthogonal, genetically encoded systems to detect transcripts in live cells has opened up new possibilities for cellular computing systems. Recently, we have reported RNA-based logic circuits  that canytoplasm . NOT logytoplasm . A similytoplasm . In addi(E. coli .Outside of the cell, toehold switches have also been incorporated into portable diagnostic devices for detection of infectious disease. These tests make use of cell-free transcription–translation systems embedded into paper substrates to detect amplified transcripts using genetically encoded pathogen-specific toehold switches . The resin vitro compared with those realized in living cells. For DNA nanostructures, tile approaches have been used to assemble nanostructures consisting of 66 unique tiles at physiological 37°C temperatures in vitro [E. coli [in vitro [E. coli consisted of 12 potential input RNAs and a single gate RNA, which combined five toehold switch modules and GFP for processing and output [Despite recent progress in synthetic bionanotechnology, there remains a substantial gap between the complexity of nucleic acid systems implemented in vitro . In cont[E. coli . For molin vitro . This syd output  (Figure in vitro and in vivo systems provides a clear roadmap marking the capabilities required to extend the limits of synthetic bionanotechnology. Some critical future advances and research directions include:Fortunately, the gap between Efficient RNA circuit synthesis: More complex circuits will require larger numbers of unique RNA species to be transcribed in vivo. Given the short length of DNA/RNA species employed in the most complex in vitro circuits, the use of promoter/terminator pairs to encode each species is an inefficient means of genetic encoding. Accordingly, future circuits will benefit from the use of efficient self-cleaving ribozymes [in vivo RNA systems could employ alternative strategies where multiple processing elements are incorporated into a single transcript, analogous to the gate RNAs in ribocomputing devices [ibozymes  or progribozymes  to divid devices ,39. Alon devices ,46 can b devices .Enhanced signal processing: DNA components that implement signal amplification, rectification, thresholding, and annihilation are important elements in neural network circuits [in vivo will need to be developed to implement circuits that operate with comparable complexity in living cells. RNA signal amplification systems in which an input RNA is used catalytically are also valuable for detection of low-copy transcripts (E. coli [circuits ,18. Paranscripts  and have(E. coli . Such caRNase mitigation strategies: Genetically encoded in vivo RNA circuits have generally been limited to a single strand-displacement step [in vitro networks can require five or more sequential strand-displacement steps [ent step , whereasnt steps , which rnt steps , self- cnt steps , circulant steps , or compnt steps  to reducEnhanced specificity and bandwidth: In principle, the use of RNA for in vivo signal processing could enable networks with hundreds or even thousands of unique RNAs to function simultaneously in the cell. Implementing systems with maximum bandwidth will require RNA circuitry with specificity down to the single-nucleotide level. In vitro toehold exchange probes and related systems have demonstrated impressive specificity [cificity –54. IntrExpanded RNA/peptide binding libraries and enclosed nanostructures: Genetically encoded RNA nanostructures that assemble in vivo can act as programmable scaffolds directing the assembly of proteins and enzymes within the cell to establish new enzymatic pathways (pathways . As the in vivo to construct nanostructures that lead to enhanced enzymatic activity, sensors that detect diverse transcripts, and logic devices that process up to a dozen different input molecules. In the coming years, it is likely that future major developments will arise from RNA circuitry powered by sequential strand-displacement reactions, which will enable critical signal amplification and rectification capabilities, and related systems that will interface directly with endogenous transcripts to monitor and manipulate cell state. Such systems can be deployed by bacteria in the environment for monitoring, reporting, and remediation efforts, or even those housed in the gut where they can respond to gastrointestinal ailments or changes in nutrition. Increasingly sophisticated in vivo nucleic acid assembly methods will be harnessed to position larger numbers of enzymes and to fabricate synthetic organelles that replicate the compartmentalization afforded by natural systems. In the absence of effective compartmentalization schemes, however, efforts should be directed toward pathways in which reactants cannot be lost to diffusion. Possible targets include signaling proteins [In conclusion, nucleic acid nanotechnology is providing synthetic biologists with a powerful set of molecular tools that can be harnessed to provide living cells with new functions. Genetically encoded nucleic acids have thus far been used proteins  along wiproteins , which cNucleic acid nanotechnology is providing synthetic biologists with a powerful set of molecular tools that can be harnessed to provide living cells with new functions.in vivo to construct nanostructures to scaffold enzymes, sensors that detect diverse transcripts, and logic devices that process up to a dozen different input molecules.Genetically encoded nucleic acids have been used in vivo nucleic acid assembly methods, and further development of circuitry for mammalian cells.It is likely future major developments will arise from RNA networks powered by sequential strand-displacement reactions, increasingly sophisticated These future efforts will require multiple new capabilities including improved methods for expressing nucleic acid networks and novel techniques for enhancing network stability, sensitivity, specificity, and function.


Long noncoding enhancer RNAs (lnc-eRNAs) are a subset of stable eRNAs identified from annotated lncRNAs. They might act as enhancer activity-related therapeutic targets in cancer. However, the underlying mechanism of epigenetic activation and their function in cancer initiation and progression remain largely unknown.MLL-rearranged leukemia (MLL leukemia), an aggressive leukemia caused by a chromosomal translocation, through a mechanism by which the HOXA cluster initiates enhancer activity, and the epigenetic reader BRD4 cooperates with the coregulator MLL fusion oncoprotein to induce transcriptional activation. To demonstrate the functional roles of lnc-eRNAs, two newly identified lnc-eRNAs transcribed from the SEELA eRNA cluster (SEELA), SEELA1 and SEELA2, are chosen for further studies. The results show that SEELA mediated cis-activated transcription of the nearby oncogene Serine incorporate 2 (SERINC2) by directly binding to the K31 amino acid (aa) of histone H4. Chromatin-bound SEELA strengthens the interaction between chromatin and histone modifiers to promote histone recognition and oncogene transcription. Further studies show that the SEELA-SERINC2 axis regulated aspects of cancer metabolism, such as sphingolipid synthesis, to affect leukemia progression.We identify a set of lncRNAs as lnc-eRNAs according to the epigenetic signatures of enhancers. We show that these lnc-eRNAs are broadly activated in This study shows that lnc-eRNAs are epigenetically activated by cancer-initiating oncoproteins and uncovers a cis-activating mechanism of oncogene transcription control based on lnc-eRNA-mediated epigenetic regulation of enhancer activity, providing insights into the critical roles of lnc-eRNAs in cancer initiation and progression.Supplementary information accompanies this paper at 10.1186/s13059-020-02186-x. Enhancers are essential DNA elements that positively modulate the transcription of target genes , 2 and aCis regulation is a general regulatory mechanism for eRNAs in cancer . In brief, 5 mL Luria-Bertani (LB) culture supplemented with 100 μg/mL ampicillin was inoculated with a single colony at 37 °C. After overnight growth, the culture was diluted 100-fold into 300 mL LB supplemented with 100 μg/mL ampicillin. Protein expression was induced in the presence of 0.4 mM IPTG at 16 °C overnight. Then the cell pellets were collected by centrifugation at 5000 rpm, 4 °C for 10 min and purified recombinant proteins using a His or GST tag Protein Purification Kit (BeaverBeads™) according to the manufacturer’s instruction.Recombinant proteins were expressed in ChIP analyses were performed on chromatin extracts from MV4-11 and RS4;11 cells using a Magna ChIP™ G - Chromatin Immunoprecipitation Kit 17-611)  with primary antibodies  according to the manufacturer’s standard protocol. The antibodies were listed in Additional file -611 . To prepare antibody-coated beads, 50 μL Protein A/G magnetic beads were incubated with 3 μg antibody or control IgG in 500 μL wash buffer at 4 °C for 1 h. Then the beads were washed three times and mixed with the cell lysates in new tubes. The tubes were rotated at 4 °C overnight. Finally, RNA extraction from the beads was further collected by using TRizol according to the manufacturer’s instructions. Reverse transcription and qPCR were performed as previously described. The antibodies were listed in Additional file In the RIP experiment, anti-FLAG, anti-HA, anti-IgG, anti-BRD4, or anti-H3 antibodies were used along with an EZ-Magna RIP™ RNA-Binding Protein Immunoprecipitation Kit 17-701)  according to the manufacturer’s instructions. In HA-tagged histone H4 or H3 fragments RIP assays, 1 × 10-701 (MerSEELA locus were cloned into MLM3636 plasmid (Addgene #43860) and co-transfected with SP-dCas9-VPR plasmid (Addgene#63798) into 293 T cells. Cells were collected 48 h after transfection to test the activating effect. The sgRNAs sequences were shown in Additional file The sgRNAs targeting The SEELA sequence was cloned into the pcDNA3 plasmid with the tRSA tag at its 5′ end. RNA products were transcribed in vitro using the TranscriptAid T7 High Yield Transcription Kit  and were then purified using the GeneJET RNA Purification Kit . The RNA pull-down assay was performed using 50 pmol RNA for each sample with the manufacturer’s instructions of Pierce Magnetic RNA-Protein Pull-down Kit .The 50 pmol tRSA-RNA products were first mixed with 1–3 μg recombinant proteins, and then the mixture was rotated at 4 °C for 1 h in RNA binding buffer according to Pierce Magnetic RNA-Protein Pull-down Kit. Beads were washed three times with RNA wash buffer and then boiled in SDS loading buffer. Finally, the enriched proteins were resolved via SDS-PAGE and analyzed western blotting.6 cells/sample; three samples each) were obtained from MV4–11 cells. The next-generation sequencing in this study was performed by poly-A RNA-seq on an illumina noveSeq instrument. The sequencing depth of the RNA-Seq is up to 6 Gites in 150-base paired-end mode. For the data analysis, pair-end reads were adapter and quality trimmed using Trim Galore (v0.6.4), and high-quality reads were aligned to reference transcriptome (Gencode v34) and quantified using Salmon (v1.3.0) with fragment GC bias and positional bias corrections [The si-NC, si-SEELA2, and si-SERINC2 samples  were obtained from MV4-11 cells. The sphingolipid metabolism  were performed and analyzed by LipidALL Technologies Co. Ltd., China.The si-NC, si-SEELA and si-SERINC2 sample .The ChIP-seq and RNA-seq data were downloaded from NCBI GEO database  with figure legends.Additional file 3. Dysregulated genes in si-SEELA and si-SERINC2 group.Additional file 4. The binding sites of HOXA9 in lnc-eRNAs loci.Additional file 5. Clinical characteristics of all samples in the study.Additional file 6. Oligonucleotides for PCR, RNA interference and sgRNAs.Additional file 7. Antibodies used in the research.Additional file 8. Complete western blot images of all figures in the manuscript.Additional file 9. Review history.


Current models for the origin of life include an earlier period when prebiotic chemistry dictated the nonenzymatic copying of RNA polymers, followed by a period of ribozyme-catalyzed reactions using nucleoside-5′-triphosphate (NTP) substrates. Our study addresses the transition from nonenzymatic to ribozyme-catalyzed RNA template copying by determining whether ribozymes could have used substrates activated with 2-aminoimidazole, a prebiotically plausible leaving group, instead of NTPs. We identify ligase ribozymes that use RNA substrates activated with 2-aminoimidazole, thereby proving that ribozyme catalysis is compatible with the reactants of nonenzymatic template-directed ligation. This work suggests that the transition from nonenzymatic to ribozyme-catalyzed RNA replication could have involved ribozymes that utilize inherently reactive RNA substrates. The hypothesized central role of RNA in the origin of life suggests that RNA propagation predated the advent of complex protein enzymes. A critical step of RNA replication is the template-directed synthesis of a complementary strand. Two experimental approaches have been extensively explored in the pursuit of demonstrating protein-free RNA synthesis: template-directed nonenzymatic RNA polymerization using intrinsically reactive monomers and ribozyme-catalyzed polymerization using more stable substrates such as biological 5′-triphosphates. Despite significant progress in both approaches in recent years, the assembly and copying of functional RNA sequences under prebiotic conditions remains a challenge. Here, we explore an alternative approach to RNA-templated RNA copying that combines ribozyme catalysis with RNA substrates activated with a prebiotically plausible leaving group, 2-aminoimidazole (2AI). We applied in vitro selection to identify ligase ribozymes that catalyze phosphodiester bond formation between a template-bound primer and a phosphor-imidazolide–activated oligomer. Sequencing revealed the progressive enrichment of 10 abundant sequences from a random sequence pool. Ligase activity was detected in all 10 RNA sequences; all required activation of the ligator with 2AI and generated a 3′-5′ phosphodiester bond. We propose that ribozyme catalysis of phosphodiester bond formation using intrinsically reactive RNA substrates, such as imidazolides, could have been an evolutionary step connecting purely nonenzymatic to ribozyme-catalyzed RNA template copying during the origin of life. The RNA world hypothesis proposes a central role for RNA as both a catalytic and an informational polymer during the emergence of life. Evidence for the RNA world includes a variety of functional RNAs, such as ribozymes and riboswitches, as well as the prebiotically plausible syntheses of nucleotides , 2. An i-imidazolides  nucleotide, and the rest being standard RNA nucleotides. Only 8 of 17 nt of the ligator are bound by the template, creating a 9-bp overhang at the 3′ end of the ligator. This overhang was incorporated into the ligator sequence to promote strand displacement of the ligator from unreacted complexes during reaction quenching  was prepared by transcription from 2.4 nmol of solid-phase synthesized DNA; the RNA was then digested with Ava-II to generate homogenous 3′ ends with free hydroxyl groups . For rounds 2 and 3, the reaction time was halved to 60 min and 30 min, respectively, and further reduced to 10 min for rounds 6–8. Beginning in the fourth round, the selection protocol incorporated a reverse transcription primer that was complementary to only the ligator sequence, whereas previously, this primer was complementary to the 3′ end of the library itself . After the eighth round of selection, the enhancement of the ligation rate had increased by a factor of ∼200. We also tested whether the putative ligase activity of the RNA pool depended upon the presence of the template strand. While ligation product was observed by 3 min when the template was included, omission of the template resulted in no detectable ligation product by 30 min . This result suggests that the most abundant functional sequences were catalyzing template-directed phosphodiester bond formation as intended.After each round of selection, the RNA pool was analyzed for ligase activity in a reaction with the 2AI-activated ligator RNA strand. Relative to the baseline nonenzymatic rate of imidazolide ligation, significant ligase activity was observed after round 6 A. EncourSI Appendix, Table S2). This analysis revealed 64 clusters with >0.1% abundance in the round 8 pool, suggesting that our selection may have identified a variety of functional sequences that catalyze the ligation of a 5′-phosphoro-2-aminoimidazolide substrate. Multiple sequence alignment . We chose 10 independently derived sequences, each the most abundant of their respective cluster, for further characterization  of the total reads of round 8 . A pointlignment , 35 of trization .SI Appendix, Fig. S3). In contrast, the ligation product was observed by 5 min for all 10 sequences RS1–RS10 . We observed a fast rate of catalyzed ligation for the first 30 min of the time course, but from 30 to 90 min, the reaction rate decreased over time , suggesting that the plateau in ligation yield is likely due to partial misfolding of the ribozymes. Therefore, we nonlinearly fit the data for the ribozyme-catalyzed reactions to a first-order rate equation that accounts for unreactive complexes in order to obtain rate constants. We determined that the rate constants for ligation catalyzed by RS1–3 range from 2.4 to 6.2 h−1, while the nonenzymatic rate under these conditions is 0.014 h−1, indicating rate enhancement by a factor of 170–440.To determine whether the enriched sequences were functional, we began individually testing these putative ligases in kinetic assays with the 2AI-activated ligator. Ligation was observed over a 90-min period and compared to a sample of the original unselected RNA pool . For thever time C. CompetSince a previous ribozyme selection using an imidazolide substrate unintentionally enriched for 5′,5′ ligases instead of 3′,5′ ligases , it was SI Appendix, Fig. S6). Disrupting Watson–Crick base pairing at the ligation junction also reduced the ligase activity of RS1, especially with pyrimidine substitutions at the 3′ end of the primer .To determine the template and activation requirements of the full-length sequences RS1–RS10, we tested each sequence in ligation reactions either with or without the template strand, and with either a 2AI-activated ligator or an unactivated ligator with a 5′ phosphate. We also prepared truncated RS1–RS10 RNA constructs lacking the 3′ terminal primer region and U6 linker and tested these in a ligation assay containing both the template strand and 2AI-activated ligator. Consistent with ligation occurring at the 3′ end of a primer, these three experimental conditions abolished ligase activity for all 10 ribozymes . FurtherAlthough these results indicate that ligation occurs in a template-directed manner at or near the primer region of the ribozyme, they cannot on their own distinguish the nature of linkage being formed. Therefore, we analyzed the ribozyme-catalyzed ligation products by RNase T1 digestion , which cSI Appendix, Fig. S8).For all 10 ribozymes, our RNase T1 digestion assays indicate that the ligation reaction must generate a 3′-5′ phosphodiester linkage, which is most easily explained by ligation to the 3′ hydroxyl of the ribozyme construct B–D. Gel In the ligation reactions described above, a catalytic RNA construct of 95 nt reacts with a ligator strand of 16 nt to generate a 111-nt product. We considered two regions to be potentially dispensable for this reaction: the first 25 nt at the 5′ end of the functional RNA sequence and the terminal 8 nt at the 3′ end of the ligator strand. The 25 nt at the 5′ end of the functional RNA sequences were included for PCR amplification during in vitro selection but may also play an important role in the folding and catalytic activity of these newly identified ribozymes. Secondary structure prediction suggested that some of the sequences  do not fold into the predesigned stem-loop structure  but instSI Appendix, Fig. S2).To test the requirement for these two sequence regions, we prepared truncated RNA constructs for RS1–RS10 that lacked the first 25 nt of the 5′ end and a 2AI-activated 8-nt ligator strand lacking the overhang sequence . Both fuSI Appendix, Fig. S2). Ligation reactions containing the short ligator and a second 8-nt strand consisting of the overhang sequence only modestly increased the observed ligation yield for RS2, RS5, RS6, RS8, and RS10 and had no effect for the other ribozymes .The 3′ truncation of the 16-nt ligator resulted in a significant decrease in ligation efficiency by all 10 ribozymes , with a ibozymes . AdditioSI Appendix, Fig. S10). This distal loop is therefore capable of forming essential long-range interactions with the substrate to assemble the catalytically relevant active site.These experiments argue that the overhang plays a role in assembling the ribozyme–substrate complex into its catalytically competent configuration rather than only contributing to proper ribozyme folding. Potential interactions between the catalytic domain of the ribozyme and the 3′ overhang of the ligator may stabilize the catalytically competent fold of the ribozyme or help position the ligase at the imidazolide ligation junction. We determined the secondary structure of the 5′ truncated form of RS1 using a SHAPE assay in conjunction with the folding algorithm of the RNAStructure program –40. The N2 substitution reaction, whereby nucleophilic attack of the 3′ hydroxyl on a 5′-phosphor-imidazolide generates a new phosphodiester bond. Nonenzymatic RNA ligation is believed to occur through the classic SN2 substitution reaction because the concentration of an imidazolium-bridged ligator intermediate would be too low, based upon the previously measured second-order rate constants of imidazolium-bridged intermediate formation . In addition, ligation reactions containing substoichiometric levels of ligator relative to ribozyme are not consistent with the second-order kinetics predicted for formation of a covalent intermediate . These results suggest that ribozyme-catalyzed and nonenzymatic RNA ligation occur through the classic SN2 substitution reaction pathway. Since ligation of 5′-triphosphate–activated substrates is believed to also occur through a similar in-line attack mechanism . Together, these results indicate that the ribozyme RS1 specifically catalyzes ligation of 2AI-activated substrates and that the mechanism does not involve a covalent, imidazolium-bridged intermediate.To determine whether the mechanism of ribozyme-catalyzed ligation involves the formation of an imidazolium-bridged intermediate, we tested whether free 2AI inhibits the ligase activity. Free 2AI rapidly reacts with the imidazolium-bridged intermediate, thereby inhibiting phosphodiester bond formation through this pathway . We obseechanism , we testSI Appendix, Fig. S12). RS3, RS7, and RS10 retained substantial activity when DNA was used in only the template strand, while RS2 and RS10 retained activity when only the ligator was composed of DNA. These results suggest that the reduction of ligase activity in these experiments cannot be entirely accounted for by helical structure, as DNA-RNA hybrids are typically A-form. Substituting an RNA substrate with DNA of the same sequence might also disrupt ribozyme–substrate interactions involving 2′ hydroxyl groups, as observed in certain self-cleaving ribozymes .Nonenzymatic ligation can utilize a variety of divalent cations to catalyze phosphodiester bond formation , whereas2+ ions –50. Thercept RS8 A. Supplentration A. RS2 anSI Appendix, Fig. S14). From pH 6 to pH 9, ligation rate increases with an increase in pH and then decreases from pH 9 to pH 11.5. A similar trend has been previously observed in uncatalyzed ligation of triphosphate substrates (Ka of the 2AI leaving group, as might be expected if the reaction mechanism involved an imidazolium-bridged intermediate. In both nonenzymatic and ribozyme-catalyzed ligation of triphosphate substrates, the pH-rate profiles reveal a log-linear region with a slope equal to 1, suggesting that the rate-limiting step involves abstraction of a single proton (Ka is equal to 8. At lower pH, 2AI is protonated, making it a better leaving group, while at higher pH, 2AI is uncharged, making it a worse leaving group for the ligation reaction.One of the potential roles of a catalytic divalent ion is to facilitate the deprotonation of the 3′ hydroxyl group on the primer, thereby accelerating nucleophilic attack on the 2AI-activated substrate. We measured ligation rates of RS1 at pH values between 6 and 11.5 to generate a pH-rate profile (bstrates . Notablye proton . We obseWe have used in vitro selection, combined with the inherently reactive substrates of nonenzymatic RNA ligation, to demonstrate that template-directed ligation of a 2AI-activated oligonucleotide to form a phosphodiester linkage is a viable target reaction for ribozyme catalysis. This result shows that nonenzymatic and ribozyme-catalyzed RNA copying can be linked in models of the emergence of life. A previous report selecting for ribozymes that use an imidazolide substrate unintentionally enriched for 5′-5′ ligase activity, but our modified selection protocol successfully identified 3′-5′ ligases. Deep sequencing identified 64 independently derived sequences enriched through selection, and template-directed imidazolide ligase activity was observed for all 10 of the enriched sequences in the RNA pool we examined after eight rounds of selection, suggesting that ribozyme catalysts of this type of reaction may be rather common. Furthermore, 4 of 10 ribozymes retained ligase activity after removal of a 5′ constant region used for PCR amplification, reducing the length of the RNA sequence to 70 nt. This length is conceivably attainable through a combination of nonenzymatic polymerization and ligation reactions, as has been recently suggested for other RNA sequences , 53. We 2+ or Mn2+ cation to enhance the nucleophilic attack of the primer on the activated ligator, similar to the mechanism of the class I ligase ribozyme (Exactly how our newly isolated imidazolide ligases catalyze phosphodiester bond formation is currently unclear. Structural studies of nonenzymatic ligation suggest that the imidazole leaving group is disordered, thereby impeding in-line attack by the hydroxyl groups of the primer . Thereforibozyme . WhetherAlthough our selection strategy successfully identified imidazolide ligases, there remains a large gap between this catalytic activity and the ultimate goal of protein-free RNA copying. To this end, short ligase ribozymes could play an important role within a larger context of predominantly nonenzymatic polymerization reactions. For example, short precursor oligonucleotides formed through nonenzymatic template-directed polymerization of imidazolide monomers could then become substrates for a ligase ribozyme. Since nonenzymatic ligation of 2AI-activated oligonucleotides is much slower than polymerization of 2AI-activated monomers , 25, ligSI Appendix, Table S3. RNA pools and individual ligase ribozymes were prepared by Ava-II digestion of transcribed RNA or by transcription of DNA templates containing 5′ terminal 2′-O-methyl modifications to generate homogenous 3′ ends with free hydroxyls (SI Appendix.The sequences of DNA and RNA oligonucleotides used in this study are listed in the ydroxyls , 54. RNAydroxyls , 42. DetAll data discussed in the paper will be made available to readers.Supplementary File