RNA molecules can adopt stable secondary and tertiary structures, which are essential in mediating physical interactions with other partners such as RNA binding proteins (RBPs) and in carrying out their cellular functions. In vivo and in vitro experiments such as RNAcompete and eCLIP have revealed in vitro binding preferences of RBPs to RNA oligomers and in vivo binding sites in cells. Analysis of these binding data showed that the structure properties of the RNAs in these binding sites are important determinants of the binding events; however, it has been a challenge to incorporate the structure information into an interpretable model. Here we describe a new approach, RNANetMotif, which takes predicted secondary structure of thousands of RNA sequences bound by an RBP as input and uses a graph theory approach to recognize enriched subgraphs. These enriched subgraphs are in essence shared sequence-structure elements that are important in RBP-RNA binding. To validate our approach, we performed RNA structure modeling via coarse-grained molecular dynamics folding simulations for selected 4 RBPs, and RNA-protein docking for LIN28B. The simulation results, e.g., solvent accessibility and energetics, further support the biological relevance of the discovered network subgraphs. RNA binding proteins (RBPs) regulate every aspect of RNA biology, including splicing, translation, transportation, and degradation. High-throughput technologies such as eCLIP have identified thousands of binding sites for a given RBP throughout the genome. It has been shown by earlier studies that, in addition to nucleotide sequences, the structure and conformation of RNAs also play important role in RBP-RNA interactions. Analogous to protein-protein interactions or protein-DNA interactions, it is likely that there exist intrinsic sequence-structure motifs common to these RNAs that underlie their binding specificity to specific RBPs. It is known that RNAs form energetically favorable secondary structures, which can be represented as graphs, with nucleotides being nodes and backbone covalent bonds and base-pairing hydrogen bonds representing edges. We hypothesize that these graphs can be mined by graph theory approaches to identify sequence-structure motifs as enriched sub-graphs. In this article, we described the details of this approach, termed RNANetMotif and associated new concepts, namely EKS (Extended K-mer Subgraph) and GraphK graph algorithm. To test the utility of our approach, we conducted 3D structure modeling of selected RNA sequences through molecular dynamics (MD) folding simulation and evaluated the significance of the discovered RNA motifs by comparing their spatial exposure with other regions on the RNA. We believe that this approach has the novelty of treating the RNA sequence as a graph and RBP binding sites as enriched subgraph, which has broader applications beyond RBP-RNA interactions. The human genome encodes approximately 1500 or more RNA binding proteins (RBPs), which regulate every aspect of the RNA biogenesis and RNA biology, including RNA splicing, modification, degradation, protein translation, and RNA subcellular localization –8. In viThese structure-based methods differ in how they encode RNA structure information in their models thus they can be grouped into two broad categories according to the abstract level of structural encoding. Methods like MEMERIS , mCarts In parallel to the study of protein-RNA interactions, our understanding on protein-DNA interactions has been greatly aided by the availability of a plethora of structure information on protein-DNA complexes and on DNA structures. It has been recognized that the overall shapes of the DNA molecules play an important role in the initial recognition by proteins and the subsequent binding processes ,37. ThesInteractions between RNA-binding proteins (RBPs) and RNA molecules employ diverse and dynamic modes ,42; findTowards these goals, we herein introduce a novel algorithm, RNANetMotif, which takes as input thousands of RNA sequences presumably bound by an RBP and uses a graph theory approach to search for “base pair derived subnetworks” enriched in the predicted RNA secondary structures. RNANetMotif consists of the following steps. (i) For each RNA locus bound by an RBP as determined via eCLIP experiments, we predict its base-pairing pattern using software RNAplfold and represent its secondary structure as a network . (ii) Wehttp://rnanetmotif.ccbr.utoronto.ca) that stores the results from our analysis of ENCODE RBP datasets (16 RBPs) and allows users to upload a collection of RNA sequences of their own interests for motif analysis. The RNANetMotif is a new way of investigating RNA sequences and can be further extended into analysis of other categories of RNA sequences.We note, in addition to RBP binding sites, RNANetMotif can also be applied in other scenarios to extract enriched RNA sequence or structure motifs. RNANetMotif compares favorably with other methods in terms of performance and run-time. We believe RNANetMotif offers a fresh approach in understanding interactions between RBP and RNA targets, and in extracting informative RNA motifs. To make this tool more accessible to the community, we constructed a web server  and to filter to obtain the final EKS pool. In Step 3, we compute the HVDM  distance matrix [As shown in e matrix  and use  and Fig 2, the EKSes were constructed by traversing and extending k nucleotides from a specific base-pair (see  for details). To select the most appropriate k to partition the RNA molecule, we investigated the occurrence of intervals between predicted base-pairs in the predicted RNA secondary structure.As shown in Fig 3 (top panel), approximately 79.51% of all the gap lengths is 1, which represents stacked adjacent base pairs. The bottom of Fig 3 shows, excluding gap length 1, gap length < = 9 can cover 87.91% of all possible gaps in 22 RBPs. Note that EKSes were constructed by extending from the initial base pair in two directions, thus a gap length of 9 is equivalent to 5-mer extension. This indicates that, with k ranging from 3 to 5, 87.91% of the nucleotides in RBP binding sites are likely covered by at least one EKS. We also note that most RNA binding domains tend to bind to short RNA sequences, therefore we investigated k up to 5 in our pipeline. The RNANetMotif pipeline is quite flexible so users can customize the cut-off k or other global parameters to suit their requirements. Moreover, the bottom bar plot in Fig 3 shows that the number of occurrences decreases as the gap length increases.We first divide all the nucleotides in the entire RNA as paired or unpaired and define the distance between two adjacent paired nucleotides as gap length. As shown in Fig 4 shows the frequency of top sequence instances and top secondary structure instances in scatter plots for five selected RBPs on different k. From Fig 4, we find that after applying GraphK partition, the distribution of top sequence instances of EKS has lower frequencies than top structure instances. Given the low number of top sequence instances occurrence, we fitted a new distance and enrichment definition in our graph-preserving framework .After applying GraphK to partition the RBP binding sites into EKSes, we pooled these selected EKSes and investigated the properties of these elements. We further discretized the predicted secondary structure probability of each nucleotide into one of the following 5 states: P (paired), H (hairpin loop), E , I , and M (multiple loop). We next counted the occurrence of sequence and structure of EKSes respectively; χ2 test [Section 2.2, we next discretized secondary structure representation of each nucleotide and visualized the sequence-structure preference of these significant modules in Fig 5 for 16 selected RBPs. We did not observe statistically significant modules for RBPs that preferentially bind to double-stranded RNAs such as DDX3X, DDX24, GRWD1 (containing WD repeat domain). We think this may be because RNA structure plays different roles in protein-RNA recognition for double-stranded and single-stranded RBPs, which is consistent with previous observations [We next calculated HVDM distances among pairs of EKSes and constructed a similarity network with individual EKSes as nodes and HVDM distances as edge weights. We then used ClusterOne software to search for densely connected network modules in the similarity network and evaluate the statistical significance of these modules by Pearson χ2 test . As descrvations , 48. We rvations . FurtherFig 5 shows the sequence logo and associated secondary structures of the enriched EKS modules for different k-mer sizes and EKS topologies. We notice that proteins with the same binding domains tend to have similar binding profiles. For example, IGF2BP1, PABPC4, LARP4 and RBM15 share a common RRM (RNA recognition Motif) domain, consisted of pyrimidine-rich internal loops and hairpin loops. Not surprisingly, we also note that the same proteins have similar profiles in different cell lines. For instance, profiles of UPF1 from HepG2 and K562 tend to share similar sequence preferences in both hairpin loops and internal loops when k is set at 5.In the world of proteins, linear polypeptides usually fold into certain three-dimensional structures to ultimately carry out cellular functions. Similarly, protein-RNA interactions often require RNA to fold into specific 3D shapes in order to precisely spatially present relevant chemical moieties on sugar-phosphate backbone or nucleoside bases . It has Methods (Section 4.7), we modeled the structure of bound RNA molecules for the four aforementioned RBPs. To achieve effective binding between protein and RNA, the relevant RNA moieties need to be exposed and accessible to amino acid residues on the RBP. We constructed coarse-grain structures of RNAs and calculated spatial accessibility of the predicted RNA structural motifs by counting the number of neighboring atoms within a 15 Å radius. Our hypothesis is that these structural motifs would have higher accessibility than the rest of the RNA molecules. For the four well-studied RBPs , we modelled 50 sequences for each RBP with 3 replica per sequence and performed one-sided Mann Whitney U Test (Wilcoxon Rank Sum Test) to compare the average atom counts of the identified motifs with all other k-mer regions. Fig 6 shows that for all of these RBPs, the identified RNA structure motifs have statistically significant fewer atoms in their vicinity than the rest of the RNA molecule, indicating higher spatial accessibility for these motifs. As an example, sequence and structure model of top representative motifs of each RBP are shown and the predicted EKS motifs are highlighted in . Similarly significant results modelled by SimRNA software are shown in S5 Fig.Following the protocol described in We next performed protein-RNA docking and simulation on LIN28 to further test the role of the predicted RNA structure motif in RBP-RNA recognition. We selected LIN28 for this purpose since this protein is well studied and there exist multiple high-resolution structure of it complexed with single stranded RNA ,53.). We chose the ClusPro web server to perform RNA-protein docking, since it uses an FFT based method that can do a quick, accurate, and unbiased global search to explore protein-RNA binding modes [Fig 7, this loop is inserted into the motifs predicted by RNANetMotif. Subsequent MD simulations confirmed that this binding mode is energetically stable and strong phosphate contacts are formed while additional base contacts also formed, mainly through three Lys residues on this loop  . In the case of 4mer simulation, this loop-RNA contact served as an anchor and additional contacts were formed after certain RNA conformational changes. In a recent study, NMR and mutation studies indicated that K88, K89  play a role in electrostatic interactions with nucleic acids [We chose two representative sequences from the 5mer motifs and one from 4mer motifs to perform all-atom structure refinement and docking . We randomly divided the positive and negative sets into a training set and a test set using a ratio of 8:2. The test set is formed by the positive test set and the negative test set with a ratio of 1:1. As shown in S7 Fig, the ROC curves indicated that RNANetMotif had robust and better performance than GraphProt, as RNANetMotif had higher AUROC values than GraphProt on 14 out of 16 RBPs.In addition, we used RNANetMotif as a supervised classifier and compared it with GraphProt on 16 RBPs. To evaluate the performance of the two classifiers for a specific RBP, we used the binding sites of the RBP as positive set and took binding sites of another RBP with similar nucleotide frequencies as the negative test set  while maintaining the structure motif. We synthesized 100 instances of these sequence motifs and inserted each instance into a randomly generated RNA sequence of 100 nt long. For each type of motif, we also created a set of random background sequence set of the same size as the negative control set. We next ran RNANetMotif and three other methods, GraphProt, RNAcontext and Zagros, on these sequence sets to try to recover these spiked-in motifs. As shown in Table 2, RNANetMotif consistently recovered the sequence and structure of every one of the five motifs, which compares favorably with other methods. The detailed recovering results of these four methods are summarized in Having demonstrated the effectiveness of RNANetMotif in finding important RNA structural elements in RBP bound RNA sequences, we next investigated whether this approach is also effective in finding meaningful RNA structural motifs in a more general setting. As shown in S8 Fig) and model training time for GraphProt. RNANetMotif consumed the least time among all the five tools, while GraphProt ran a little slower than RNANetMotif on three datasets and faster than RNANetMotif on two other datasets. All the tools had similar level of memory usage, with maximum memory usage of no more than 10 GB on all the tested data. We like to note that a more comprehensive and robust investigation and benchmark is warranted to test the utility of RNANetMotif with regards to this task.We compared the run time of RNANetMotif with other methods on five eCLIP datasets (see Introduction). The novelty of RNANetMotif is that it takes a graph-based approach to extract enriched subgraphs from RNA secondary structures. Ideally, the most accurate and unbiased way to determine the binding mechanism between an RBP and its target RNAs is to compare high-resolution 3D structures of a representative set of RBP-RNA complexes and derive a set of common 3D structure elements shared by these structures. However, this is technically challenging and logistically unrealistic. It has been well documented that linear representations such as Positional Weight Matrices (PWM) have their limitations in capturing the binding preferences [In this paper we describe a novel network-based approach in finding meaningful sequence and structure motifs from RBP bound RNA sequences. We recognize that there have been many other methods developed over the years that have addressed this problem from many different angles . We also constructed a website that allow users to test their own data: http://rnanetmotif.ccbr.utoronto.ca.We introduced a new concept to represent RNA elements, i.e., EKS (Extended K-mer Subgraph) and an algorithm GraphK. As demonstrated in this study, we think EKS is a promising approach in mining local RNA secondary structures for enriched motifs. We further showed that it can be extended to the study of more general problems in addition to RBP-RNA interactions, although more rigorous benchmarking is required. Compared to other methods, RNANetMotif has the following unique characteristics. (i) It recognizes RNA target sequences as networks and applies graph theory approaches to extract meaningful and enriched subgraphs. (ii) In contrast to other methods, RNANetMotif follows an unsupervised scheme thus avoids the noise and uncertainty introduced by the false negative data. (iii) We also conducted structure modeling and molecular dynamics studies and validated some of the predictions. RNANetMotif is a new way of investigating RNA sequences and can be further extended into the analysis of other categories of RNA sequences. The source code of RNANetMotif can be accessed at Github . We envision that RNANetMotif or other similar approaches such as RNAcompete or GraphProt are more amenable to the study of these ssRNA binding proteins than to dsRNA binding proteins, since single stranded RNAs have a more diverse range of secondary structure elements and these elements are presumably important in the binding process. On the other hand, although the binding sites of the dsRNA binding proteins are double stranded stem structures, it is also possible that other regions outside of the binding sies have enriched structure elements that put constraint on the RNA and possibly help present the stem structure to the incoming RBPs. A more detailed examination of these potential structure elements is warranted when more eCLIP data become available for these dsRNA binding proteins.https://www.encodeproject.org/) in June 2020, which consists of chromosomal peak regions that are bound by specific RBPs. The peaks were annotated as IDR BED files which had been processed following the eCLIP-seq Processing Pipeline. Reproducible and significant peaks that passed the Irreproducible Discovery Rate (IDR) were identified by a modified IDR method.We downloaded the “enhanced UV crosslinking followed by immunoprecipitation” (eCLIP) data (1) from ENCODE project website (release v101)  were collected, which comprised of 120 files for K562 cell line, 103 files for HepG2 cell line. We next retained only 56 IDR files that have more than 5,000 mapped binding sites. We further excluded those proteins that were annotated to be primarily involved in mRNA splicing and excluded those RBPs that had fewer than half of the binding sites mapped to the annotated exon regions. The final derived set contained 22 IDR files. The rational of above procedures is to limit our study only to those RBPs that primarily bind to the mature mRNA transcript region. To unambiguously extract exon regions, we used the most prominent transcript for each gene as was defined based on the basic gene annotation from GENCODE  through hierarchical filtering: first filtered by APPRIS annotation (highest priority) , then by We chose 100 nt as the uniform binding site length (50 nt extensions up- and down- stream of the center position) and used ’getfasta’ function from ’bedtools’ to map BED files to FASTA files. We then removed redundant and missing data in the binding sites by using module ’cd-hit-est’ from CD-HIT at 80% similarity cut-off [We next processed the 22 IDR files by only keeping the peaks that are entirely localized in one of the exons of the prominent transcript., i.e., removing those binding sites in the introns. The detailed information of the 22 RBPs with specific cell line is summarized in  cut-off . A list We used the RNAplfold program in the ViennaRNA package (version 2.4.13) for secondary structure prediction. The stem candidates were derived from the base pairing probability matrix calculated by McCaskill’s algorithm (43). After experimenting with the choices of window sizes (L and W-L), we set the parameters of RNAplfold as W = 100, L = 100 while allowing Watson-Crick (A:U and G:C) and wobble G:U base pairing . We noteHaving predicted base-pairing probabilities in the entire RNA, we next predicted and refined local secondary structure probabilities associated with each nucleotide by using the software RCK . The folWe represented each 100 nucleotides (nt) long RBP bound RNA sequence as a weighted graph G = , where V consists of nucleotides encoded as vertices with discrete labels  and continuous labels (see above). The edge set E contains RNA backbones and the predicted base-pairings. The weight of backbone edges is defined as 1, while base-pair edges (Watson-Crick or G: U) as the probability predicted from RNAplfold.i and j, we build a subgraph by extending along the backbone edges from i and from j, separately and respectively . During the extension process, we traverse along the backbone from i and from j respectively, until a k-mer is reached in both directions. We define the resultant subgraph containing the 2k number of nucleotides as an Extended k-mer Subgraph (EKS). The intuition of EKS is that these subgraphs represent smaller tractable network elements as component of a larger complex network.In this work, we introduce a new concept dealing with graph representation of local RNA secondary structures, referred to as Extended K-mer Subgraph or EKS. Given two base-pairing nucleotides, Fig 2, the linear sequence of the local folded RNA follows the following direction: O(|U|3) [As illustrated in  O(|U|3) , 72. TheFig 2, the opposite-direction extensions can generate EKSes that are either left-opened or right-opened subgraphs. Mixed-mode extensions have the most diverse shapes and topologies. As for the same-direction extensions, the network is extended in the same direction, both toward 3’ end or 5’ end. The nucleotides in these EKSes have lower chance of forming base-pairing unless pseudoknot structure is allowed.Depending on the directions of traversing from the root base pair , we can divide the set of EKSes into three categories: opposite-direction extensions, mixed-mode extensions, and same-direction extensions. As shown at the top of . In Step 1, we conduct k-mer extension as described above in each RBP-bound RNA sequence to obtain all EKSes associated with the RNA sequence. In addition, we add an annotation to each EKS, including its type, compactness score, and the starting positions of the two k-mers. In Step 2, we extract EKSes generated from opposite-direction extensions , and exclude those generated by same-direction or mixed-mode extensions. The rationale is to extract only those likely to form single stranded RNAs (see above). In Step 3, we filter EKSes by their compactness scores and only retain those with lower compactness scores, i.e., those with less base pairings. We next filter the EKSes by the position of the nucleotides and keep the EKSes whose sequence is entirely located within the central 40 nucleotides. By this approach, we partitioned every 100 nt RNA into local network elements, further filtered and pooled them into the final EKS pool.Having defined EKS, we next introduce an algorithm, GraphK, which applies extension, extraction, and filtering steps to partition a complex graph into overlapping representative EKSes. The detailed steps of the GraphK algorithm are as follows Fig 2). In addition, we used a graph theory method to calculate compactness scores for EKSes and retained those with lower compactness score, i.e. those EKSes with sparse base-pairs and are more prone to binding by RBPs. Thirdly, we explain the choice of 40 nucleotides as the length of EKS. This is because the mean length of the peaks from eCLIP datasets is around 40 ~ 60 nt and the structural distribution in the central regions of peaks shows better structural conservation.In the following, we elaborate the rationales behind the GraphK algorithm. First, it is difficult to represent the 2D or 3D structure of the entire RBP binding region. Therefore, we treated the predicted secondary structure of the entire RBP binding region as a network and extracted base-pair derived subgraphs from the network to represent components of the entire structure network. These local subgraphs are more tractable than a global graph in representing the entire RNA region. Our hypothesis is that the RBP-bound RNAs could share some of these common subgraphs. Secondly, we briefly explain the choice of size, shape, and compactness score of the EKS. Since most single RNA binding domains (RBDs) appear to bind RNA motifs 3 ~ 8 nt long , we chosk nucleotides and has 12k mixed-type attributes, comprised of 2k categorical attributes  and 10k continuous attributes . We chose to use the heterogeneous value difference metric (HVDM) as the distance function, which uses value difference metric (VDM) to handle categorical attributes, and absolute differences to handle continuous attributes [We next calculate similarities between any pairs of EKSes so that we can select representative and discriminative EKSes associated with each RBP binding region. Each instance of EKS consists of 2tributes ,73. Giveclasses = {Ci, i = 1,2,3,…,10}. As shown in S9 Fig, we first partition the central 40 nucleotides into 4 intervals of equal length of 10 nt as {Ik, k = 1,2,3,4} and assign the EKS to one of the following classes according to the intervals into which the first and the last of the nucleotide falls: Ci∈{Ii,j, i, j = 1,2,3,4. i≤j}. The VDM distance between First, we classify an EKS according to which region on the RNA sequence the first and last nucleotide of the EKS falls into and assign the EKS to one of the following classes: xα for attribute α and class Ch; Nα,x is the number of instances in all EKSes that have value xα for attribute α, i.e., P(Ch|xα) is the conditional probability that the class is Ch given that attribute α has the value xα. The sum of P(Ch|xα) over all classes is 1 for a fixed value of α and xα.β and yβ of a continuous attribute β as the absolute difference:We next define the difference between two values xTaking these together, we then define the HVDM distance between S  with EKSes as vertices and the above calculated HVDM distance between vertices as the weight of the edges. To denoise this graph, we then refined this graph by the following pruning steps. Firstly, we selected those vertices that are close to each other and have similar partners on the network. For v1, v2∈VS, if v1 and v2 are among top n nearest neighbors of each other, i.e., v1 is one of the top n nearest neighbors of v2 and v2 is also one of the top n nearest neighbors of v1, v1, v2 and the edge between them . Starting from an RNA sequence and base-pairing information, the default DMD settings were used to perform coarse-grained folding simulation for 100,000 DMD time units; for each sequence, 3 replica of MD simulation was performed. For selected LIN28B systems, the last snapshots of DMD simulations were extracted to recover the 3-beads coarse-grained representation back to full atoms to perform more accurate full-atom docking and simulations. Since this conversion may result in severe clashes or abnormal bond length, some minimization and equilibration, as well as a short 10-ns full atom simulations were performed to further refine the structures before docking. For spatial accessibility, a 15Å cut-off was used to count the neighboring atoms of the given regions to calculate the exposure level, while nucleotides within 2 positions on both 5’ and 3’ directions were excluded since they would always be counted due to bonded connections except for at both ends of the sequences. To evaluate the significance of the discovered network motifs, Mann-Whitney-Wilcoxon (MWW) rank-sum U Test was performed on such neighboring counts of identified motifs against the average of all the k-mer regions. To avoid possible bias introduced by the selection of MD protocol, we also employed SimRNA [The RNA 3D structures were predicted using a discrete molecular dynamics (DMD) method with predicted base-pair information provided as constraints . The DMDd SimRNA  to model+ and Cl2- ions were added to ensure a 0.15M ionic concentration and zero net charge. Due to the diversity and intrinsic flexibility of RNA molecules, the built systems contain a wide range of ~300,000 to 700,000 atoms. After 10,000 steps of minimization and equilibration, where harmonic restraints were applied to heavy atoms, production MD simulations were performed in the NPT ensemble. The Nosé-Hoover method was used with temperature T = 30°C [Charmm-gui webserver was used to set up the MD systems , the comT = 30°C . The ParT = 30°C . A 10-Å T = 30°C . The LINT = 30°C . The eneT = 30°C  . We used the following geometric criteria to identify a hydrogen bond (HB) between two polar non-hydrogen atoms : the donor-acceptor distance is <3.5 Å, and the deviation of the donor-hydrogen-acceptor angle from 180° is <60°. We used the VMD program to identify and calculate the occupancy of HBs, the root-mean-square deviation (RMSD) of the protein and motif and for visualization {Humphrey, 1996 #10}.The RNA structures after 10-ns production run were used as receptors and the LIN28B CSD structure (PDB ID 4A4I)  was usedIt is known that base-pairing probabilities predicted by RNAplfold or any other software can be influenced by global parameters such as the size of the window (W) and the maximum base pair span (L). We experimented with different combination of W and L: , ,  and , and tested on four representative RBPs, G3BP1, IGF2BP1, LIN28B and PUM2  parameters, the generated RNA network motifs were further used in a supervised classifier to separate true or false RBP binding sites. As shown in S12 Fig, the overall trend and the shape of the ROC curves are very similar to each other.S13 Fig), which showed general consistency among these three methods. We also compared in silico predicted RNA base-pairing probabilities with the experimentally determined RNA nucleotide accessibilities, i.e., the icSHAPE scores [We also compared the prediction results between RNAplfold with several other prediction tools. We compared the results of RNAplfold, RNAstructure, RNAfold and calculated the Jaccard similarity of the predicted base-pairs Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 MethodA. Selection of control sites for the evaluation of network modules. B. Summary of parameters in RNANetMotif algorithm. C. Parameters in RNA secondary structure prediction.(DOCX)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S9 FigIk,k = 1,2,3,4}.Three EKSes are marked as red, green, and brown dashed circles in the RNA secondary structure. After unfolding, these EKSes are mapped back to primary RNA as gapped sequences with two k-mers falling into four intervals {(PDF)Click here for additional data file.S10 Fig(PDF)Click here for additional data file.S11 FigThe value of Y axis stands for the trimmed mean of unpaired probability of each position(PDF)Click here for additional data file.S12 Fig(PDF)Click here for additional data file.S13 Fig(PDF)Click here for additional data file.


Hevea brasiliensis. TPD is associated with clonal susceptibility and overexploitation of rubber trees. Most studies are based on a binary point view of the absence or presence of TPD. This study sets out to characterize the dynamic of the TPD onset through the monthly monitoring of the dry cut length. This reveals the presence of dry spots on the tapped panel of any trees. The frequency of these dry spots increases dramatically in trees developing high level of TPD. Brown bast is an irreversible form of TPD. Brown bast is correlated to a high level of dry cut length. Application of an intensive harvesting system induces early TPD occurrence, which facilitates the study of TPD. Among latex diagnosis parameters, only sucrose content is significantly associated with TPD. Other parameters are more prone to environmental effects and are not reliable as physiological markers. These findings explain the contradictory conclusions of some papers. This study suggests to use intensive harvesting system and monitor the dry cut length for genetic analysis of TPD.Tapping Panel Dryness (TPD) is a physiological disorder affecting natural rubber production in  Dry cut; Marker; Phenotyping; Rubber; Sucrose; Yield. This cis-1,4-polysioprene is synthesized in the rubber particles of laticifers. These latex cells are articulated and anastomosed to create a cellular network enabling the production of a large volume of latex after cutting, also called tapping, the soft bark of rubber trees without damaging the vascular cambium. Complete latex regeneration required 48–72 h depending on clone latex metabolism. For some rubber clones, the application of an ethylene releaser, the ethephon or 2-chloroethylphosphonic acid, on the tapping panel is necessary to increase both the latex flow after tapping and latex regeneration between two tappings. Harvesting systems, frequency of tapping and ethephon application, are dependent on the latex metabolism  is a renewable product, which plays an important role in the economy of producing countries. tabolism . Many patabolism . Sucrosetabolism . RSH contabolism .NR production is dramatically affected by Tapping Panel Dryness (TPD) . TPD is Breeding for TPD-tolerant rubber clones is impeded by a long phenotyping period and a weak knowledge of molecular and genetic bases of TPD. Little is known about the phenotypic variability for TPD tolerance. A sufficient number of TPD-affected trees can be only observed in a population after 10–15 years of cultivation consisting of an immature period before production of 4–7 years according the clone growth, and several years of mature period of production. Although TPD may be governed by heritable genes , environHevea populations to determine their level of susceptibility to TPD? Second, are high latex metabolism clones such as PB 260 more susceptible to TPD than low-medium latex metabolism clones? Finally, can a LD be an indicator of TPD susceptibility? To answer to these questions, a new polyclonal trial (PT2) was built with five rubber clones including high latex metabolism clones  and 2 intermediate latex metabolism clones (BPM 24 and RRIC 100). In this paper, data of this trial (PT1) were re-analysed as well as data from PT2 conducted with a high tapping frequency and ethephon stimulation in order to validate the ability of this treatment to early induce the occurrence of TPD. The dry cut length (DCL), latex yield and LD were monthly monitored and analysed to better understand the onset of the TPD. This study revealed several patterns of TPD occurrence, challenged the perception of this physiological syndrome and validate the high tapping frequency and stimulation d1 ET 12/y as suitable method for further genetic analysis of TPD. Finally, the intrinsic sucrose content value of clones was negatively associated with TPD.Most of these studies rely on binary comparison between trees affected or not by TPD. A first dynamic analysis of TPD occurrence was attempted on a production potential trial with three rubber clones . Putrant22.1The polyclonal trial PT1 was established at the Sembawa Research Centre of the Indonesian Rubber Research Institute (2°57′42″S 104°30′30″E). This trial planted in 2004 was opened in December 2009. PT1 consists of 3 rubber clones . The tapping panel was tapped as a half spiral tapping (S/2). For each clone, three replicates of one tree each were observed for each of the nine combinations (3 tapping × 3 ethephon frequency levels). Trees were tapped every day (d1), 2 days (d2) and 4 days (d4). Ethephon was applied at 2.5% on the groove of the tapping panel of the bark at various frequencies (0 (0/y), 12 (12/y) and 24 (24/y) times a year).A partial dataset with LD data from March to May 2012 was published by Putranto and collaborators . Supplem2.2The polyclonal trial PT2 was established at the Sembawa Research Centre of the Indonesian Rubber Research Institute (GPS coordinates: 2°57′25″S 104°30′30″E). This trial planted in 2012 was opened in December 2016. PT2 consists of 5 rubber clones  including two new clones with high latex metabolism , in order to test the hypothesis of TPD susceptibility of these types of clones as clone PB 260. The tapping panel was tapped as a half spiral tapping (S/2). For each clone, fifteen trees were subjected to the tapping frequency d1 (6 days a week) and ethephon 2.5% applied on the groove of the tapping panel 12 times a year. 2.3Tapping panel dryness was assessed by visual assessment of the percentage of DCL instantly after tapping before latex flows on the groove. DCL was observed once a month by the same observer in order to avoid variability between observers. The latex yield was measured by weighing the cup lump from each tree per tapping. After tapping, latex was left uncollected and allowed to coagulate in the collecting cup overnight to form cup lump. Cup lump was collected and weighed the next day before subsequent tapping. The rubber yield was calculated based on the dry rubber content (DRC) measurement of the cup lump that was carried out periodically. Latex diagnosis parameters  were monthly measured 48 h after the last tapping and 3 weeks after the last ethephon stimulation in order to avoid any variation due to these harvesting stresses. Fresh latex (0.5 ml) was supplemented with 4.5 ml of 2.5% trichloroacetic acid (TCA) to induce protein precipitation, and then the latex serum was filtered with filter paper. This clear latex serum was then used for the determination of sucrose, inorganic phosphorous and reduced thiols contents using the anthrone method , the mol2.4https://www.xlstat.com).All data were recorded on the Microsoft ACCESS database management system. Specific tables were created for each series of variables . Relationships were established in order to combine data from different tables. Each relationship consists of fields of two tables with corresponding data. Queries were designed to extract dataset presented in 3The effect of tapping and ethephon stimulation frequencies was monitored tree per tree thirteen times during the three years of the PT1 trial for DCL, and once at the end of the trial for the brown bast . Trees wClass 3 consist of trees with a mean DCL lower than 20%. For trees with the lowest average of DCL at 1% , a partial low DCL was recorded for a period of time suggesting a localized small dry spot. Another tree of the class 3 (PT1F12) with a mean DCL value at 7% had a transient high DCL peaking at 50% revealing larger dry spot.For trees of class 2, DCL mean values range between 30% and 70%. For these trees, a transient high DCL can be observed more than thrice ranging from 50% to 100%.For trees of class 1, the mean of DCL is higher than 50%, except for tree PT1F43, which has contrasting DCL data (0–10% or 78–96%). The bark of ten trees was scraped at the end of the trial. Of the seven trees of class 1, three did not show any brown bast spot showing that BB does not occur systematically in high DCL trees.The ANOVA table reveals that DCL, latex yield and some LD parameters are influenced by several factors . DCL, laTukey test on expected marginal mean values computed by ANOVA for clone x tapping × ethephon interaction show that clone PB 260 has a significantly higher DCL values than the other clones for all treatments except for d2 and d4 without stimulation  appear for d1 with stimulation by ethephon 12 and 24 times a year. These intermediate DCL values are due to a high DCL occurring after 2 years of tapping . For othThis PT1 experiment showed that high DCL can be induced rapidly in the TPD susceptible clone PB 260 when using a high-tapping frequency harvesting system with ethephon stimulation. The clonal effect is highly significant for DCL. High tapping frequency and ethephon stimulation were applied on the PT2 trial in order to induce high DCL in susceptible clones and validate the previous results.DCL was monthly monitored for each tree during two years in the PT2 trial . As for Latex collected after tapping is supposed to come from a drainage area consisting of the articulated and anastomosed laticifer network A and B. The number of brown spots is linked to DCL level: eleven brown bast spots were seen for trees with high DCL, 1 spot with medium DCL, 2 with low DCL. To study more accurately the links between clone, DCL and BB classes, multiple correspondence analysis (MCA) was performed . On axisnd component, sucrose (0.86) is negatively correlated to DCL (−0.81). The linear regression of the mean values of monthly sucrose data on DCL is significant with a high correlation between these variables (R = 0.81)  . The reg4Tapping Panel Dryness is a physiological syndrome affecting latex production. Both preventing TPD in plantations and breeding for TPD-tolerant clones are still challenging, especially in a context of climate change. In this study, the dynamic analysis of TPD reveals new insights on the establishment of TPD, the relation between latex metabolism and the tolerance of clones to TPD as well as the ability to perform an early assessment of clones for TPD susceptibility.4.1In rubber plantations, TPD-affected trees are identified by tappers only when the latex yield is very low in the cup. This loss of production is associated with a late phase of TPD occurrence consisting of high DCL and brown bast occurrence. Agronomy literature often mentions the dry tree incidence corresponding to the number of dry trees per hectare.Physiological and molecular studies have also a binary approach considering healthy and dry rubber trees. Based on the determination of DCL, some authors have described TPD ranging from healthy, slight or mild, intermediate or moderate, and severe occurrence  or by clIn this study, monthly observation of DCL on the 147 trees of the PT1 (74 trees) and PT2 (73 trees) trials shows that even the trees considered to be free of TPD have small and localized dry spots. Small dry spots may appear on the tapped panel regardless of clone, harvesting system and season. Conversely, high DCL occurrence is significantly associated with clone and harvesting system factors . During Dry spots can appear in all trees during the rubber production cycle. Monitoring for DCL can help assess the level of its occurrence and determine if there is a risk of TPD affecting the latex production.4.2High latex metabolism rubber clones are often considered as TPD-susceptible clones . The lev2+, lutoid bursting index  is a way to accelerate the onset of TPD and facilitate its study. Most of the trees from clone PB 260 had an early high DCL (before 6 months after opening) in the two independent experiments PT1 and PT2 conducted at 6 years apart. According to Putranto and collaborators , high taAlthough agronomists have observed a clonal susceptibility to TPD, its heritability has never really been estimated by geneticists. The first reason is the low natural occurrence of TPD in plantations for standard recommended harvesting systems (about 1% per year). However, a certain level of clonal variation of TPD rate was determined in large-scale clone trials for six rubber clones . The sec5Hevea populations so as to identify TPD-susceptible genotypes. The genetic × environment interaction may also be analysed in order to make this study more robust. Sucrose content is an essential latex diagnosis parameter for estimating yield potential and responsiveness to ethephon stimulation. Sucrose in latex was here negatively associated with TPD susceptibility. In a context of climate change, breeding for TPD-tolerant clones is becoming a matter of emergency. Studies on molecular and genetic bases of TPD should be facilitated by using intensive harvesting system and such a physiological marker. These findings should lead to applications especially in breeding programmes and genetics studies.This paper set out a new perception of Tapping Panel Dryness. All trees develop more or less important dry spots without systematically affecting latex production whatever the harvesting system and the season. The level of DCL changes over time. The irreversible form of TPD (brown bast) sets in when high DCL occurs. The application of an intensive harvesting system (d1 ET 12/y) induces early TPD occurrence and homogenizes the clonal response in the TPD susceptible clone PB 260. These results suggest that this methodology be applied to phenotype Pascal Montoro, Fetrina Oktavia: Conceived and designed the experiments.Eva Herlinawati, Sigit Ismawanto, Afdholiatus Syafaah, Martini Aji: Performed the experiments.Pascal Montoro, Eva Herlinawati, Eric Gohet, Michel Giner, Albert Flori: Analyzed and interpreted the data.Eva Herlinawati, Afdholiatus Syafaah, Martini Aji: Contributed reagents, materials, analysis tools or data.Pascal Montoro: Wrote the paper.Dr. Fetrina Oktavia was supported by Indonesian Rubber Research Institute.10.13039/501100007204CIRAD.Mr. Pascal Montoro was supported by Data included in article/supplementary material/referenced in article.The authors declare no conflict of interest.No additional information is available for this paper.


Rhipicephalus microplus populations. The present study evaluated the compatibility of different Metarhizium spp. propagules  by incubating them with synthetic acaricides  for 1 h, 5 h, 10 h, and 24 h. Conidia and microsclerotia of the tested isolates were usually more tolerant to synthetic acaricides than blastospores. Our study also analyzed the in vitro effect of deltamethrin associated with fungal propagules for controlling a population of R. microplus females that were not susceptible to this synthetic acaricide. The use of entomopathogenic fungi in association with deltamethrin in this tick population caused a greater tick control than did the use of the fungus or the synthetic acaricide separately.The inappropriate use of synthetic acaricides has selected resistant  Rhipicephalus microplus is a blood-sucking ectoparasite. Infestations with this tick are common on cattle in tropical areas. R. microplus control strategies involve the use of synthetic acaricides, and the main classes used are organochlorines, synthetic pyrethroids, organophosphates, amidines, phenylpyrazoles, growth regulators, and avermectins  with synthetic acaricides and their in vitro effect against engorged females of R. microplus.Most mycoinsecticides currently produced in Brazil are based on aerial conidia that are harvested after solid substrate fermentation . On the Metarhizium robertsii isolate ARSEF 2575, which was obtained from the Agricultural Research Service Collection of Entomopathogenic Fungal Cultures  and Metarhizium pingshaense isolate LCM S09 . The fungal isolates used here were  LCM S09 . Because®, MSD, São Paulo, Brazil), deltamethrin  and a mixture of pyrethroid, organophosphate and monoterpenoid alcohol . Each commercial acaricide was added to Metarhizium conidial Tween 80® suspensions  and was kept in contact for four different periods . The final concentrations of the acaricides followed what is recommended by their manufacturers for spraying on cattle . Tween 80® sterile distilled water (0.01%) was added to the fungal suspensions in the control groups. The suspensions were then plated onto OA and left at 25 °C ± 1 °C and RH ≥ 80% for 96 h , from 60 g of oat flour added to 12.5 g of agar in 1 liter of distilled water, at 25 °C ± 1 °C and relative humidity (RH) ≥ 80% for 14 days. The compatibility of these conidia with acaricides was evaluated using three commercial products: amitraz  for 14 days. Three milliliters of conidial suspensions (108 conidia ml-1) were inoculated into 50 ml of oatmeal broth (60 g of oat flour and 42.1 gl-1 of glucose) at 200 rpm for 72 h at 25 °C ± 1 °C. The blastospores were produced as described by 3 blastospores ml-1. Each commercial acaricide was added to a Metarhizium blastospores suspension  and was left in contact for four different time periods, following the methodology described for conidia. CFUs were quantified and the relative culturability was calculated as described by For blastospores production, Metarhizium spp. were prepared as described by Metarhizium microsclerotia suspensions , following the methodology described for conidia. After the incubation with the synthetic acaricides, an aliquot of 30 microsclerotia was transferred to an agar-agar medium. Germination was assessed at 72 h. Microsclerotia granules were considered to have germinated upon observation of hyphal development around the granule. Experiments were considered valid when control plates contained at least 95% germinated microsclerotia. The relative culturability was calculated as previously described by Microsclerotia from ®) and M. robertsii or M. pingshaense propagules . The fungal propagules had not previously been incubated with the synthetic acaricide. The positive control group was treated either with the fungal propagules or the chemical acaricide alone. In the negative control group, the females were treated with Tween 80® aqueous suspension at 0.01% (vol/vol).Completely engorged females were distributed in 14 experimental groups. These ticks were treated with an association of deltamethrin  were transformed (√x + 0.5) and subjected to the Shapiro-Wilk normality test and Bartlett homogeneity of variance test. After these assumptions had been assessed, two-way ANOVA and the Tukey test (P < 0.05) were applied to compare these treatments. The biological parameters  exhibited non-normal distribution. Data with non-normal distribution were evaluated using the Kruskal-Wallis test followed by Dunn's test (P < 0.0001). All data were analyzed using the GraphPad Prism software, v.9.1.2.Metarhizium spp. conidia, blastospores and microsclerotia exposed to the different acaricides for the different lengths of time are shown in M. pingshaense conidia + combination; 1 h) to 23.8% . With blastospores, it ranged from 83.6%  to 0.7% . The relative culturability of microsclerotia ranged from 99.3%  to 47.8% . 24 h of incubation with acaricides negatively impacted the culturability of M. robertsii conidia and microsclerotia, but not that of M. pingshaense ; 5 h ; 10 h ; and 24h . Asterisks were included in The relative culturabilities of gshaense . SignifiM. pingshaense conidia and deltamethrin.R. microplus to acaricides is an issue that has been observed in several countries around the world and it occurs with different classes of acaricides. The intensity and frequency of use of these products and their misuse are factors that greatly contribute to occurrences of resistance. The use of entomopathogenic fungi is considered to be a promising alternative for tick control  blastospores were overall more tolerant than those of M. robertsii (ARSEF 2575)  . Also, MSEF 2575 .Our results revealed that the use of entomopathogenic fungi in association with synthetic acaricides (considering both their active ingredients and solvents) can represent a stressful condition for these fungi, particularly for some propagules and isolates when incubated for long periods. The distinct levels of culturability observed in the present study suggest that the isolates tested have different capacities to overcome stressful situations. This is important with regard to framing the strategies for integrated pest management. As reported in the literature, blastospores can be effective propagules against ticks , but if M. pingshaense LCM S09 yielded higher tick control percentages than did M. robertsii ARSEF 2575. Moreover, when the tick control percentages of M. pingshaense propagules were compared, conidia had better results. The tick population used in the present study was not susceptible to deltamethrin (®) or to the combination of acaricides (Colosso®) (unpublished results). That is why these synthetic acaricides were not used in the bioassay. Application of deltamethrin plus M. pingshaense LCM S09 conidia to ticks that were susceptible to this synthetic acaricide caused greater tick control than the fungus or the synthetic acaricide alone (M. pingshaense conidia + deltamethrin and M. robertsii microsclerotia + deltamethrin)  .in situ . Accordingly, the effect of the incubation of fungal propagules plus synthetic acaricides on tick control was not evaluated. Despite this, based on the results from the compatibility tests, it can be suggested that even if aricides .Metarhizium spp. conidia and microsclerotia were more compatible with synthetic acaricides than blastospores. The use of virulent entomopathogenic fungus plus deltamethrin gave rise to greater tick control than using the fungus or the acaricide separately in a population of ticks that were not susceptible to deltamethrin.


Transcriptome profiling of human whole blood is used to discover biomarkers of diseases and to assess phenotypic traits. Recently, finger-stick blood collection systems have allowed a less invasive and quicker collection of peripheral blood. Such non-invasive sampling of small volumes of blood offers practical advantages. The quality of gene expression data is strictly dependent on the steps used for the sample collection, extraction, preparation and sequencing. Here we have: (i) compared the manual and automated RNA extraction of small volumes of blood using the Tempus Spin RNA isolation kit and the MagMAX for Stabilized Blood RNA Isolation kit , respectively; and (ii) assessed the effect of TURBO DNA Free treatment on the transcriptomic data of RNA isolated from small volumes of blood. We have used the QuantSeq 3′ FWD mRNA-Seq Library Prep kit to prepare RNA-seq libraries, which were sequenced on the Illumina NextSeq 500 system. The samples isolated manually displayed a higher variability in the transcriptomic data as compared to the other samples. The TURBO DNA Free treatment affected the RNA samples negatively, decreasing the RNA yield and reducing the quality and reproducibility of the transcriptomic data. We conclude that automated extraction systems should be preferred over manual extraction systems for data consistency, and that the TURBO DNA Free treatment should be avoided when working on RNA samples isolated manually from small volumes of blood. The analysis of the human transcriptome allows us to understand the human genome at the gene expression level and also provides a window to understand gene regulation and genome plasticity4. However, gene expression profiling can only be of value when the RNA under study is representative of the starting material5. Unfortunately, several pre-analytical factors affect the RNA yield and quality and might hamper the representativeness of the starting RNA5, including RNA isolation methods, DNase treatments, library preparation etc. The ex vivo instability of RNA can be reduced if the blood is freshly extracted and processed for RNA isolation immediately. However, this is not a feasible option and, in most cases, blood is collected with variations in timing and storage conditions, which have been proven to affect transcriptomic profiles to some degree6. Different RNA stabilizers are employed to overcome the limitation of using fresh blood for RNA isolation9. Such stabilizer solutions immediately lyse cells chemically and stabilize nucleic acids. Cellular RNases are inactivated, and the RNA is selectively precipitated, leaving proteins and genomic DNA in solution. One of the most common RNA stabilizer solutions is represented by Tempus Blood RNA . Tempus system uses a solid-phase, silica-based isolation strategy and its performance has been proven higher than other systems10. Yet, Tempus Blood RNA utility is limited by the requirement of a venous blood samples of at least 3.0 ml. Recently, finger-stick blood collection systems have made it possible to collect peripheral blood without the need of medical infrastructures, offering practical and logistic advantages11. Nevertheless, technical improvements are required to make the gene expression profiling of small volumes of blood a reliable and reproducible technique12. Automated workflows offer several advantages for large-scale projects, as they increase sample throughput and reduce cost and manual errors16. The MagMAX for Stabilized Blood RNA Isolation kit  employs a magnetic bead-based technology to purify RNA from blood stored in Tempus solution. Because of its bead-based approach, it can easily be implemented on automated systems. The MagMax workflow includes a TURBO DNase step that removes contaminating DNA and can also be implemented in automation systems, such as the KingFisher Magnetic Particle Processors . However, there currently exist many liquid-handling workstations on the market, each one of them offers different degrees of flexibility. Hamilton Robotics , for instance, offers autonomous programming15. In this study the Hamilton NGS Star platform has been employed for automated RNA extraction.Transcriptome profiling is a reference research field, and it is applied especially for the study of human diseasesHere, we have compared the manual RNA isolation of small volumes of blood (Tempus Blood RNA kit) and an automated workflow implemented in-house by using the MagMAX for Stabilized Blood RNA Isolation kit on the Hamilton NGS Star platform ; we have also evaluated the effect of the TURBO DNA Free treatment  on the reproducibility and reliability of the transcriptomic data. Transcriptome sequencing was performed by using the Lexogen QuantSeq 3′ mRNA-Seq Library Prep FWD kit  with unique molecular identifiers (UMI), because of its streamlined protocol and its relatively lower cost as compared to other systems.Here we demonstrate that the automated extraction workflow produces more consistent data as compared to the manual extraction method and that the TURBO DNA Free treatment should be avoided when working on RNA isolated manually from small volumes of blood.11. Ethical approvals were collected from Sidra Institutional Review Board committee (IRB Protocol #1707011887). An informed consent was obtained from the study subjects and all methods were performed in accordance with the relevant guidelines and regulations. Different conditions were tested for each healthy donor recruited, as shown in Fig. Whole blood was collected from healthy donors as previously describedLexogen QuantSeq3′ mRNA-Seq libraries for Illumina sequencing were prepared from 120 ng of total RNA according to the manufacturer’s recommendations.The first strand was synthesized by reverse transcription with oligo-dT priming followed by treatment with The Globin Block (RS-GB) Module for QuantSeq. The RS-GB solution has specific oligos which selectively bind to the globin mRNA cDNA-transcripts and prevent the generation of library fragments from globin mRNAs, by blocking their extension during second strand synthesis, initiated by random priming. Because the globin blocking oligo is bound close to the poly-(T)-section of the first strand, the second strand synthesis stops for globin transcripts and does not reach the 5′ sequencing tag of the first strand, thus yielding non-amplifiable globin cDNAs. The non-globin tagged double-stranded cDNA library fragments were then amplified for 18 PCR cycles and labelled with different single indices. The UMI Second Strand Synthesis Mix (USS) containing Unique Molecular Identifiers (UMIs) was used during second strand synthesis prior to PCR. UMIs act as tags that allow detection and removal of PCR duplicates in sequencing data.The quality and size of the libraries were determined using the NGS 3K assay on the Labchip GXII and pooled based on quantification via qPCR using the KAPA HiFi Library quantification kit on the LightCycler 480 II . Libraries were pooled on the Hamilton MicroLab Star and sequenced on the Illumina NextSeq 500 system at a depth of 8 million reads per library. Out of the 60 samples, 48 samples were sequenced using the High Output 75 cycles kit and single end read mode and 12 samples were sequenced using the High Output 150 cycles kit and single end read mode . We have used the recommended QuantSeq FWD-UMI Data Analysis Pipeline which is specific for Quantseq FWD libraries that contain UMIs. First, the umi2index process adds the 6 nucleotide UMI sequence to the identifier of each read and trims the UMI from the start of each read; the FASTQ file generated at this step was processed through trimming and alignment. Quality trimming was performed to remove the adapter sequences and polyA tails using bbduk.sh from BBMap v38.69). Per base sequence quality plots showing average quality scores above 30 for raw fastq and the quality trimmed fastq is reported in Supplementary Fig. 8.69. PerNormalized data was transformed using variance-stabilizing transform (VST) and removed batch effect using limma::removeBatchEffect from Lima package (v3.48.2). The RSeQC (v3.0.1) geneBody_coverage2.py and the RSeQC’s read_distribution.py modules were used to calculate the RNA-seq reads coverage over the gene body and the distribution of mapped reads across genomic features, respectively. Heatmaps, correlation matrices and PCA plots were generated as relevant by using R packages.A simulation analysis was performed using RSEM (v1.2.25). Reads of 75 nt were simulated to different depths, namely: 50 M, 100 M, and 400 M reads using rsem-simulate-reads. This program took estimated_model_file and estimated_isoform_results as input which were learned from real data using rsem-calculate-expression program. Simulated reads were analyzed in the same way as for the real data  and HTSeq-count (0.9.1) tools followed by DeSeq2 VST normalization and generate QC plots).r test was used to evaluate correlations. All statistical tests were two-sided. p-values lower than 0.05 were considered statistically significant.Non-parametric Wilcoxon signed-ranks and Mann–Whitney tests were applied to compare groups as appropriate. Non-parametric Spearman Samples were divided into 6 groups, according to their (i) extraction method ; (ii) TURBO DNA Free treatment (treated vs untreated), and iii. starting volume of whole blood . The elution volume was 50 μl for all the extractions performed. Thus, we have used the Qubit (fluorescence-based) concentration values (ng/μl) for comparative analysis. As expected, the RNA concentration increased parallelly to the increased volume of blood used for the extraction, with the concentration obtained from 66 μl of blood being significantly higher as compared to the concentration obtained from 16 μl of blood  we found no significant correlation between the manual and automated protocols that included the TURBO DNA Free treatment, suggesting that workflow-specific steps might affect the RNA concentration more than the TURBO DNA Free treatment and biological variables . The A260/A280 values were > 2 for all the experimental groups except for the manual TURBO-treated 50 μl blood samples that displayed an average A260/A280 ratio of 1.31 ± 0.09.We next sought to assess the effect of TURBO DNA Free treatment on the RNA yield and RIN values. For the manual protocol the TURBO DNA Free treatment resulted in an overall yield reduction > 25% Table . HoweverWe assessed the reproducibility of gene expression profiles obtained from the different RNA extraction methods. Out of the 60 samples, 2 samples extracted manually from the same donor generated a library of a size and concentration deviating from what is recommended for Lexogen Quant-Seq 3′mRNA-Seq Library Prep and were removed from the downstream processing. This could be due to the low purity of the samples as their Nanodrop readings demonstrated a high A260/A230 ratio. Two additional samples produced libraries of suboptimal molarity and were labeled as “low conc. library” for further analyses. After sequencing and read mapping, we have evaluated the alignment scores, the count assignments and read distribution across the samples. Because in the Lexogen Quant-Seq 3′mRNA-Seq Library Prep method cDNA molecules are transcribed from the 3′ end of the mRNAs, the reads preferentially mapped to the 3′ end of the transcript. Samples processed manually had a slightly higher percentage of reads mapping to exonic region, however the samples showed overall consistent body coverage, alignment scores and gene type assignment profiles .The assignment of the samples to the three individuals accurately predicted their distribution in a three-dimensional space suggesting that their transcriptional signatures can be retraced to the individual biology Fig. B. ContraNevertheless, the correlation matrix identified an overall high degree of similarity across the samples isolated with the automation method as compared to the ones isolated manually, irrespective to starting blood volume and DNase treatment Fig. A. When pWe have also evaluated the processing time of the standard manual extraction protocol and the automated protocol developed in-house on the Hamilton NGS Star platform.The manual workflow overall takes about 75 min hands-on-time and 65 min incubation time, while the automated workflow overall takes 20 min hands-on-time and 50 min incubation time. The above calculations refer to the processing of a batch of 24 samples. However, the sample throughput can be significantly increased in the automated workflow as the Hamilton NGS STAR system is equipped with 3 × 32 sample tube carriers and it can process 96 samples per batch. Additionally, faster bead clean-up steps can be adopted to this method if the liquid handler is equipped with 96-Multi Probe Head.18. Blood gene expression profiles can be significantly affected by blood collection and RNA isolation methods21. This is mainly due to the differences in the composition of RNA-stabilizing solutions or differences in the chemistries employed by the different RNA isolation methods. As the manual protocol employs spin columns while the automated protocol uses a magnetic beads approach, we questioned whether the use of the two different methods in this study could have impacted the RNA QC and gene expression profiles. Although we found significant differences in RNA quality and yield, overall the gene expression profiles were maintained, and the inter-individual differences were reproducible across the different extraction methods. The transcriptomic profiles were in fact driven mainly by the subject assignment rather than by analytical variables, suggesting that the Lexogen QuantSeq 3′ FWD mRNA-Seq is a robust method for gene expression profiling. The Lexogen QuantSeq 3′ FWD mRNA-Seq has a streamlined protocol, does not require RNA fragmentation before reverse transcription and only detects the 3′ end of the mRNA, thus it has been employed for low input and highly degraded RNA24. The present study supports the Lexogen QuantSeq 3′ FWD mRNA-Seq application for RNA isolated from small volumes of blood.Transcriptomic profiling of peripheral blood is often employed for the identification of susceptibility genes or biomarkers of human phenotypes and diseases25. Automated solutions are currently applied in many fields of life sciences; especially in genomics, laboratory specialists are streamlining their protocols by using automated workstations28. Automated solutions help cutting costs associated to manual labor and also help wet-lab specialists who would not have to spend long time processing samples15. In our study we questioned whether consistent expression profiles could be obtained from the automated isolation of volumes of blood < 50 μl. We found that volumes of blood samples as low as 16 μl provide reliable transcriptomic profiles; interestingly, these samples displayed the lowest variability, suggesting that our in-house approach could be applied in studies where blood is limited.Especially when working on small volumes of samples, pipetting accuracy and reproducibility are of critical importance; automated RNA isolation systems reduce manual errors and should ensure a higher data reproducibility30. However, to the best of our knowledge this is the first assessment on the effect of TURBO DNA Free treatment on RNA isolated from small volumes of blood by using a manual and an automated workflow. The TURBO DNA Free treatment impacted more negatively the samples processed manually. The DNase inactivation reagent is in fact known to sequester divalent cations, change the buffer condition and interfere with enzymatic reactions. In our study, when we compared the automated and manual workflows both including the TURBO DNA Free treatment, we found the treatment to have a stronger negative impact on the manual samples, likely because the treatment is performed at the end of the workflow, differently to the automated protocol.Other groups have employed TURBO DNA Free treatment for transcriptomic profilesWe expect this study to increase the adoption of automation systems for RNA isolation from small volumes of blood especially in core facility settings where sample throughput and turn-around-time are of critical importance.Collectively, these results indicate that transcriptomic profiles obtained using the Lexogen QuantSeq 3′ FWD mRNA-Seq protocol are highly reproducible across different extraction methods employed for small volumes of blood, despite differences in RNA quantity and quality. The TURBO DNase treatment should be avoided when isolating RNA from small volumes of blood. The data produced from the automated method displayed less variability as compared to the manual method.Supplementary Information.


Flap endonuclease 1 (FEN1) is an essential enzyme that removes RNA primers and base lesions during DNA lagging strand maturation and long-patch base excision repair (BER). It plays a crucial role in maintaining genome stability and integrity. FEN1 is also implicated in RNA processing and biogenesis. A recent study from our group has shown that FEN1 is involved in trinucleotide repeat deletion by processing the RNA strand in R-loops through BER, further suggesting that the enzyme can modulate genome stability by facilitating the resolution of R-loops. However, it remains unknown how FEN1 can process RNA to resolve an R-loop. In this study, we examined the FEN1 cleavage activity on the RNA:DNA hybrid intermediates generated during DNA lagging strand processing and BER in R-loops. We found that both human and yeast FEN1 efficiently cleaved an RNA flap in the intermediates using its endonuclease activity. We further demonstrated that FEN1 was recruited to R-loops in normal human fibroblasts and senataxin-deficient (AOA2) fibroblasts, and its R-loop recruitment was significantly increased by oxidative DNA damage. We showed that FEN1 specifically employed its endonucleolytic cleavage activity to remove the RNA strand in an R-loop during BER. We found that FEN1 coordinated its DNA and RNA endonucleolytic cleavage activity with the 3′-5′ exonuclease of APE1 to resolve the R-loop. Our results further suggest that FEN1 employed its unique tracking mechanism to endonucleolytically cleave the RNA strand in an R-loop by coordinating with other BER enzymes and cofactors during BER. Our study provides the first evidence that FEN1 endonucleolytic cleavage can result in the resolution of R-loops via the BER pathway, thereby maintaining genome integrity. Flap endonuclease 1 (FEN1) is a central component of DNA metabolism that plays an essential role in removing RNA primers and DNA base damage during DNA lagging strand maturation and long-patch base excision repair (BER) . FEN1 beBesides its roles in DNA replication, BER, and genome maintenance, FEN1 is involved in other cellular functions by processing DNA and RNA and modulating epigenetic features. It is implicated that FEN1 can induce apoptosis by interacting with the proteins of DNA degradosome . The enzInterestingly, FEN1 is also implicated in RNA processing and ribosome biogenesis . An earlR-loops are bulky non-B form DNA structures that contain an RNA:DNA hybrid and a non-template single-strand DNA (ssDNA), frequently generated during gene transcription . They plThe primary normal human fibroblasts and ataxia with oculomotor apraxia type 2 (AOA2) patient fibroblasts with SETX deficiency were generously provided by Dr. Kenneth Fischbeck at the National Institute of Neurological Disorders and Stroke/National Institutes of Health. Fetal Bovine Serum (FBS) and Dulbecco’s Modified Medium (DMEM) high glucose cell culture medium were from Thermo Fisher Scientific . 32P-cordycepin triphosphate (5000 Ci/mmol), and γ-32P-adenosine triphosphate (3000 Ci/mmol) were from PerkinElmer Inc. . T4 polynucleotide kinase was from New England BioLabs . Terminal deoxynucleotidyl transferase was from Thermo Fisher Scientific . Diethyl pyrocarbonate (DEPC) was from MP Biomedicals . Deoxynucleosides triphosphate were from Sigma-Aldrich . All other chemicals were from Thermo Fisher Scientific  and Sigma-Aldrich .RNA and DNA oligonucleotides were synthesized by Integrated DNA Technologies  or Eurofins Genomics . Radionucleotides, α-Escherichia coli (E. Coli) BL21(DE3) and purified using FPLC according to the procedures described [g rpm for 30 min. Proteins were separated through affinity, ion exchange, and hydrophobic chromatography. After a series of sequential chromatographic purification, the peak fractions of BER proteins were pooled and dialyzed into the storage buffer containing 30 mM HEPES, pH 7.5, 50 mM KCl, 20% glycerol, and 1 mM PMSF. Recombinant Saccharomyces cerevisiae FEN1 (Rad27) was cloned into T7 expression vector pET-24b , expressed in E. coli strain BL21(DE3) codon plus (Stratagene), and purified as previously described, resulting in a recombinant protein with a C-terminal His-tag [Recombinant human APE1, pol β, FEN1, and LIG I was expressed in escribed ,32,33. F His-tag . All purDuplex oligonucleotide substrates containing a 19 nt RNA primer were constructed by annealing a downstream primer with an RNA primer, the template, and a DNA upstream primer with varying lengths (29–48 nt) at a molar ratio of 1:3:5. The R-loop substrate containing an abasic site  on the non-template DNA and 36 nt RNA:DNA hybrid was created by annealing the non-template DNA with a THF residue and the 36 nt-RNA strand with the template strand at a molar ratio of 4:1:3. The substrate that contains the double DNA flaps with a 36 nt RNA:DNA hybrid was constructed by annealing the 34 nt upstream primer, the RNA primer, the 33 nt downstream DNA strand with a THF residue with the template strand at a ratio of 4:1:4:3. Substrates were constructed in the annealing buffer  that was prepared with water treated by 0.1% diethylpyrocarbonate (DEPC) with denaturation of the oligonucleotides at 96 °C for 5 min and cooling down to 25 °C. The sequences of the substrates are listed in 2+, and 2 mM ATP at 37 °C for 30 min. Reactions were stopped with a 2× stopping buffer (95% deionized formamide and 10 mM EDTA). Cleavage of RNA by FEN1 and APE1 and BER enzymatic activities were determined by incubating 25 nM substrates with different concentrations of FEN1 and APE1 in the absence or presence of pol β and LIG I in BER reaction buffer . Reconstituted BER reactions were performed by incubating 25 nM substrate with different concentrations of FEN1 in the presence of 50 nM APE1, 5 nM pol β, and 10 nM LIG I or with various concentrations of APE1 in the presence of 25 nM FEN1, 5 nM pol β, and 10 nM LIG I. Reactions (20 µL) were assembled in BER reaction buffer containing 50 µM dNTPs, 5 mM Mg2 and 3% O2). Cells were treated with 10 mM KBrO3 for 75 min and fixed in 4% PFA .Normal human fibroblasts (Hum Fbs) and senataxin deficient human fibroblasts (AOA2 Fbs) were grown to 60−80% confluence in Media , 20% FBS , 1 mM sodium pyruvate , 1 mM GlutaMax , 1× MEM-NEAA , 1× Antibiotic-Antimycotic  in reduced oxygen conditions  for 1 h at 37 °C. The negative control experiments were performed by digesting RNA:DNA hybrids in cells with 0.1 U/μL RNase H (NEB cat#M0297S) in RNaseH buffer (NEB) for 5 h at 37 °C. Cells were blocked in blocking buffer containing 3% BSA , 3% donkey serum , 3% goat serum (Sigma Aldrich cat# S26-M) in PBS for 30 min at 37 °C and subjected to the incubation with the primary antibodies (1:500 dilution in 10% blocking buffer in PBS) for 1 h at 37 °C. Cells were washed twice with PBS (10 min each) and subjected to incubation with the secondary antibodies (1:1000 dilution in 10% blocking buffer in PBS) along with 0.1 μg/mL DAPI (Sigma Aldrich cat# D9542) for 30 min at 37 °C. Cells were washed twice with PBS (10 min each) and refixed (4% PFA for 20 min) before being covered in antifade  and imaged. Antibodies used for the experiments included S9.6 mouse anti-DNA:RNA hybrid antibody , rabbit anti-FEN1 , donkey anti-mouse-Alexa488 , goat anti-Rb-Alexa546 . Fixed fibroblast cells were permeabilized , 0.05% Tween-20 , 1 M glycine  for 2 min at room temperature. To avoid the detection of non-specific signals resulting from single- and double-stranded RNA, cells were pretreated with single- and double-strand RNA nucleases RNase III (0.05 U/μL)  and RNase T1 (0.04 U/μL)  in 1 x Shortcut buffer (New England BioLabs) with 20 mM MnClCells were imaged . Images were analyzed using Fiji/ImageJ software with the JACoP pluggin . BrieflySince calf thymus FEN1 can cleave an RNA flap generated on a DNA lagging strand , we initWe then asked if FEN1 also used its 5′-3′ exonuclease activity to continuously remove the RNA strand after the enzyme cleaved an RNA flap. We tested this possibility by examining human FEN1 cleavage activity on the substrate containing the 10 nt-RNA flap within RNA at various time intervals . The sub3) in normal human fibroblasts and SETX helicase-deficient, AOA2 fibroblasts using immunofluorescence  in cells [3, suggesting that FEN1 was recruited to process the RNA on R-loops during BER of oxidative DNA damage in normal and AOA2 fibroblasts. We then examined FEN1 cleavage activity on the RNA on an R-loop containing a 36 nt-RNA:DNA hybrid and an abasic lesion in the middle of the non-template strand (Since a recent study has implicated a role of FEN1 in processing R-loops in telomeres , and therescence . We founrescence  because rescence A. Upon tin cells , the cole strand . FEN1 cle strand . We foune strand . Similare strand . We foune strand . At 5 mie strand . At 15 me strand . The resAs one of the key BER enzymes, APE1 can incise the 5′-end of an abasic site using its endonucleolytic cleavage activity, creating a 1 nt-gap as the substrate for pol β . APE1 alTo further determine if FEN1 and APE1 cleavage on RNA can coordinate to promote the resolution of an R-loop through BER, we reconstituted BER on the R-loop substrates in the presence of increasing concentrations of FEN1 or APE1 . We founIn this study, we identified a unique role of FEN1 RNA processing in resolving an R-loop through BER. We found that human and yeast FEN1 endonucleolytically cleaved a nicked RNA and an RNA flap . We furtEarly work from the Bambara group shows that calf thymus FEN1 can cleave an RNA flap , indicatThe Hickson group identified the cleavage of RNAs by APE1 in the 1990s . Later sOur study further indicates that BER may serve as a unique channel to resolve R-loops through FEN1 endonucleolytic cleavage of DNAs and RNAs in coordination with APE1 3′-exoribonuclease . SeveralOur results showed that DNA base lesions that occurred on the non-template strand of an R-loop were efficiently removed through BER. This, in turn, resulted in the ssDNA breaks on the non-template strand, converting the R-loop into a double-flap with an RNA:DNA hybrid that was removed by FEN1 DNA flap cleavage and endoribonuclease activity along with APE1 3′-exoribonuclease activity. Since it is implicated that a DNA glycosylase, SMUG1  and BER Our study has demonstrated that FEN1 can process RNA endonucleolytically. FEN1 endonucleolytic flap cleavage coordinates with APE1 exoribonuclease activity to remove RNA strands in RNA:DNA hybrid, thereby resolving an R-loop through BER.


HSPD1/TNF/MMP14/ITGB1) mRNAs and their upstream noncoding RNA regulators (miRNA-6881-5P and lnc-SPARCL1-1:2) in NASH rats. Results: High sucrose high fat (HSHF) diet-induced NASH rats were treated with different concentrations of rosavin  for the last four weeks of dietary manipulation. The data revealed that rosavin had the ability to modulate the expression of the hepatic cell death-related RNA panel through the upregulation of both (HSPD1/TNF/MMP14/ITGB1) mRNAs and their epigenetic regulators (miRNA-6881-5P and lnc-SPARCL1-1:2). Moreover, rosavin ameliorated the deterioration in both liver functions and lipid profile, and thereby improved the hepatic inflammation, fibrosis, and apoptosis, as evidenced by the decreased protein levels of IL6, TNF-α, and caspase-3 in liver sections of treated animals compared to the untreated NASH rats. Conclusion: Rosavin has demonstrated a potential ability to attenuate disease progression and inhibit hepatic cell death in the NASH animal model. The produced effect was correlated with upregulation of the hepatic cell death-related  mRNAs—(miRNA-6881-5P—(lnc-SPARCL1-1:2) RNA panel.Background: Non-alcoholic fatty liver disease (NAFLD) represents the most common form of chronic liver disease that urgently needs effective therapy. Rosavin, a major constituent of the Rhodiola Rosea plant of the family Crassulaceae, is believed to exhibit multiple pharmacological effects on diverse diseases. However, its effect on non-alcoholic steatohepatitis (NASH), the progressive form of NAFLD, and the underlying mechanisms are not fully illustrated. Aim: Investigate the pharmacological activity and potential mechanism of rosavin treatment on NASH management via targeting hepatic cell death-related ( Non-alcoholic fatty liver disease (NAFLD) has become a worldwide health problem with increased prevalence, and serious complications . It rangThe pathogenesis of NASH is complex and includes the perturbation of several sophisticated mechanisms, such as host genetics, environmental factors, intestinal dysbiosis, innate immune activation, hepatic inflammation, cell death, or fibrogenesis with progressive cell damage . Under lIncreasing evidence has also highlighted the implicating role of epigenetic mechanisms, including noncoding RNAs (ncRNAs), in the pathogenesis of human NASH . The maiInterestingly, a number of in vitro and in vivo studies have reported that the Rhodiola Rosea (R. Rosea), a member of the plant family Crassulaceae, possesses various protective effects such as anti-antioxidant, anti-inflammatory, and hepato-protective effects ,15,16. THSPD1/TNF/MMP14/ITGB1) mRNAs and their upstream noncoding RNA regulators (miRNA-6881-5P and lnc-SPARCL1-1:2) in NASH animal model.Therefore, the present study aimed to investigate the pharmacological activity and potential mechanism of Rosavin treatment on NASH management via targeting hepatic cell death-related  with accession MI0022728  were assessed in serum using commercial kits according to the manufacturer’s instructions and performed by AU680 multifunctional biochemistry analyzer .The buffered formalin-fixed liver samples were dehydrated in alcohol, embedded in paraffin wax, and cut into thin sections (4-μm thick). Tissue sections were subjected to histological stains; hematoxylin and eosin (H&E) and Masson’s trichrome stains to evaluate the morphological changes and collagen deposition, respectively. The liver sections were assessed according to the SAF system  which asLeica Qwin 500 C image analyzer  was used to assess the followings: (i) The area percent (%) of collagen deposition for the Masson’s trichrome stained sections. www.ncbi.nlm.nih.gov/geo/, accessed on 22 July 2021) [HSPD1), tumor necrosis factor (TNF), Matrix metalloproteinase 14 (MMP14), and integrin β1 (ITGB1) were selected as they are crucial players contributing to hepatic cell death through their embroilment either in the inflammatory response, oxidative stress, apoptotic process, anoikis, or cell adhesion  was used to retrieve miRNAs that interact with the four selected mRNAs. It was found that miRNA-6881-5P could interact with the 4 selected mRNAs. Lastly, the interaction between the retrieved miRNA and lncRNA was predicted by using miRWalk 2.0. The lncRNA SPARCL1-1:2 was identified to be interacting with the retrieved miRNA-6881-5P. Finally, (HSPD1/TNF/MMP14/ITGB1) mRNAs—(miRNA-6881-5P)—( lnc-SPARCL1-1:2) RNA panel was constructed. It is noteworthy to mention that the constructed RNA panel was previously validated in our internationally published research on human NASH [Based on our interest, the RNAs species that are implicated in NASH pathogenesis and related to hepatic cell death were screened. Firstly, the differentially expressed genes (DEGs) in NASH were screened from the Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) (ly 2021) ,49. Fromadhesion . Secondlhttps://github.com/openbabel/openbabel/releases/tag/openbabel-3-1-1) [https://bio-hpc.ucam.edu/achilles, accessed on 22 July 2021) [https://plip-tool.biotec.tu-dresden.de/, accessed on 22 July 2021) [We used blind docking algorithm to predict the interaction between rosavin and dysregulated corresponding proteins from the selected mRNAs. One monomer from the human mitochondrial HSPD1 single ring was used for docking since they were all identical. The protein structure was obtained from Protein Data Bank (PDB) with identification (PDB ID: 7AZP) . Integril-3-1-1)  and MGL ly 2021)  which usly 2021)  for dockly 2021) . The intly 2021) .http://rna.tbi.univie.ac.at/, accessed on 22 July 2021) [https://rnacomposer.cs.put.poznan.pl/, accessed on 22 July 2021) [We obtained the secondary structure of miRNA-6881-5P using RNAFold web server under Vienna RNA tools (ly 2021) . The secly 2021) ,61. We uly 2021) .Total RNA was isolated from the 50 mg of frozen liver tissue samples using miRNeasy Mini Kit  as per instructions provided by the manufacturer. The concentration and purity of total RNA were measured using NanoDrop ; the purity of the extracted RNAs (A260/A280) was 1.8–2. The total RNA extracted from the liver tissues was immediately reverse transcribed into single-stranded complementary DNA (cDNA) using RT2 First Strand Kit  and miScript II RT , as per instructions provided by the manufacturer. The reaction was conducted in Thermo Hybaid PCR express Thermal Cycler . SNORD72 were used as the housekeeping genes to normalize the raw data for the chosen mRNA, miRNA and lncRNA, respectively. All primer reagents used in this study were obtained from Qiagen, Germany  and RT2 SYBR Green ROX qPCR Mastermix , respectively. miRNA-6881-5P expression in liver samples was measured by using miScript SYBR Green PCR Kit ; according to the manufacturing protocol. The GAPDH, and Ct−ΔΔ [Real-time (RT)-qPCR has been in an Applied Biosystems 7500 FAST RT-PCR system  thermal cycler. The thermal program for the SYBR Green-based qPCR was as follows: denaturation at 95 °C for 10 min; 45 cycles of denaturation for 15 s at 95 °C; followed by annealing for 30 s at 55 °C; and lastly, extension for 30 s at 70 °C. All reactions were performed in duplicate. The threshold cycle (Ct) values over 36 were considered negative expressions. The relative quantification of RNA expression was carried out using the Livak method, where RQ = 2−ΔΔCt .Immunohistochemistry for the paraffin sections was carried out using rabbit anti-caspase 3  as a marker for apoptosis , rabbit p < 0.05 was considered to be statistically significant.Data were presented as the mean ± SD. The distribution normality of the data was achieved using Shapiro–Wilk test. Statistical differences among experimental groups were determined by one-way analysis of variance (ANOVA) using GraphPad Prism software, version 8.0 . Tukey’s test method was utilized to compare differences between groups. Correlation test was performed using the Pearson correlation coefficient. Rosavin has demonstrated a potential ability to attenuate NASH progression, inhibit hepatic cell death, and diminish the metabolic and pathological disturbances observed in the applied NASH animal model. The produced effect was correlated with upregulation of the hepatic cell death-related  mRNAs—(miRNA-6881-5P)—(lnc-SPARCL1-1:2) RNA panel.


The acquisition of functions via the elongation of nucleotides is an important factor in the development of the RNA world. In our previous study, we found that the introduction of complementary seven-membered kissing loops into inactive R3C ligase ribozymes revived their ligation activity. In this study, we applied the kissing complex formation-induced rearrangement of RNAs to two nonfunctional RNAs by introducing complementary seven-membered loops into each of them. By combining these two forms of RNAs, the ligase activity (derived from the R3C ligase ribozyme) as well as cleavage activity (derived from the hammerhead ribozyme) was obtained. Thus, effective RNA evolution toward the formation of a life system may require the achievement of “multiple” functions via kissing-loop interactions, as indicated in this study. Our results point toward the versatility of kissing-loop interactions in the evolution of RNA, i.e., two small nonfunctional RNAs can gain dual functions via a kissing-loop interaction. The “RNA world” hypothesis  was proptrans-rearrangement of two nonfunctional shortened R3C ribozymes . On. On11]. The native gel electrophoretic characterization showed that <hairpin-ΔU> alone behaved as a monomer, but <A> alone behaved as both a monomer and dimer . Howeverex = 473 nm, Y520 filter) associated with 6-FAM-labeled RNAs on a denaturing 12% polyacrylamide gel electrophoresis, and used the ligation activity of the R3C ligase ribozyme as a positive control  associated with Cy5-labeled RNAs . TheAs shown in previous reports regarding kissing-loop interactions ,38,39,40Trans-molecular structural changes due to kissing-loop interactions utilized in our present study are a familiar phenomenon in biological systems, e.g., in HIV dimerization [Regarding “two ribozyme functions from one RNA” and “evolution of RNA by adding nucleotides”, DasGupta et al. also showed that a single sequence could harbor two distinct active sites and perform both hairpin ribozyme and hammerhead ribozyme activities. They also proposed RNA evolution by nucleotide accretion . Trans-mrization ,46,47,4821 possible 35-mers would weigh only approximately 22 g (the mass of the Earth is approximately 6 × 1024 kg). Therefore, when considering the effects of ligation and time scale, it does not necessarily seem to be impossible that the two RNAs used in our study could be synthesized via random processes on primitive Earth.In terms of accurate replication under Darwinian selection in enzyme-free nucleotide polymerization, Eigen and Schuster insisted that the nucleotide length is no more than approximately 100 when the physical properties inherent to current nucleic acids are considered . In addiFinally, we comment on the transition from the RNA world to the protein theater. Among some in vitro evolved ligase ribozymes, the class I ligase ribozyme has a catalytic rate of the order of 100 cycles per minute ,54, whic