Hepatic metastasis of soft tissue sarcoma is rare compared to lung metastasis, and the literature is scarce. We examined the risk of hepatic metastasis according to the site of occurrence and histological type.From a Hospital‐based Cancer Registry, 658 patients registered between 2007 and 2017 with soft tissue sarcomas were evaluated. The exclusion criteria were gastrointestinal stromal tumors, tumors of unknown origin, and follow‐up periods of less than 1 month. SPSS 25 was used for statistical analysis.The risk of hepatic metastasis was significantly higher in the retroperitoneum  and leiomyosarcoma . Multivariate analysis showed that the risk of hepatic metastasis as first distant metastasis was high in leiomyosarcoma  and retroperitoneal onset . The 2‐year survival rate after hepatic metastasis was 21.7%.The onset of hepatic metastasis indicates a poor prognosis. However, hepatic metastasis from retroperitoneal sarcoma and leiomyosarcoma may be the first distant metastasis in some cases. For retroperitoneal sarcoma and leiomyosarcoma, additional screening for hepatic metastasis such as contrast CT should be considered during staging and follow‐up after treatment. Following the analysis of 658 cases of soft tissue sarcomas excluding gastrointestinal stromal tumors registered in the Hospital‐based Cancer Registry from 2007 to 2017, we found that the percentage of retroperitoneal sarcomas and leiomyosarcomas causing hepatic metastasis was high at first visit, as the first distant metastasis, and throughout the entire course of the disease. For retroperitoneal sarcomas and leiomyosarcomas, additional screening for hepatic metastases should be considered during staging and postoperative follow‐up. The data used in this study were obtained from the Hospital‐based Cancer Registry.A total of 24 552 malignant tumor cases were collected in the Hospital‐based Cancer Registry from January 1 of 2007 to December 31 of 2017. The inclusion criteria comprised of patients with soft tissue sarcoma, and 687 cases (2.8%) were included. Exclusion criteria were as follows: gastrointestinal stromal tumors, tumors of unknown origin, and follow‐up periods of less than 1 month. These patients were analyzed to establish risk factors for hepatic metastases presenting various clinical features of soft tissue sarcoma.Based on the evaluation of radiology reports by a radiologist, the diagnosis of hepatic metastasis was determined by either a new appearance or gradually enlarging nodular shadow on CT and MRI imaging tests performed at our hospital. The direct invasion of primary tumor into the liver was not classified as hepatic metastasis. Potential risk factors for hepatic metastases included age at first visit, gender, histological type, site of occurrence for primary tumor, size, presence or absence of distant metastasis  and time of onset, follow‐up period, outcome at final follow‐up, 2‐year and 5‐year survival rates from first visit, and the 1‐year and 2‐year survival rates after indication of hepatic metastasis were evaluated. The site of occurrence of the primary tumor was divided into the following five groups: extremity, body wall, retroperitoneal, thoracic and peritoneal, and head and neck. According to a report by Jaques et al,P < .05 was considered significant.IBM SPSS Statistics for Windows, Version 25.0 was used for statistical analysis. Survival curves were generated by the Kaplan‐Meier method. For multivariate analysis, the hazard ratio and 95% confidence interval were calculated using the Cox proportional hazards model. 3Of 687 soft tissue sarcomas that were registered, 13 cases were gastrointestinal stromal tumors, 1 case was of an unknown primary site, and 15 cases were censored at less than 1 month after registration. A total of 658 cases remained after these exclusions, comprising of 323 males and 335 females with a median age at first visit of 65.0 years . The median follow‐up was 35.0 months . There were 128 patients who were diagnosed with sarcoma following primary tumor resection at another hospital, and these patients were referred to our hospital immediately after resection for additional treatment. In 32 patients who had inadequate resection at another hospital, an additional wide resection was performed at our hospital. A total of 434 surgical resections of the primary lesion were performed at our hospital, and 96 patients did not undergo surgery. Chemotherapy was performed in 156 patients, and radiotherapy was performed in 146 patients , followed by undifferentiated/unclassified sarcomas 170 cases) and leiomyosarcoma (69 cases). The site of occurrence was most common in the extremities (376 cases), followed by the body wall (124 cases) and retroperitoneum (74 cases). Details on histological type are described in Table 70 cases Table The presence of hepatic metastasis according to histological type is outlined in Table In terms of multivariate analysis, retroperitoneal sarcoma and leiomyosarcoma were significantly associated with the risk of hepatic metastasis at first metastasis and the development of hepatic metastasis at the last day of follow‐up in this study Table . Eight oThe 1‐year cumulative overall survival rate was 36.1%  after indication of hepatic metastasis, and the 2‐year cumulative overall survival rate was 21.7%   using a hospital‐based cancer registry at a single institution. Second, in soft tissue sarcomas that arise in the limb, screening for hepatic metastasis such as abdominal CT is not routinely performed and may underestimate the risk of hepatic metastasis. Routine abdominal imaging examinations were not performed in this study. Even if a patient underwent abdominal CT imaging, most were plain CT scans. Lastly, our hospital has only performed one surgical operation for hepatic metastasis, and analyses of treatment outcomes and cost‐effectiveness for the surgical treatment of hepatic metastasis of retroperitoneal sarcoma and leiomyosarcoma were not performed in this study and warrants further investigation.5Hepatic metastases of soft tissue sarcomas are relatively rare. The occurrence of hepatic metastasis indicates a poor prognosis. There was no hepatic metastasis from the head and neck. However, retroperitoneal sarcoma and leiomyosarcoma may cause early hepatic metastasis, and hepatic metastasis may be the first distant metastasis in some cases. For retroperitoneal sarcoma and leiomyosarcoma, additional screening for hepatic metastasis such as contrast CT should be considered during staging and follow‐up after treatment.6None.7None declared.Masanori Okamoto: Conceived and designed the study, curated the data, formally analyzed and interpreted the data, and wrote the original draft. Masatake Matsuoka: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Tamotsu Soma: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Ryuta Arai: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Hidenori Kato: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Toru Harabayashi: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Hirohumi Adachi: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Toshiki Shinohara: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Tamotsu Sagawa: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Noriaki Nishiyama: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Toshikazu Nambu: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Wataru Sakai: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Hiroaki Suzuki: Curated the data, formally analyzed and interpreted the data, and reviewed the article. Hiroyuki Kato: Reviewed and edited the article. Hiroaki Hiraga: Conceived and designed the study and reviewed and edited the article.Table S1Click here for additional data file.


C. elegans UNC-112 (kindlin) is required for muscle sarcomere assembly , and is part of a conserved four protein complex that associates with the cytoplasmic tail of integrin at the base of muscle integrin adhesion complexes . UNC-112 consists of 720 amino acids and contains FERM and PH domains. UNC-112 N-terminal half (1-396 aa) can bind to UNC-112 C-terminal half (397-720 aa), and this interaction is inhibited by the association of PAT-4  to the UNC-112 N-terminal half . The UNC-112 N-terminal half with either T346A or E349K mutations cannot bind to the UNC-112 C-terminal half, but can still bind to PAT-4 (ILK) . To elucidate the molecular mechanism by which the UNC-112 N- and C-terminal halves interact genetically, we identified suppressor mutations in the UNC-112 C-terminal half that restore the ability of the UNC-112 C-terminal half to bind to the UNC-112 N-terminal half with T346A or E349K. We introduced random mutations into the UNC-112 C-terminal half (by error-prone PCR), and assayed for binding to the UNC-112 N-terminal half with T346A or E349K. From screening using the yeast two hybrid system and DNA sequencing of suppressor clones, we identified 5 single amino acid changes; R633G, S644C, N659D, I662T, and R663Q. S644C and R663Q were identified from screening with the UNC-112 N-terminal half containing T346A, and R633G, N659D, and I662T were identified from screening with the UNC-112 N-terminal half containing E349K. The wild type and all 5 suppressor clones failed to activate growth with an empty prey vector. Our results are summarized in et al. 2012). Full length UNC-112 singly harboring the 5 suppressor mutations were tested for binding to the cytoplasmic tail of PAT-3. We found that 4 of 5 suppressors  could still bind to PAT-3, but full length UNC-112 with N659D could not bind , humans have three kindlins, each encoded by a separate gene . Alignment of nematode and human kindlins reveals that both of the residues mutated in the N-terminal half of UNC-112 that block its interaction with the C-terminal half are conserved in all three human kindlins. Of the five suppressor mutations, S644 is not conserved, but R633 and N659 are absolutely conserved, I662 is conserved except there is a T at this position in kindlin-1, and R663 is conserved except that there is a K at this position in kindlin-2. The only crystal structure available for a kindlin is for human kindlin-3 . Based on this structure, we created a homology model of UNC-112 and placed the mutations described in In contrast to et al. 2006; Qadota et al. 2012). Briefly, the UNC-112 N-terminal half was cloned into pGAD-C1 (prey plasmid), and the UNC-112 C-terminal half was cloned into pGBKT7 (bait plasmid). For random mutagenesis using PCR, two primers (TAATACGACTCACTATAGGGC and TAAGAGTCACTTTAAAATTTGTAT) were used. PCR amplified fragments of UNC-112 C-terminal half, digested pGBKT7, and pGAD-UNC-112 N-terminal half were introduced into yeast two hybrid host strain (PJ69-4A), then His+ colonies were selected. After checking for growth on Ade- plates, colonies containing pGBKT7-UNC-112 C-terminal fragment plasmids were grown in liquid media, and total yeast DNA was isolated, and transformed into XL1 Blue bacteria. LB + kanamycin plates were used to select for the growth of bacteria containing pGBKT7-UNC-112 C-terminal fragments; note that the pGAD-UNC-112 N-terminal half plasmids contains an ampicillin resistance marker. Seven such “suppressor clones” were isolated from the screening of UNC-112 N-terminal half containing the T346A mutation, and 12 suppressor clones were isolated from screening of UNC-112 N-terminal half containing the E349K mutation. DNA sequencing revealed that 10, among a total 19 clones, contained premature stop codons. All of these stop codons occurred near the N-terminus of UNC-112C and when translated would result in GAL4 DNA binding domains fused to only 14-25 amino acids from UNC-112C. Moreover, 4 of 10 of these showed non-specific growth (i.e. with an empty prey vector). For these reasons, we decided not to study them further. Among the remaining 9 clones, 3 clones contained single amino acid changes; S664C and R663Q from the screening of UNC-112 N-terminal with T346A, N659D from the screening of UNC-112 N-terminal half with E349K. The other 6 clones from screening with the UNC-112 N-terminal half containing E349K, had two or more mutations, three of which had R633G and three of which had I662T. We created UNC-112 C-terminal half with single mutations of R633G and I662T by site-directed mutagenesis, and then tested for binding to the UNC-112 N-terminal half with E349K. Since both mutations showed binding to UNC-112 N-terminal half with E349K, we concluded that R633G or I662T are responsible for suppression in the clones containing multiple mutations. For UNC-112 protein structure modeling, the SWISSMODEL  and Phyre2  online tools were used. Human kindlin-3  was used as reference crystal structure. Molecular graphics were generated by using Chimera .Random mutagenesis using PCR and screening for interactions using the yeast two hybrid method were performed as described previously (Miller


Non-coding RNAs (ncRNAs) participate in various biological processes, including regulating transcription and sustaining genome 3D organization. Here, we present a method termed Red-C that exploits proximity ligation to identify contacts with the genome for all RNA molecules present in the nucleus. Using Red-C, we uncovered the RNA–DNA interactome of human K562 cells and identified hundreds of ncRNAs enriched in active or repressed chromatin, including previously undescribed RNAs. Analysis of the RNA–DNA interactome also allowed us to trace the kinetics of messenger RNA production. Our data support the model of co-transcriptional intron splicing, but not the hypothesis of the circularization of actively transcribed genes. The vast majority of the eukaryotic genome is transcribed to produce a broad range of RNAs, including both protein-coding and non-coding RNAs (ncRNAs) . Early sAlong with lncRNAs, short (<200 nt) ncRNAs may also play a role in regulating gene expression at the transcriptional level. Thus, promoter-associated RNAs transcribed in both directions from the promoters of structural genes are likely to contribute to transcription activation . MicroRNA growing body of evidence implicates ncRNAs in spatial genome organization ,6,13. SeAll of the examples described above are likely only the tip of the iceberg. Diverse functions of ncRNAs are only beginning to be unraveled. Further progress in disclosing the functions of ncRNAs in gene regulation will depend on the availability of the genome-wide spectrum of RNA associations with chromosomes, the RNA–DNA interactome. The problem has been addressed in several recent studies . The pro® CCL-243™) were cultured in DMEM supplemented with 10% FBS and penicillin/streptomycin. Normal human skin fibroblasts  were kindly provided by Dr M. Lagarkova  and were cultured in DMEM supplemented with 10% FBS and penicillin/streptomycin. Human cells were grown at 37°C and 5% CO2 in a conventional humidified CO2 incubator. Drosophila melanogaster Schneider-2 (S2) cells were a kind gift of Dr O. Maksimenko  and were grown at 25°C in Schneider's Drosophila Medium supplemented with 10% FBS and penicillin/streptomycin.Human K562 cells  in full growth media for 10 min at room temperature followed by quenching with 125 mM glycine. Cells were washed with cold PBS and incubated in 375 μl lysis buffer , 37.5 U SUPERase-In RNase inhibitor (Invitrogene)) for 10 min on ice. To remove cytoplasm and extract RNA and proteins that were not cross-linked to DNA, permeabilized cells were resuspended in 250 μl nuclease-free water (Qiagen) followed by adding 7.5 μl 10% SDS and incubated for 30 min at 37°C with shaking at 1200 rpm. SDS was sequestered by adding 25 μl 20% Triton X-100 followed by incubation for 30 min at 37°C with shaking at 1200 rpm. After adding 100 μl warm 4× NEB buffer 4, nuclei were pelleted for 3 min at 2500 g and resuspended in 250 μl 1× NEB buffer 4. DNA was digested by adding 10 μl NlaIII  and incubated for 3Approximately 2.5 × 10Nuclei were immobilized on magnetic beads by mixing the suspension with 310 μl AMPure XP beads (Beckman Coulter) and incubating for 5 min at room temperature. Immobilization on beads helps to manipulate nuclei in downstream steps, in particular when using a small amount of starting material . It doesThe 3′ OH ends of RNA were ligated with 5′ rApp ends of a bridge adapter . For this purpose, bead-nuclei were resuspended in 190 μl RNA ligase solution (1× RNA ligase buffer (NEB), 4.5 μM bridge adapter, 20% PEG-8000 (NEB)), 10 μl T4 RNA ligase 2 truncated  was added, and the mixture was incubated for 6 h at room temperature then overnight at 16°C with shaking at 800 rpm. To wash off non-ligated bridge adapter, bead-nuclei were pelleted, resuspended in 200 μl nuclease-free water and mixed with 165 μl AMPure buffer  . Bead-nuTo reverse formaldehyde cross-links and digest proteins, bead-nuclei were resuspended in 235 μl proteinase K solution , 15 μl proteinase K  was added, and incubation for 1 h at 55°C and then for 2 h at 65°C followed. To precipitate RNA–DNA chimeras, 1.5 μl GlycoBlue (Thermo Scientific), 25 μl 3M NaAC and 275 μl isopropanol were added and, after overnight incubation at −80°C, the mixture was centrifuged for 30 min at 16 100 g and 4°C. The pellet was resuspended in 50 μl nuclease-free water, and RNA–DNA chimeras were further purified with 2 volumes of AMPure XP beads and finally eluted into 50 μl nuclease-free water followed by measuring the concentration with a Qubit dsDNA broad range kit. For the control experiment with RNase A treatment, RNA–DNA chimeras (3.5 μg) were incubated with 0.4 μl RNase A  in water for 30 min at 37°C, followed by clean up with 2 volumes of AMPure XP beads.RNA–DNA chimeras (3.5 μg) were digested with MmeI in 100 μl reaction containing 1 × NEB buffer 4, 0.1 mg/mL BSA (NEB), 80 μM SAM (NEB), 0.1 μM ds oligo with MmeI site (a duplex of 5′-CTGTCCGTTCCGACTACCCTCCCGAC-3′ and 5′-GTCGGGAGGGTAGTCGGAACGGACAG-3′), and 4 U MmeI (NEB) for 2 h at 37°C. Short dsDNA containing the MmeI site is added to stimulate the cleavage of DNA molecules containing a single MmeI site .After MmeI digestion, RNA–DNA chimeras were subjected to biotin pull-down. For this process, 10 μl of Dynabeads MyOne Streptavidin C1 beads  was washed twice with 400 μl tween washing buffer (TWB)  by repeating the resuspension/magnet separation. Streptavidin beads were resuspended in 100 μl 2 × binding buffer  and mixed with the solution after MmeI digestion followed by incubation for 15 min at room temperature to bind biotinylated bridge to streptavidin beads. Streptavidin beads with tethered RNA–DNA chimeras were washed twice with 600 μl TWB, once with 100 μl 1 × NEB buffer 2, once with 50 μl 1 × First-Strand Buffer (Clontech) and resuspended in 38 μl reverse transcriptase solution (1 × First-Strand Buffer (Clontech), 2.5 mM DTT (Clontech), 1 mM each dNTP, 1 μM switch template oligo , 20 U SUPERase-In RNase inhibitor (Invitrogene)). After pre-heating at 42°C for 2 min, reverse transcription was initiated from the bridge 3′ OH by adding 2 μl SMARTScribe Reverse Transcriptase  and incubating for 1 h at 42°C with shaking at 800 rpm. Reverse transcriptase first transcribes bridge DNA, then DNA–RNA junction, then RNA. Upon reaching the 5′ end of the RNA, reverse transcriptase adds a few non-template nucleotides (predominantly dC) to the 3′ end of cDNA. This dC stretch pairs with rGrGrG of the switch template oligo, and reverse transcriptase continues replication using the switch template oligo as a template  Supplem. AtypicaAfter cDNA synthesis, streptavidin beads were washed twice with 600 μl TWB, once with 100 μl 1 × NEB buffer 2, once with 100 μl 1 × T4 DNA ligase buffer (Thermo Scientific) and resuspended in 48 μl DNA ligase solution (1 × rapid ligation buffer (Thermo Scientific), 3 μM NN-adapter (a duplex of 5′-AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT-3′ and 5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTNN-3′ where N designates any base). To ligate DNA NN ends produced by MmeI digestion to adapter NN ends, 2 μl T4 DNA ligase  was added followed by incubation for 1 h at room temperature. The NN-adaptor is used in a non-phosphorylated form to avoid adaptor-to-adaptor ligation. As a result, a nick is left in the non-biotinylated strand see . After lDNA-cDNA chimeras were amplified in 50 μl PCR containing 1 × KAPA HiFi Fidelity Buffer, 0.3 mM each dNTP, 0.5 μM universal primer (5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGA-3′), 0.5 μM indexed primer (5′-CAAGCAGAAGACGGCATACGAGATNNNNNNGTGACTGGAGTTCAGACGTGTGC-3′ where NNNNNN is a sequencing index), 1 U KAPA HiFi DNA Polymerase, and 4 μl streptavidin beads from the above step. PCR was performed as follows: 95°C 5 min, 12–14 cycles of 98°C 20 s, 65°C 15 s, 72°C 45 s, 72°C 3 min. PCR products of 4 reactions were pooled, purified with 1.8 volumes of AMPure XP beads and separated in a 2% agarose gel. PCR products were excised from the gel and purified using a QIAquick Gel Extraction Kit (Qiagen). Purified PCR products were paired-end sequenced on the HiSeq 2500 or MiSeq Illumina platform with the read length of 80–133 nt.In the case of fibroblasts, we used 0.3 μg RNA–DNA chimeras for MmeI digestion and 17 cycles of PCR for final amplification. All oligos were purchased from Integrated DNA Technologies, Inc. Certified RNase-free reagents and materials were used.The raw Red-C reads were processed using a computational pipeline outlined in For sequencing quality control, we ran FASTQC and found a decline in sequencing quality at the end of reads. We used TRIMMOMATIC for the detection of the first leftmost low-quality position in each forward and reverse read. The parameters were set to: window size: 5, quality threshold: 26. Only reads with at least one nucleotide passing the quality control filter were selected.Each read, regardless of quality filter, was subjected to the scanning of adaptors, bridge, and GGG/CCC oligonucleotides. The scanning was done with Rabin–Karp algorithm implementation in C. For that, sequences from FASTQ files with reads and FASTA files with oligonucleotides were first converted to binary indexed files; then, the search was run. For each read, the positions of start and end of oligonucleotides were reported. First, R1 was scanned for a complete bridge (AGTCGGAGCGTTGCCTATCGCATTGATGGTGCTAGGA). The bridge was allowed to have one mismatch in any position except the rightmost GA and to be positioned anywhere in R1. If a complete bridge was identified in R1, the read pair was retained. Second, only read pairs with an R2 read starting with GGG were retained. Third, R1 was scanned for STO (CCCAGATCGGAAGA was required) with allowing one mismatch. If identified, R1 was trimmed right in front of STO. To trim shorter pieces of STO that could occur at the end of R1, we took 14 nucleotides adjoining GGG in the start of R2, converted to reverse complement, and performed scanning of R1 for the rightmost position of complementarity. If identified, the region of R1 to the right of the position of complementarity was trimmed. Fourth, R2 was scanned for the bridge (TCCTAGCACCATCA was required) with allowing one mismatch. If identified, R2 was trimmed right in front of the bridge. To trim shorter pieces of a bridge that could happen at the end of R2, we took 14 nucleotides located to the right of the bridge in R1, converted to reverse complement, and performed scanning of R2 for the rightmost position of complementarity. If identified, the region of R2 to the right of the position of complementarity was trimmed. Finally, we extracted the DNA part as the region of R1 to the left of the bridge, the RNA 3′ part as the region of R1 to the right of the bridge, and the RNA 5′ part as the region of R2 to the right of the first GGG. Note that RNA 3′ and RNA 5′ parts can partly or completely overlap if the RNA portion of the chimera is short. If the lengths of DNA, RNA 3′, and RNA 5′ parts were more than 0, these sequences were written to separate FASTQ files with corresponding qualities from initial files.Most DNA parts are 18–20 nucleotides long , with thDNA parts of 18–20 nucleotides, RNA 3′ parts of ≥14 nucleotides, and RNA 5′ parts of ≥14 nucleotides were independently mapped to the hg19 genome with the hisat2 program. Before mapping, the end of the DNA part adjoining the bridge was supplemented with CATG  was used instead of the most recent hg38 because some of epigenetic tracks important for our analysis, such as chromatin states, chromatin compartments, annotation of vlinc RNA, fRIP-Seq data, are not available for hg38. To allow comparison of our data with the chromatin features annotated in hg38, we performed additional mapping of RNA and DNA parts to the hg38 genome and disclosed TSV files with contacts for hg38 assembly along with TSV files for hg19 assembly (see Data availability section).et al. ; basic gene annotation); piRNAs annotation from piRNABank; tRNAs annotation from UCSC ; set of rRNAs, snRNAs, scRNAs, tRNAs, RNAs, srpRNAs from UCSC ; vlinc from Laurent et al. . In caseet al. . If the Clusters of RNA parts that were not assigned to any gene may potentially represent novel chromatin-associated RNAs (designated X-RNAs). To identify X-RNAs, we search for clusters comprising at least 100 non-assigned RNA 3′ parts mapped to the same strand, with a distance between consecutive RNA parts of no more than 100 bp. If a known gene is detected at a distance of less than 100 bp of the cluster boundaries that is covered by more RNA parts than there are in the cluster, the cluster is discarded because it may represent a ‘tail’ of this gene. Clusters spaced less than 1 Kb apart at the same strand are further aggregated into one cluster to compensate for coverage gaps. The procedure yields 1867 X-RNAs in K562 cells .eRNAs are arbitrarily defined as RNAs produced from an enhancer-specific chromatin type  . Each getrans-contacts with each genomic site, as suggested by Li et al. , we sum the number of background normalized contacts this RNA establishes with the region and divide by the total length of the region. In our analysis, we discard contacts with genomic regions annotated by chromatin types 14 and 15 and not annotated by any chromatin type. If such regions occur within the region of interest, we subtract the length of these regions from the total length of the region.et al. ; 0–500 kb upstream and downstream of gene boundaries ; 500 kb–5 Mb upstream and downstream of gene boundaries ; >5 Mb from gene boundaries in the same chromosome ; and finally the other chromosomes   and 3′ end (the end adjoining bridge): (i) both 5′ and 3′ ends are within the same exon; (ii) both 5′ and 3′ ends are within the same intron; (iii) the 5′ end is within an exon and the 3′ end is within the next intron or the 5′ end is within an intron and the 3′ end is within the next exon (exon-intron junction) and (iv) the 5′ end is within an exon and the 3′ end is within the next exon, with a splicing junction reported within the RNA part (exon-exon junction). We also discriminate RNA parts representing different portions of mRNA, such as the first or last exon/intron, or a particular bin (RNA parts are assigned to a bin based on the position of the RNA 3′ end). Of note, GRID-seq, which operates with short RNA parts of a fixed length (20–21 nt), is less sensitive in detecting exon-intron and exon-exon junctions compared to Red-C .In the analysis of intra-gene contacts, we select protein-coding mRNAs that establish at least 1 contact with its own gene or gene flanking regions of half gene length . We divide corresponding genomic regions into 24 bins (12 bins for gene body ± 6 bins for flanks). Note that bin length varies depending on gene length. For each mRNA, we consider RNA parts of a given type  and determine the number of background normalized contacts these RNA parts establish with each genomic bin. Finally, we average contacts for genomic bins located in the same position relative to the gene body for all mRNAs.For calculating the scaling of contact probability of exons of mRNAs with regions of the chromosome bearing the encoding gene, we select a set of RNA–DNA contacts such that the 3′ end of the RNA part is mapped within an exon of a protein-coding gene and the DNA part is mapped anywhere in the genome. We divide the genome into 100 kb bins and select bins to which one or more RNA parts are mapped. We then consider DNA parts mated to RNA parts of a given bin and calculate how many of these DNA parts are mapped to each consecutive genomic bin , thus yielding the number of contacts RNA of a given bin establishes throughout the genome. Finally, the contact numbers are averaged among pairs of bins equally spaced in a linear chromosome, and obtained values are normalized to the total number of contacts in the set, including both cis and trans-contacts. Note that the number of bin pairs decreases with an increase in distance between bins because short distances are assessed from both short and long chromosomes, whereas long distances are assessed only from long chromosomes.Scaling of contact probabilities for introns of mRNAs is calculated in the same way with the only difference being that we select a set of RNA–DNA contacts such that the 3′ end of the RNA part is mapped within an intron of a protein-coding gene.To calculate the scaling of DNA–DNA contacts, we used a publicly available Hi-C data set for K562 . We requ6 single-end reads. Reads were mapped and annotated by genes in the same way as for the RNA 3′ parts of the RNA–DNA chimeras (see above).Total cellular RNA was isolated from K562 cells using an RNeasy Mini Kit (Qiagen) followed by the removal of ribosomal RNA using a Ribo-Zero Gold rRNA Removal Kit (Human/Mouse/Rat) (Illumina). Strand-specific sequencing libraries were prepared using a NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB). Libraries from two biological replicates were sequenced on the Illumina NextSeq 500 platform resulting in 22–25 × 10RNA ends on DNA capture) experimental procedure for mapping the RNA–DNA interactome is based on adapter-mediated RNA–DNA ligation in fixed nuclei followed by high-throughput sequencing of the chimeric RNA–DNA molecules , allowis Figure . SequencThe main difference between Red-C and similar protocols MARGI ,34, GRID, GRID34,R > 0.94). The same is true for contact numbers for individual RNAs (R = 0.96).We applied the Red-C protocol to uncover the RNA–DNA interactome of the cultured human erythroleukemia cells (line K562). In two biological replicates, we identified 44M unique RNA–DNA contacts see . This seThe highest number of contacts was observed for mRNAs (31M) and linc and vlinc RNAs ; 0–50 Kb upstream and downstream from gene boundaries (S); 50–500 kb upstream and downstream from gene boundaries (M); 500 kb–5 Mb upstream and downstream from gene boundaries (L) and >5 Mb from gene boundaries in the same chromosome (R) Figure . We thenP-value < 0.0001, Figure Genomic distribution of an RNA is an important characteristic that may shed light on its potential function. Indeed, RNAs involved in splicing demonstrate a wide spectrum of contacts along all chromosomes, while XIST is spread specifically along the X chromosome. To expand this type of analysis for all RNAs, we developed an algorithm for identification of RNAs with specific genome distribution patterns based on the comparison of contact frequencies between the intervals described above: S+M and L, L and R, and R and T . Here, we define eRNAs as RNAs transcribed from enhancer-specific chromatin states . For eacWe further analyzed spliceosomal U snRNAs from the group of RNAs with genome-wide distribution pattern . In agreement with previous observations , U RNAs Also of interest are MIR3648 and MIR3687 derived from the upstream part of pre-rRNA . These met al.  representing fragments of spliced or unspliced transcripts. We grouped RNA parts of chimeric molecules of each type according to their parental chromosome and determined how frequently their respective DNA parts are mapped to the same or other chromosomes. The spatial proximity of any part of mRNA with a remote genomic region may occur during transcription and reflect the spatial proximity between the gene encoding for this mRNA and the remote genomic region. In another scenario, the RNA may diffuse to a remote genomic region after release from a transcription complex. To discriminate between these possibilities, we compared our Red-C data with Hi-C data for K562 cells produced by Rao et al. . With thet al. . The reset al. . It becaet al. . Accordiet al. . Hence, et al. . Also ofet al. . Finallyet al. . These rWe next considered the contacts of mRNAs along the gene body. First, we determined frequencies with which mRNA fragments interact with genomic regions upstream and downstream of the site from where the fragment was transcribed, with respect to the direction of mRNA transcription. As expected, the highest interaction frequency was observed over the transcription site  across all mRNAs. We started by examining the contacts of the first exon and intron with the downstream regions of the gene. The contact frequency of the first exon remains quite high until the transcription end site (TES) and sharply decreases beyond the TES Figure . The firThe conclusion about co-transcriptional intron splicing was confirmed when consecutive segments of mRNAs from the 5′ to 3′ end were examined . RemarkaSurprisingly, our data do not support a popular hypothesis about gene circularization aiming to facilitate transcription re-initiation . If loopP-value < 2.2e–16). We also observed that RNAs with the highest propensity to bind chromatin  frequently interact with Polycomb proteins , histone acetylase/deacetylase (CBP/HDAC1), and other proteins involved in the control of chromatin folding and dynamics . A significant number of chromatin-associated RNAs have contacts with the RNA editing protein ADAR  from . This stein ADAR .It is becoming increasingly evident that various non-coding RNAs play important roles in nuclear organization, chromatin architecture, and regulation of gene expression ,47–50. SUsing the Red-C procedure, we identified a number of presently unknown sense and antisense RNAs interacting with DNA in the vicinity of structural genes as well as ncRNAs preferentially associated with specific chromatin types. The entirety of the data obtained is yet to be fully explored. Here, we began this analysis by partitioning chromatin-bound RNAs into groups according to their genomic distribution relative to their parental transcription unit. This kind of analysis Figure  allowed An interesting observation made in our study is that eRNA transcribed from strong enhancers interacts with other strong enhancers, but not with promoters. This may signify that enhancers are assembled in spatial clusters even when they do not interact with promoters or interact with different promoters transiently. Another option is that eRNA is involved in establishing communication between enhancers. This supposition certainly deserves further investigation.The results of our analysis allowed the tracing of the dynamics of structural gene transcription and splicing for the first time. Our results strongly support the model of co-transcriptional splicing  and thusAll datasets reported in this paper are available at the Gene Expression Omnibus with accession numbers GEO: GSE136141.Datasets with raw fastq Red-C and RNA-Seq data and processed TSV files with contacts are available under GEO accession: GSE136141.https://github.com/agalitsyna/RedClib.The code for read processing is available as RedClib on github: gkaa457_Supplemental_FilesClick here for additional data file.


Background: Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m5C) methyltransferases have recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m5C methyltransferases in glioma remains unclear. This study investigated RNA: m5C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas.Methods: We obtained the RNA-sequence and Clinicopathological data of RNA:m5C methyltransferases underlying gliomas from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets. We analyzed the expression of RNA:m5C methyltransferase genes in gliomas with different clinicopathological characteristics and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk signature. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors.Results: We revealed the differential expression of RNA:m5C methyltransferase genes in gliomas with different clinicopathological features. Consensus clustering of RNA:m5C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, functional annotations demonstrated that RNA:m5C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m5C methyltransferase genes were screened to construct a risk signature that can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome.Conclusion: We demonstrated the prognostic role of RNA:m5C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy for gliomas. The dynsine (I) , 11. AmoA is m5C . This poing RNAs –15. m5C  for m5C . The cel5C methyltransferases and the modifications they induce have revealed their importance in several aspects of protein biosynthesis, cell proliferation, and differentiation, as well as on mitochondrial and nuclear gene expression , NSUN2, NSUN3, NSUN4, NSUN5, NSUN6, and NSUN7. The biological function of these RNA:mpression , 20, 21.d cervix , 21–24.6A) RNA methylation, especially its regulatory enzymes , have also emerged as important epigenetic mechanisms for the progression and malignancy of glioma. To date, no literature has reported the relationship between aberrant expression of these RNA:m5C methyltransferases and clinicopathological features, as well as the prognostic value of these methyltransferases with respect to gliomas. It may be useful to study the biological role of these enzymes as they have potential as therapeutic targets for the treatment of glioma.Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors, and is notorious for its high recurrence rate and resistance to treatment , 25, 26.5C methyltransferases, using the RNA sequencing data from the CGGA (n = 306) and TCGA (n = 616) datasets and aimed to investigate its prognostic value in glioma. We demonstrated the association between RNA and glioma malignant progression and constructed an RNA:m5C methyltransferase-related signature to evaluate the patients with glioma. Surprisingly, this gene signature can effectively predict the malignancy and prognosis of glioma patients.In this study, we comprehensively studied the expression profiles of the NOL1/NOP2/sun domain RNA methyltransferase family, which are RNA:mhttp://www.cgga.org.cn/) as the training set. Similarly, the TCGA RNA sequencing data [fragments per kilobase of transcript per million mapped reads (FPKM)] and clinical information were downloaded from the TCGA data portal (https://www.cancergenome.nih.gov/) and used as a verification set. Moreover, the somatic mutation data from glioma patients were also downloaded from the TCGA data portal as MAF files. After the removal of samples with missing data on survival and WHO grade, we obtained 306 (CGGA dataset) and 616 (TCGA dataset) glioma patients. Thereafter, the CGGA and TCGA RNA sequencing data were normalized with Expectation-Maximization algorithm (log2 transformation) for the subsequent analysis. The clinical information for the CGGA and TCGA datasets was listed in 5C methyltransferases were screened according to the published literature (The CGGA RNA sequencing data [per kilobase of transcript per million mapped reads (RSEM)] and the relevant clinical information were downloaded from the CGGA data portal , 10 low-grade glioma samples (WHO grade II), and 6 non-neoplastic brain tissues (NBTs) were used as a control collected from the surgery of intractable epilepsy cases. The study was approved by the medical ethics committee of the Second Affiliated Hospital of Nanchang University and was performed in accordance with the approved guidelines. Informed consents were acquired from each glioma patient. The tumor tissues were immediately frozen and stored at −80°C.5C methyltransferases-related genes, we identified three subtypes based on the RNA:m5C methyltransferase expression profiles of 306 patients with gliomas from the CGGA dataset using the R package “ConsensusClusterPlus”  (To explore the function of gliomas' RNA:mor.org/) . The appor.org/) .5C methyltransferases were predicted using the String website (https://string-db.org/) and the R package “corrplot.” Gene Ontology (GO) analysis was performed with Metascape (http://metascape.org/) to annotate the function of differentially expressed genes in the different subtypes. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was performed using Database for Annotation, Visualization and Integrated Discovery  and visualized in the imageGP website (http://www.ehbio.com/ImageGP/). Gene set enrichment analysis  was performed using the JAVA program and downloaded from the website (http://software.broadinstitute.org/gsea/). The hallmark gene set  was also downloaded. Thereafter, the hallmark gene set was determined to be significantly enriched following normalization (p < 0.05) and a false discovery rate (FDR < 0.25).Function and interaction of the seven RNA:mP < 0.05). Thereafter, six RNA:m5C methyltransferases  were screened with the LASSO multivariate Cox regression algorithm using the R package “glmnet” (version 3.0). Finally, the signature genes and coefficients in the risk score signature were constructed based on the most suitable penalty parameter λ. The risk score formula we used was:Univariate Cox regression analysis was performed to identify the genes significantly related to survival  was constructed in the CGGA dataset, and evaluated with the TCGA dataset. Patients were ranked into high-risk and low-risk groups using the median risk score. Moreover, the genomic alterations of these glioma patients were analyzed using the R package “Maftools” (where aftools” .5C methyltransferases were downloaded from The Human Protein Atlas (https://www.proteinatlas.org/). Staining intensity, quantity and patients' information can be inquired about online.The immunohistochemistry pathological specimen results from the prognostic RNA:m5C methyltransferases in glioma patients we used trizol reagent , extracted total RNA from brain tissues and the total RNA (2 μg) was reverse transcribed into cDNA, according to manufacturer's instructions. The qRT-PCR was performed on The LightCycler® 480 Real-Time PCR System. The expression level of genes was analyzed and normalized to GAPDH and the 2−ΔΔCT method was performed to evaluate the fold change in gene expression. The following primer sequences : NOP2 forward 5′-AAATGGGAGAAGGTGGCGTC-3′, and reverse 5′-CTCTCGGACATTAACCCGCA-3′; NSUN4 forward 5′-ATGTGCCCTGTACCACAGAC-3′, and reverse 5′-GGTAAGAGCTTCCAGTCCCC-3′; NSUN5 forward 5′-TGCTTCTCAGAGCCAACGAA-3′, and reverse 5′-CTTCCAGACTGTCTCACTGCAT-3′; NSUN6 forward 5′-CTCGGTGACAGGGAGGTCTA-3′, and reverse 5′-AGGCATCTCTCATGGTGAGG-3′; NSUN7 forward 5′-TCAGCTCCAGATTGCGATTC-3′, and reverse 5′-CCCTCGTGCCTTAGGATCAC-3′.To assess the mRNA expression levels of RNA:m5C methyltransferases in gliomas with different WHO grades and IDH status. The t-tests were performed to contrast the expression levels in gliomas for clinicopathological features. A chi-square test was conducted to compare the distribution of clinical and molecular pathological features between the different groups stratified by risk scores. Univariate, multivariate, LASSO Cox regression and Kaplan-Meier analyses were performed to construct and evaluate the risk signature using the R packages “glmnet” and “survival” (https://www.r-project.org/) and SPSS (version 26). P < 0.05 was considered statistically significant.The One-way ANOVA and R packages “limma” were used to compare the expression level of RNA:murvival” , 31. Roc5C modifications of RNA play an important role in the occurrence and progression of tumors and influence many biological functions. We comprehensively studied the relationship between each RNA:m5C methyltransferase and the clinicopathological characteristics of gliomas, for example, WHO grades and isocitrate dehydrogenase (IDH)-mutant status. The relationship between aberrant expression of RNA:m5C methyltransferases and WHO grades are shown using heatmaps , and the prognostic value of the mutation has been confirmed by many authors in the literature. Based on these findings, we investigated the relationship between aberrant expression of RNA:m5C methyltransferases and IDH-mutant status in LGGs. The results showed different expression of NSUN3, NSUN4, NSUN5, NSUN6, and NSUN7 between IDH-mutant and IDH-wildtype status in both CGGA  stratified by IDH mutant status, and findings indicated that NSUN5, NSUN6, and NSUN7 were still differentially expressed , IDH-wildtype status (P < 0.0001), 1p/19q-noncodel status (P < 0.0001), older average age at diagnosis (P < 0.0001), and receipt of additional chemotherapy (P < 0.01) (P < 0.0001). The survival of patients who fell into the MC2 subtype was obviously shorter than for the other two subtypes  . Moreovesubtypes . Moreovesubtypes . The resP < 0.01 and FDR = 0.05) and 645 genes were down-regulated  in MC2 relative to MC1 and MC3 subtypes. Go analysis of the up-regulated genes revealed that “extracellular matrix organization,” “vasculature development,” “epithelial cell proliferation,” “cell-substrate adhesion,” and “cellular response to tumor necrosis factor” were enriched in biological processes and pathways which might be highly correlated with malignant progression of gliomas. The top 20 significantly enriched biological processes were shown in the figure , P13K/AKT/mTOR signaling , DNA repair , and MTORC1 signaling   was performed to investigate the hallmarks of tumors in the MC2 subtype. The results indicated that the tumor hallmarks, such as P53 pathway   were enr5C methyltransferases and the malignant progression of gliomas, we further attempted to explore the prognostic role of RNA:m5C methyltransferases in gliomas by a univariate survival analysis using Cox proportional hazards models of expression levels in the CGGA dataset, which was set up as a training dataset. We obtained six genes associated with prognosis (P < 0.01), among which NOP2, NSUN2, NSUN4, NSUN5, and NSUN7 acted as risk factors (HR > 1), and NSUN6 played a protective role (HR < 1) in gliomas  Cox regression algorithm in the CGGA dataset (5C methyltransferase genes and coefficients (5C methyltransferase genes were significantly correlated with OS (P < 0.0001) (Based on the relationship between RNA:m gliomas . To impr dataset . Five geficients  were scr 0.0001) .P < 0.0001) between the high-risk group and low-risk group  and IDH-mutant status subtypes , histology (P < 0.0001), IDH status (P < 0.0001), 1p/19q status (P < 0.0001), age (P < 0.0001), and receipt of additional chemotherapy (P < 0.0001) , RB pathway , and P13K/RTK pathway , we obtained the somatic mutation data from the TCGA database (The expression of the five screened RNA:mheatmaps . We foun 0.0001) . Thereaf 0.0001)  compartm dataset . The resdatabase , 33. Thedatabase . The higIDH status, 1p/19q codel status, chemotherapy status, and radiotherapy status were significantly correlated with prognosis using the univariate analysis , we identified three subtypes with different clinicopathological characteristics and prognoses. Moreover, the subtypes were closely correlated with tumor-related hallmarks, biological processes, and signaling pathways. Based on the features of RNA:m5C methyltransferases, we constructed a related risk score algorithm that divided glioma patients into high- and low-risk groups to precisely predict clinical outcomes of glioma patients. Furthermore, univariate and multivariate Cox regression analyses were performed to demonstrate that it was an independent prognostic factor for glioma patients, in addition to the WHO grade and IDH-mutant status as well as age at diagnosis. The signature-based on five RNA:m5C methyltransferases can serve as a potent prognostic signature and effectively stratify glioma patients based on risk scores and provide new insight into targeted therapy.The important role played by aberrant RNA epigenetic modifications in tumorigenesis and tumor progression, as well as patient prognosis, has been increasingly demonstrated. This suggests that epigenetic regulators have a potential application in glioma diagnosis and prognosis. In the past, extensive literature reports involving glioma and 5-methylcytosine have focused on DNA methylation, which could serve as ideal biomarkers for cancer diagnosis –36. In t5C methyltransferases, several genes have been reported to be involved in tumor progression across malignancies. According to the latest literature, NSUN5 plays an important role in ribosomal RNA cellular transformation and protein translation. Furthermore, NSUN5 epigenetic inactivation was associated with a better prognosis for glioma patients  and a verification set (TCGA), respectively, and found their prognostic value in predicting OS. The results of subsequent multivariate Cox regression analyses eliminated four variables other than grade and age, although among which IDH had been proved to be an effective prognostic indicator .Publicly available datasets were analyzed in this study. This data can be found here: (The studies involving human participants were reviewed and approved by the medical ethics committee of the Second Affiliated Hospital of Nanchang University. The patients/participants provided their written informed consent to participate in this study.PW and KH designed the research. PW, KH, CT, and KL contributed to the data collection and analysis, figures and tables, and were involved in manuscript writing. ZT and PW contributed to the conducted the experiments. XZ and KH performed the correction of the language and revision. All authors proofread and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Antisense DNA oligonucleotides, short interfering RNAs (siRNAs), and CRISPR/Cas9 genetic tools are the most useful therapeutic nucleic acids regulating gene expression based on the antisense specificity towards messenger RNA. Here, we present an effective novel strategy for inhibiting translation based on the antisense-controlled formation of an RNA quadruplex-duplex hybrid (QDH) between a G-rich RNA antisense oligoribonucleotide (Q-ASO) and specific mRNA, comprising two distant G-tracts. We selected epidermal growth factor receptor (EGFR) as a well-established target protein in anticancer therapy. The chemically modified, bi-functional anti-EGFR Q-ASO and a 56-nt long EGFR mRNA fragment, in the presence of potassium ions, were shown to form in vitro very stable parallel G-quadruplex containing a 28-nt long external loop folding to two duplex-stem structure. Besides, the Q-ASOs effectively reduced EGFR mRNA levels compared to the non-modified RNA and DNA antisense oligonucleotides . In addition, the hybridization specificity of Q-ASO comprising a covalently attached fluorescent tag was confirmed in living cells by visualization of the G4 green fluorescent species in the presence of other antisense inhibitors under competitive conditions. The results presented here offer novel insights into the potential application of Q-ASOs for the detection and/or alteration of (patho)biological processes through RNA:RNA quadruplex-duplex formation in cellular systems. QF-ASO, QL-ASO, Q-ASO-NH2, and EGFR oligonucleotides were desalted using Amicon® Ultra 3K centrifugal filters  by loading on the filter, washing with 4000 µL MilliQ water and then washing successively against ∼150 mM LiCl and against water. The purity and homogeneity of Q-ASO and EGFR mRNA oligomers were verified by 15% denaturing gel electrophoresis  dissolved in sodium phosphate buffer  was combined with 5-fold molar excess of in house made oBMVC-C3-NHS ester in DMSO  or  were suspended in 4 µL water and 4 µL of 8 M urea . Next, samples were heated at 95 °C for 3 min, cooled to room temperature, and loaded on 15% TBE-Urea gels . Denaturing gel electrophoresis experiments were performed in 1.5× TBE buffer. Electrophoresis experiments were run at 180 V for 2 h and gels were viewed by UV shadowing .N-methyl mesoporphyrin IX staining  as previously described   (MTT) assay. HeLa cells were subjected to Lipofectamine-mediated transfection with the tested ASOs (50–200 nM), and mitochondrial activity was determined after 48 h of incubation. As shown in The silencing activity of the most active QL-ASO antisense oligonucleotide was monitored by imaging HeLa cells in the EGFP-EGFR/RFP double fluorescence model. In a control experiment, HeLa cells were subjected to Lipofectamine-mediated transfection with EGFP-EGFR/RFP plasmids and then with the control dASO-C oligomer (100 mM), while the remaining cells were transfected with antisense QL-ASO . Living cells were imagined using fluorescence microscopy measurements. As shown in in vitro experiments demonstrated slower growth of MCF-7 tumor cells relative to HeLa cells [A characteristic feature of cancer cells is an increased number of epidermal growth factor receptors (EGFR) on the outer cell membranes and in cellular organelles. These receptors are important for maintaining uncontrolled growth of cancerous tumors and metastasis to other organs, and their quantity correlates well with the aggressiveness of cancer cells and their resistance to chemo- and radiotherapy. In our tests, MCF-7 (breast cancer), HeLa , and A431 (epidermoid carcinoma) cancer cells with different densities of EGF receptors were used. Levels of the EGFR protein in MCF-7 cells were reportedly the lowest compared to HeLa and A431 cells ,46. The La cells ,48. In cLa cells ,50. TherTo determine whether levels of EGFR mRNA in the three tested cell lines correlated with the reported data, we decided to visualize the G-quadruplex domains of the hybrid structures using an anti-EGFR-mRNA oligonucleotide conjugated to a fluorescent probe Fl (Fl-Q-ASO). At the 3′-end, this Fl-Q-ASO contains 3,6-bis(1-methyl-4-vinylpyridinium) carbazole diiodide substituted with propionic acid at N9  were treated with ER-Tracker Red to stain the endoplasmic reticulum (ER) and with DAPI (to stain the dsDNA in the nucleus) and were examined by fluorescence microscopy. Formation of the Fl-Q-ASO/EGFR hybrid labeled with the oBMVC-C3 fluorescent probe was confirmed by non-denaturing PAGE  and by mex = is 358 nm, green fluorescence mode, λex = 458/500 nm; red fluorescence mode, λex = 587 nm)  D. Intere 587 nm) C, suggesIn line with our hypothesis, the oBMVC-C3-linker-Q-ASO binds strongly to EGFR mRNA and forms a hybrid structure comprising the bimolecular G-quadruplex, which was visualized by the fluorescent indicator present in close vicinity . The preIn summary, we demonstrated that Fl-Q-ASO exerts highly sequence-specific hybridization to the target mRNA, and the formation of the quadruplex-duplex hybrids results in inactivation of mRNA and inhibition of protein synthesis, as shown by the DFA assay . Thus, tin vitro [Recent studies have identified 80,307 stem-loop-containing G-quadruplex sequences (SLQS) within the human genome, including 23,742 in pre-mRNAs and 2429 in mature mRNAs . These fin vitro ,51. Eachin vitro ,53. G-quin vitro ,53. In tin vitro . A specianti conformation of G residue which is the preferred conformation of parallel RNA G-quadruplexes. The presence of two GFFG-tracts in QF-ASO led to the formation of the QF-ASO/EGFR hybrid with the parallel G-quadruplex domain. In this G-quadruplex the two inner layers are composed of FFGG-tetrads. The second hybrid (QL-ASO/EGFR) comprises an unmodified G-core with abasic modifications and aliphatic linkers located in the junction region and in the external loop . A targeted sequence contains two important fragments, one complementary to a region of the Q-ASO oligonucleotide and another comprising two four-guanosine tracts separated by 28 nucleotides. After hybridization with Q-ASO, formation of quadruplex-duplex hybrid structures was confirmed in vitro and in living cells. We demonstrated that the use of RNA Q-ASO led to formation of bimolecular RNA-RNA G4 structures on mRNA templates with enhanced gene silencing ability compared to rASO and dASO. In this work, we introduced chemical modification in the G-quadruplex segment only and not duplex. The comparison of unmodified control antisense oligonucleotides  and Q-ASO modified in the G-quadruplex segment allows to better assess the impact of the selected modifications on the functionality of the designed bifunctional antisense tool. We indicated the advantage of using non-nucleotide modifications, especially such as abasic analogs between Q and ASO sequences or aliphatic linker between G-tracts. We postulate that the more flexible -aaa- chain and the longer linker S6 between G-tracts allows the QL-ASO for easier spatial accommodation of the G-strands to form the G-quadruplex in comparison to QF-ASO, containing corresponding -AAA- and -UC- linkers, instead . These mRNA ASOs ,63.in vivo, depending on cellular conditions, the presence of binding proteins, helicases, stress conditions, or the cell cycle phase. Recently, the use of the fluorescent probe QUMA-1 for the real-time visualization of endogenous RNA G-quadruplexes in living cells has been demonstrated [NRAS mRNA 5′-UTR), use of a bifunctional fluorogenic probe, ISCH-nras1, was presented [on-target sequence [The identification of RNA G-quadruplex formation in living cells remains a challenge, especially in mRNAs. It was postulated by Guo and Bartel that RNA G4 can be globally unfolded in humans . Howevernstrated . To seleresented . This prsequence ,68. In tsequence , i.e., oHerein, we propose an approach for downregulation of genes containing two distant G-tracts in a G4-mediated manner using RNA G-rich antisense oligonucleotides. We have provided evidence that the chemically modified RNA Q-ASO binds to the 56-nt EGFR mRNA fragment, resulting in the assembly of a bimolecular quadruplex-duplex hybrid structure (2ST-QDH). The formation of the stable G-quadruplex on mRNA template appeared to be an effective steric hindrance for ribosome and resulted in stalling and inhibition of protein synthesis. The silencing activity of QL-ASO G-rich antisense oligonucleotides toward the disease-associated EGFR mRNA target was at least 20% higher than for unmodified RNA or DNA antisense oligonucleotides. Finally, Q-ASO with covalently attached carbazole derivative oBMVC-C3 was used as an internal fluorescent probe. Sequence-specific binding of Q-ASO to the target mRNA was monitored in living cells by the observation of a green fluorescence of oBMVC-C3 moiety. The possibility to control formation of G-quadruplex by the use of G4-ligand linked to oligonucleotides can provide a basis for new investigations in the field of biology.


The programmability of RNA–RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of RNA devices that artificially regulate gene expression. An in silico design for interacting structured RNA sequences that satisfies multiple design criteria becomes a complex multi-objective problem. Although multi-objective optimization is a powerful technique that explores a vast solution space without empirical weights between design objectives, to date, no web service for multi-objective design of RNA switches that utilizes RNA–RNA interaction has been proposed. We developed a web server, which is based on a multi-objective design algorithm called MODENA, to design two interacting RNAs that form a complex in silico. By predicting the secondary structures with RactIP during the design process, we can design RNAs that form a joint secondary structure with an external pseudoknot. The energy barrier upon the complex formation is modeled by an interaction seed that is optimized in the design algorithm. We benchmarked the RNA switch design approaches (MODENA+RactIP and MODENA+RNAcofold) for the target structures based on natural RNA-RNA interactions. As a result, MODENA+RactIP showed high design performance for the benchmark datasets. Synthetic RNA molecules are promising biomolecular devices that provide powerful means for artificially controlling gene expression ,2,3,4. TRNA inverse folding, which automatically explores an RNA sequence space to find RNA sequences that fold into a specified target structure, has been utilized to obtain RNA sequences that have desired structural features and are functional in vitro/in vivo ,11,12. TThese previous RNA–RNA switch design tools adopt mono-objective optimization techniques . RNA–RNA switch design is a complex design problem and it requires more design objectives compared to those of typical single-sequence inverse folding. Since determining optimal weights between the objectives is a tedious task, it is desirable to use the framework of multi-objective optimization. By using multi-objective optimization, where our aim is to find out the Pareto-optimal solutions, we can optimize the RNA sequences without such empirical weights .To design interacting RNAs with multi-objective optimization, we have extended MODENA  with RacAn RNA sequence is defined as a sequence of the nucleotides A, C, G, and U. Let us denote an RNA sequence A multi-objective optimization problem (MOP) addressed in the present study is defined as follows:The In RNA–RNA switch design, we consider the hybridization process between two structured RNA strands. A schematic illustration is shown in ion seed . The intIn RNA–RNA switch design, structure-constrained secondary structure prediction is useful for predicting an interaction seed between two structured RNA strands. We assume that the formation of a nascent complex between two RNA strands occurs through the formation of an interaction seed; therefore, the hybridization probability of the interaction seed should be maximized for desired RNA complexes and minimized for undesired ones  . The intBy using RactIP, for a given maximum nucleotide length of the seed, we can find the optimal interaction seed between two RNA strands, each of which has an independently folded secondary structure. The interaction seed is searched for in a set of nucleotide positions named “seed candidates”; seed candidates are an intersection of the following two sets of nucleotide positions: (i) the nucleotides of the intermolecular base pairs in the predicted joint secondary structure; (ii) the loop nucleotides of the independently folded secondary structures.w nt” [w nt  First, fold two RNA strands independently e.g., b; this iw nt” . With thnt e.g., e; this aw nucleotides) on each of the two strands. While scanning a predicted joint secondary structure, whenever the sliding windows find a pair of continuous seed candidates, the nucleotides of the sliding windows are folded using RNAcofold. After the scan is finished, the folded result with the lowest free energy in the scan is returned as the interaction seed.To compute the interaction seed by using the Vienna RNA Package, we predict the lowest energy value of an interaction seed based on a small sliding window  of Pareto-optimal solutions based on a multi-objective genetic algorithm (GA). The optimal solutions against various weighted sums of OFs, which are obtained by using various weight values, are included in the Pareto-optimal solutions. By using a multi-objective optimization technique, we can perform RNA designs that take multiple OFs into account without empirical weights.iIf the MOP has constraints, we have to handle the constraints properly in the optimization algorithm. In the present study, according to , we adopHere, we briefly describe the algorithm of MODENA . MODENA P of solutions. A child set C of solutions is set to an empty set.The initialization step randomly generates P and used as the parent solutions in the next reproduction step. The remaining worst In the evaluation step, prediction methods such as RactIP are invoked by using a system call to assign predicted structures and energies to the generated solutions. Then, all solutions in  sorting , where l sorting  is consiC.In the reproduction step, the GA operators are called and applied to the parent solutions to generate As design functionalities, sequence constraints, in accordance with the International Union of Pure and Applied Chemistry (IUPAC) notation and mutation of undesired sequence motifs (these work as soft sequence constraints), are available in MODENA. Since the RNA sequence sampling algorithm adopted by MODENA takes the sequence constraints and the dependency graph into account, the generated RNA sequences rigorously satisfy the sequence constraints and are compatible with the target structures, even in cases when multiple target structures are specified. Mutation of undesired sequence motifs is performed just after the initialization and reproduction steps.Es (Es). The structure distance of the isolated state is defined with the array representation of secondary structures as follows: i-th element of the array representation of the secondary structure predicted for strand i-th element of the target structure for strand w of the interaction seed is set to six base pairs [In this design, we specify extended target structures of two RNA sequences and one joint extended target structure for when the two RNAs coexist and are hybridized. RNA sequences .In the To show the design performance of the web server, we benchmarked MODENA+RactIP with a dataset taken from a natural non-coding RNA complex dataset (the sample “RNA–RNA dataset” of RactIP 1.0.1). This dataset consists of eight sets of natural secondary structures . The design results (mean values of We developed a web server for designing molecular switches consisting of two interacting structured RNAs. Users can specify the secondary structures of two isolated RNAs and one hybridized state of the two RNAs as target structures, where we intend that these two RNAs will interact with each other and form a complex when these two RNAs coexist in vivo/vitro. In this web server, the secondary structures are predicted using RactIP or the Vienna RNA Package, and a set of two RNA sequences are optimized by a standard multi-objective genetic algorithm. The GC content of the RNA sequences is controlled through constraints during the design. The design performances were evaluated using the target secondary structures taken from the natural non-coding RNA complex dataset, where MODENA+RactIP showed high design performance for the benchmark target structures. Particularly, when we designed a set of RNAs that fold into the target structures predicted by RactIP, perfect design results were obtained for all target sets. To lower the energy barrier between two isolated RNAs, we considered an interaction seed that is modeled by the formation of an energetically stable intermolecular short helix. In the MODENA web server, with S+ notation, users can easily specify design targets, which include complicated base-paring information and sequence/structure motifs. Our web server is particularly useful if the user wants to design an RNA–RNA switch that has a joint secondary structure with an external pseudoknot, since the MODENA web server is currently the only web service that is capable of such designs.


There are challenges in generating mRNA-Seq data from whole-blood derived RNA as globin gene and rRNA are frequent contaminants. Given the abundance of erythrocytes in whole blood, globin genes comprise some 80% or more of the total RNA. Therefore, depletion of globin gene RNA and rRNA are critical steps required to have adequate coverage of reads mapping to the reference transcripts and thus reduce the total cost of sequencing. In this study, we directly compared the performance of probe hybridization  and RNAse-H enzymatic depletion (NEBNext® Globin & rRNA Depletion Kit and Ribo-Zero Plus rRNA Depletion Kit) methods from 1 μg of whole blood-derived RNA on mRNA-Seq profiling. All RNA samples were treated with DNaseI for additional cleanup before the depletion step and were processed for poly-A selection for library generation.Probe hybridization revealed a better overall performance than the RNAse-H enzymatic depletion method, detecting a higher number of genes and transcripts without 3′ region bias. After depletion, samples treated with probe hybridization showed globin genes at 0.5% (±0.6%) of the total mapped reads; the RNAse-H enzymatic depletion had 3.2% (±3.8%). Probe hybridization showed more junction reads and transcripts compared with RNAse-H enzymatic depletion and also had a higher correlation (R > 0.9) than RNAse-H enzymatic depletion (R > 0.85).In this study, our results showed that 1 μg of high-quality RNA from whole blood could be routinely used for transcriptional profiling analysis studies with globin gene and rRNA depletion pre-processing. We also demonstrated that the probe hybridization depletion method is better suited to mRNA sequencing analysis with minimal effect on RNA quality during depletion procedures. Transcriptome profiling of peripheral whole blood samples is highly desirable for biological research, drug discovery, diagnostic testing, and developing biomarkers in clinical settings –4. WhileTotal RNA from whole blood contains a large portion of globin genes, which originate from red blood cells and accounts for 80–90% of total transcripts . PreviouIn this study, we evaluated two methods for globin gene removal, probe hybridization and RNase H-based enzymatic digestion. The data generated from four commercially available kits were analyzed for performance on mRNA-Seq for whole blood-derived RNA transcriptome. Our results provide information on which of the globin gene removal kit is most suitable for mRNA-Seq data analysis from whole blood samples.Figure p < 0.05) in the NEBgr with 6.3% (±2.3%), while the other three kits were below 1%  than enzymatic methods NEBgr and RZrs  but still below 2% from the total mapped reads  following institutionally approved IRBs. Total RNA was extracted from four aliquots using a PAXgene Blood RNA Kit with DNaseI treatment  according to the manufacturer’s protocol. The extracted RNA was cleaned with DNAseI using Zymo RNA Clean and Concentrator Kit , and yield and quality of the purified RNAs were evaluated using a Qubit  and Agilent 2100 Bioanalyzer , respectively. Those RNAs with RIN values > 7.5 proceeded to the globin mRNA and rRNA removal step from total RNA. Figure A total of 1 μg of each RNA sample was further processed with a GLOBINClear Kit  to remove globin mRNA. Briefly, to remove globin mRNA using this kit, a total of 1 μg of RNA was mixed with a biotinylated capture oligo mix and incubated for 15 min to hybridize with the globin mRNA. Washed streptavidin magnetic beads were added and incubated for 30 min, then the beads were pulled to the bottom of the plate on a magnet plate, and globin-depleted mRNA was transferred to a new tube. Another 1 μg of total RNA was used for both globin mRNA and rRNA  depletion using NEBNext® Globin & rRNA Depletion Kit (NEBgr) , Ribo-Zero Plus rRNA Depletion Kit (RZr) , or Globin-Zero Gold rRNA Removal Kit (GZr) , according to the manufacturer’s protocols with some modifications. For GZr, total RNA was mixed with biotinylated oligonucleotides mixture, and the streptavidin magnetic beads were added and incubated for binding to the hybridized mixture. The streptavidin magnetic beads were pulled down to the bottom of the plate on a magnet plate, and the depleted RNA was transferred to the new plate for the next RNA cleanup step. The NEBgr and RZr employ enzymatic depletion to remove globin mRNA and rRNA, and both kits have a similar procedure. Briefly, total RNA was hybridized with single-strand DNA probes, and RNase H digestion was performed. To remove the excessive single-strand DNA probes, DNaseI treatment was carried out after RNAase H digestion. All depleted RNAs were mixed with 1.8 times amounts of Ampure RNA Clean XP beads  for the enrichment and eluted with 50 μl of water. Then, 1 μl of each RNA was assessed with an Agilent 2100 Bioanalyzer using an RNA 6000 Pico Chip to verify the depletion of globin mRNA and rRNA. The RNA was stored at − 80 °C before being analyzed.2-transformed FPKM + 1) were used. Sequence mapping to the genome and the transcriptome was visualized in Integrative Genomics Viewer . The one-way ANOVA test was used for all statistical analyses , and p-value < 0.05 was considered as statistically significant. Pearson r value was used for correlation analysis of mRNA-Seq data from each kit.The TrueSeq Stranded mRNA-Seq Kit  with poly-A+ selection was used to generate the mRNA-Seq library from depleted RNA samples using four different kits, according to the manufacturer’s protocol. Constructed libraries were quantified by BioAnalizer 2100 system using the D1000 kit (Agilent) and Qubit dsDNA BR Assay kits (Thermo Fisher Scientific). All libraries were pooled  and sequenced 101 bp paired-end reads on Illumina HiSeq 4000. Approximately 30 to 60 million (M) pairs of total reads were generated from each library. FASTQ files were uploaded into Partek Flow software , and primary QC was performed. The STAR (2.6.1d) aligner was used to align reads to the human reference genome (hg38). After alignment, the final BAM files were quantified using Partek E/M algorithm  by Ensem


Understanding habitat-level variation in community structure provides an informed basis for natural resources’ management. Reef fishes are a major component of tropical marine biodiversity, but their abundance and distribution are poorly assessed beyond conventional SCUBA diving depths. Based on a baited-video survey of fish assemblages in Southwestern Atlantic’s most biodiverse region we show that species composition responded mainly to the two major hard-bottom megahabitats (reefs and rhodolith beds) and to the amount of light reaching the bottom. Both megahabitats encompassed typical reef fish assemblages but, unexpectedly, richness in rhodolith beds and reefs was equivalent. The dissimilar fish biomass and trophic structure in reefs and rhodolith beds indicates that these systems function based on contrasting energy pathways, such as the much lower herbivory recorded in the latter. Rhodolith beds, the dominant benthic megahabitat in the tropical Southwestern Atlantic shelf, play an underrated role as fish habitats, and it is critical that they are considered in conservation planning. Within tropical seascapes, reefs are connected with other benthic megahabitats (i.e. features with dimensions from km to tens of km) through the exchange of propagules and organisms at different life cycle stages, as well as through nutrients and energy fluxes3. Connections among reefs, mangroves and seagrass beds enhance biomass and diversity in reef fish assemblages , but the role of other neighboring megahabitats remains unclear, including rhodolith beds, which are among the world’s largest photosynthesizer-dominated benthic communities7. Due to such major knowledge gaps about the distributions and abundance of organisms in tropical seascapes9, management and conservation measures are often suboptimal and based on poorly substantiated assumptions about species’ nestedness and turnover patterns11, among other caveats [e.g.12]. Comprehensive biodiversity inventories with significant spatial cover may take several decades, but a broader understanding of species-habitat associations provides timely surrogates for management13. Shallow water tropical ecosystems can be readily detected by remote sensing and easily sampled with conventional SCUBA in depths of up to 30 m. However, detection and mapping of deeper habitats depends on acoustic surveys and bottom imaging for validation of sonograms, while biological sampling relies largely on mixed-gas diving and submersibles, or fishing14. The use of remote underwater video is an emerging approach for sampling fish and benthic assemblages in these less accessible compartments of tropical shelves15.Coral reefs are among the most biodiverse and productive marine ecosystems, but climate changes and local stressors are driving a rapid and massive global decline16. Reef fishes are model organisms to explore evolutionary and biogeographic mechanisms, sustain millions of small-scale fishers, and their global overfishing has bitterly revealed the central role of herbivory, competition and trophic cascades as drivers of coral reef community structure and dynamics17. As remarked by18, reef fishes defy definitions beyond the tautological and are those individuals that live on coral reefs. Globally, approximately 20% of all reef fishes also occur in non-reef habitats8, which are used as foraging and spawning grounds, or as recruitment/juvenile habitats. Groupers and snappers, among several other fish families, engage into long-range movements towards nearshore or offshore spawning grounds19, and mangroves and seagrass beds can be important habitats for early stages of reef fishes4. On short time scales, diel movements of diurnal planktivores (e.g. damselfishes) and nocturnal invertivores (e.g. grunts and emperor breams) enhance the coupling among benthic habitats and the water column . Knowledge gaps about the use of the wider seascape by fish and other organisms, as well as the biological and physical connections between its habitats, still impede conservation planning and management20.Due to extensive commercial exploitation and the relatively large sizes attained by fishes, these vertebrates are among the best-known reef-associated groups21. Southwestern Atlantic (SWA) was regarded as a small and fragmented outpost of the “West Indian” reef province until the end of the 20th Century, and a myriad of reef-associated SWA endemics was only described in the past few decades, from fishes to keystone reef-building invertebrates21. The size and distribution of SWA reefs are still largely known from nautical charts that only show navigation hazards, but recent multi-level sampling is revealing large and complex mesophotic coralline reefs7, as well as extensive rhodolith beds [sensu22] along more than 30 latitudinal degrees, between 10 and 150 m depths5. Indeed, rhodolith beds are the dominant benthic habitat along the tropical SWA continental shelf and one of the world’s largest algae-dominated benthic systems22. Nevertheless, the role of tropical rhodolith beds as fish habitats has never been assessed23. Due to their broad spatial extension, rhodolith beds may influence ecological processes  that drive reef fish community structure and dynamics4.The Eastern South American coast encompasses a relatively isolated, small and species-poor reef Province with high endemism levels and biological communities assembled by species with Caribbean, West African and Indian Ocean affinities2 mosaic of carbonate hard bottom. The study region comprises SWA’s largest reef complex with (1) fringing reefs around a small archipelago, (2) emerging and quasi-emerging pinnacles distributed in one inshore (10 km off the coast) and one offshore arc (55 km off the coast) totaling ~ 8,800 km2, and (3) the world’s largest continuous rhodolith bed 24. The inshore reef arc (~ 10 km offshore) is subjected to high fishing pressure and terrigenous sourced turbidity, while the offshore arc (~ 60 km offshore) is within the Abrolhos National Marine Park (ANMP) and is less exposed to fisheries and land-based stressors. Our standardized survey with BRUVs allowed for richness and biomass estimates for nearshore and mid-shelf reefs, as well as rhodolith beds in depths beyond SCUBA limits. Instead of being marginal  habitats26, rhodolith beds were found to be major reef fish habitats in the tropical SWA and need to be thoughtfully accounted for conservation planning and marine management.Here, we aimed at exploring the importance of rhodolith beds as fish habitats at a range of local to regional scales and depth categories. Our regional-level reef fish survey in the Abrolhos Bank, Brazil, was carried out with stereo Baited Remote Underwater strereo-Videos (BRUVs) across a ~ 30,000 km6, mainly soft bottom specialists such as flatfishes (Symphurus sp. and Bothus sp.), sand perches (Diplectrum sp.) and the grey triggerfish . On the other hand, over half of the species recorded only in rhodolith beds are deeper dwellers, including butterflyfishes (Chaetodon sedentarius and Prognathodes brasiliensis), planktivorous damselfishes (Chromis enchrysura and C. flavicauda), flameback angelfish (Centropyge aurantonotus), vermillion snapper (Rhomboplites aurorubens) and dwarf serranids (Serranus chionaraia and S. phoebe). Inshore pinnacles had unique species that are biologically connected to estuarine habitats, including mojarras (Eucinostomus spp.), mutton snapper , crevalle jack (Caranx hippos) and Atlantic spadefish (Chaetodipterus faber). Unique occurrences in the three offshore pinnacles’ sites comprised the Caribbean reef shark (Carcharhinus perezi), sharksucker (Echeneis naucrates) and the yellowtail damselfish (Microspathodon chrysurus). The fringing reef also had few unique occurrences, including remarkably large schools of chubs (Kyphosus spp.).We recorded 107 reef fish species , 71 (66.4%) in fringing and pinnacles’ reefs and 85 (79.4%) in rhodolith beds (Supplementary Table p < 0.000001) was associated with differences in the amount of light reaching the bottom  and megahabitat  . Turnover between pinnacles/fringing reefs and offshore rhodolith sites was relatively high, while inshore rhodolith sites encompassed subgroups of the reef assemblages . Most of the variation explained by a generalized dissimilarity model  Fig.  online, Pinnacles’ and fringing reefs had the highest abundances for 35 fish species that occurred in both benthic megahabitats (71%), while rhodolith beds encompassed the highest abundances for 14 species 29%) that also occurred in reefs 6. Fine-sediment dissipative beaches and a large estuary with mangroves dominate the coastline, and terrigenous-mixed sediments predominate in the inner shelf27. This large and complex seascape  due to variation in habitat structure, exposure to wave energy and currents, and stochasticity in population dynamics16, but may be markedly similar over broader biogeographic areas30, indicating non-random species associations and convergent ecosystem functioning under similar biotic and abiotic forcing. In Abrolhos, distance among sites was a weak predictor of beta diversity at the seascape level, and turnover between reef sites was also relatively low, akin to the turnover between reefs and inshore rhodolith beds with high amounts of sand and low macroalgae cover  and non-trophic (e.g. shelter from predators) connections with the benthos31.Compositional variability in biological communities is strongly dependent on spatial scale. Accordingly, beta diversity is expected to be high at biogeographic and local scales, while turnover tends to be lower at regional scalesscales <  km due tver Fig.  online. 21. The high turnover recorded at the seascape level, with highly dissimilar reef fish assemblage structures, indicates that reefs and rhodolith beds have distinct functional properties. Turf dominance on reefs (~ 50% of turf cover) may be associated with the high abundance of roving herbivores in this megahabitat, and to the lower turnover between reef sites. Turf algae is the main trophic connection with herbivorous fish, which typically avoid macroalgae32, and was positively correlated with light at bottom, which may be associated with the higher reef fish turnovers observed at the seascape level.Reef fishes have a relatively high dispersion potential, both as larvae and as swimming adults, and their distribution at the seascape level is largely constrained by large scale environmental filtering33. While there were more species with maximum abundances recorded in pinnacles and fringing reefs, nearly one-third of the 49 species recorded in both benthic megahabitats had maximum abundances in rhodolith beds  and black (Mycteroperca bonaci) groupers34.Most biological assemblages include a smaller number of abundant species and a long tail of rare species, and coral reef fishes are no exception. Abundance distributions tend to be more skewed toward commonness than the Gaussian and less so than the lognormal distribution, which would be the sum and the product of random variables, respectivelyeds Fig. . This treds Fig. , which m35, and is herein confirmed as a regional trend, once this trophic guild was also minor in rhodolith beds. For instance, large schools of forked-tailed plankton-feeding damselfishes (Chromis spp.), wrasses (Clepticus spp.) and groupers (Paranthias spp.), which are common along the entire West Atlantic16, are lacking from Abrolhos, where only sparse individuals occur in offshore sites. Several microcarnivores  and other deeper-water dwellers  recorded only in offshore rhodolith beds add to the habitat and cross-shelf variation, albeit these species may associate to reef structures across their distribution range21. The dissimilar trophic structure of fish assemblages in rhodolith beds was dominated by an impoverished assemblage of benthic-feeding carnivores  and carnivorous fish that feed both in the bottom and in the water column   Fig. , this tr20. Fish biomass was five-fold higher in the reefs protected by the ANMP, indicating an overwhelming effect of management regime. The exclusive and frequent occurrence of the endangered Caribbean reef sharks (Carcharhinus perezi) within the ANMP adds to the positive effects of the enforced no-take status of this area. Rhodolith beds became the most important reef fishing grounds in the Abrolhos shelf since the 1980s, following the overfishing of inshore reefs31. However, besides being rarely targeted by enforcement operations, which should regularly restrain illegal fishing gear  and seasons’ violations, rhodolith beds and shelf-edge reefs are the least represented habitats in the existing MPA network12.Fish biomass in the vast offshore rhodolith beds was consistently higher than that of inshore reefs and rhodolith beds, and seconded that of the outer arc reefs within the ANMP. Considering the several maximum abundances recorded in rhodolith beds Fig.  and the 23, and in ocean basins with very different pools of fish species. Therefore, some rhodolith beds may not be suitable as reef fish habitats. Despite the low SWA-endemic fish biomass (~ 2%) in rhodolith beds, endemics comprised 15% of the fish assemblages in this megahabitat. This finding provides additional support for the idea that rhodolith beds are extremely relevant to the conservation of the biodiversity of the small, unique and highly threatened Brazilian reefs. Rhodolith beds comprise a major but belittled reef fish habitat within the SWA, and should be urgently prioritized in marine spatial planning, licensing and fisheries management. While carbonates’ mining and oil and gas exploitation in reefs are banned in Brazil, these activities are steadily growing in rhodolith beds, which are often categorized as rubble with low importance for biodiversity conservation36. Quantitative assessments covering the broad spectrum of reef fish habitats are needed for robust inferences about rarity/conservation status and the habitat range of these vertebrates, which play a major role in healthy ecosystem functioning.It is unlikely that further sampling will change our overall conclusions, nor will have any impact in the recommendations for managers and policy-makers. An additional caveat about the generality of the results presented herein is that rhodoliths occur under a wide spectrum of environmental conditions, from shallow high-energy to deep low-energy settings, from the tropics to polar latitudes15, with stainless steel frameworks and two cylindrical PVC camera housings separated by 70 cm and converged at 8°. High-resolution videos were captured with Go Pro Hero 4 cameras . A flexible PVC arm (1.5 m) was used to hold plastic or metal mesh bags with 800 g of bait (mix of sardines and canned cat food) in front of the camera lens. Replicated 1-h deployments (n = 137) were done between 08:00AM and 16:00PM in order to avoid twilight periods. Sampling effort at each site is detailed in Fig. 15. Videos were analyzed for 40 min after BRUV systems settled on seafloor. Each system was calibrated before and after each field trip using the software CAL (SeaGIS Pty. Ltd.). Benthic cover was estimated from 5 to 10 standardized (1 × 1 m) orthogonal images of the bottom obtained at each site with a Go Pro camera inside a PVC housing and two light beans . Abundances and body lengths of each fish species were recorded with the software EventMeasure v3.51 (SeaGIS Pty. Ltd). Relative abundances were based on the MaxN index, a conservative measure that uses the largest number of individuals of each species in a single frame15. Since the number of deployments varied among sites, total abundance was divided by the sampling effort at each site. The fork length of all individuals at the MaxN was measured and used to estimate biomasses through conversion constants and length and weight equations provided by FISHBASE . Species’ assignments to trophic groups follow21.Sampling aiming to assess fish abundance and benthic cover in different megahabitats and cross-shelf strata was carried out in May 2017 and 2018 (summer) in the Abrolhos Shelf . Sampling covered 19 sites, six of which in pinnacles’ and fringing reefs, and 13 in rhodolith beds, distributed in a cross-shelf and latitudinal grid  was obtained from the Bio-ORACLE platform (http://www.bio-oracle.org/index.php)37, comprising post-processed data by GlobColour (http://globcolour.info) merged with MERIS/MODIS/SeaWiFS data, with a spatial resolution of 9.2 km. Geographical distances among sites and offshore distance were calculated from the geographical coordinates of the sampling sites using the ArcGis platform (ESRI). Bathymetry and megahabitat distribution were obtained from24. Distribution of emerging and quasi-emerging reefs was obtained from the interpretation and semiautomatic classification of an orbital image  in a digital image processing environment. The diffuse attenuation coefficient of light at 490 nm (Kd490) was used as a proxy for turbidity and was obtained from NOAA’s Easier Access to Scientific Data database (https://coastwatch.pfeg.noaa.gov/erddap), at 4 km spatial resolution.Light at bottom . The importance of each variable, as described in the results, was measured as the percent change in the explained deviance observed in the full model  and the deviance explained by a model fit only with the individual variable. The output of the model containing only the significant explanatory gradients (megahabitat and light at bottom) was used to predict community dissimilarity for each sampling site. The scores from a multidimensional scaling (MDS) between the predicted beta diversity values among sampling locations were used to assign colors to those sites based on their position in this two-dimensional ordinal space40. The same colors for each site were used in Fig. Total beta diversity was calculated with the Sørensen dissimilarity index and decomposed into the Simpson index, which is related to spatial turnover of species, and the nestedness componentsee Fig. . Beta di version .4.2;41. Data and scripts are provided as supplemental materials.Supplementary Information.