Amphimedon queenslandica holobiont. Annotated genomes of the sponge host and the three most abundant bacterial symbionts, which can comprise up to 95% of the adult microbiome, are available. Importantly, this allows for transcriptomes to be accurately mapped to these genomes, and thus quantitatively assessed and compared. The two strategies that we compare here are (i) poly(A) captured mRNA-Seq (Poly(A)-RNA-Seq) and (ii) ribosomal RNA depleted RNA-Seq (rRNA-depleted-RNA-Seq). For the host sponge, we find no significant difference in transcriptomes generated by the two different mRNA capture methods. However, for the symbiont transcriptomes, we confirm the expectation that the rRNA-depleted-RNA-Seq performs much better than the Poly(A)-RNA-Seq. This comparison demonstrates that RNA-Seq by ribosomal RNA depletion is an effective and reliable method to simultaneously capture gene expression in host and symbionts and thus to analyse holobiont transcriptomes.Investigations of host-symbiont interactions can benefit enormously from a complete and reliable holobiont gene expression profiling. The most efficient way to acquire holobiont transcriptomes is to perform RNA-Seq on both host and symbionts simultaneously. However, optimal methods for capturing both host and symbiont mRNAs are still under development, particularly when the host is a eukaryote and the symbionts are bacteria or archaea. Traditionally, poly(A)-enriched libraries have been used to capture eukaryotic mRNA, but the ability of this method to adequately capture bacterial mRNAs is unclear because of the short half-life of the bacterial transcripts. Here, we address this gap in knowledge with the aim of helping others to choose an appropriate RNA-Seq approach for analysis of animal host-bacterial symbiont transcriptomes. Specifically, we compared transcriptome bias, depth and coverage achieved by two different mRNA capture and sequencing strategies applied to the marine demosponge The online version contains supplementary material available at 10.1007/s10126-022-10138-8. Increasing interest in the microbes that live in association with multicellular organisms has revealed that symbioses are ubiquitous, and that plants and animals are best considered as holobionts comprising both a multicellular host and diverse microbiota (Bordenstein and Theis Bemisia tabaci) complex revealed complementation in amino acid biosynthesis pathways between the whitefly and its bacterial endosymbiont Candidatus Portiera aleyrodidarum  has become a widely used method to study host-symbiont interactions and is especially valuable when used to profile host and symbiont transcriptomes simultaneously in the same biological samples. In the past decade, several studies have applied dual transcriptome analysis to reveal potential modes of cross-talk between symbiosis partners. For instance, early transcriptome analysis of the whitefly RNAs, making it necessary first to remove rRNA and enrich for coding sequence mRNAs. This need for rRNA removal creates challenges for studies wishing to capture both eukaryotic and bacterial mRNAs, because of differences in eukaryotic and bacterial transcript processing, lifespan and decay. In the eukaryote animal hosts, mRNA transcripts generally have a half-life of many hours  capture method  through most of the life cycle  capture RNA-Seq methods. Accurate determination of the fraction of expressed eukaryote and bacterial genes that are captured by poly(A) enrichment compared to rRNA depletion methods requires that transcripts can be mapped back to assembled genomes of all partners. When this is not possible because genomes are not available for both host and symbionts, it is unclear to what extent the transcriptomes represent an accurate picture of the contributions of various partners in the symbiosis. To quantitatively compare the efficacy of Poly(A)-RNA-Seq versus rRNA depletion-RNA-Seq for the capture of both animal host and bacterial symbiont transcriptomes, here, we apply both strategies to an analysis of gene expression in the demosponge Amphimedon queenslandica were collected from Heron Island Reef, Great Barrier Reef, Queensland, Australia  as described previously , and quality was assessed by 1% TAE agarose gel electrophoresis. Illumina libraries were constructed separately from each of the six total RNAs using an Illumina TruSeq Stranded mRNA Library Prep Kit.Total RNA was extracted separately from the six replicated biopsies, after bacterial cell enrichment, using TRIzol (Sigma-Aldrich) and DNA was removed with deoxyribonuclease I (Invitrogen), both following the manufacturer’s protocol. RNA was quantified by Qbithttps://sapac.illumina.com/products/by-type/molecular-biology-reagents/ribo-zero-gold-rrna-removal-epidemiology.html), which removes animal, Gram-positive and Gram-negative bacterial cytoplasmic and mitochondrial rRNA. One hundred nanograms of the depleted RNA was then used as input to the mRNA-seq protocol, but omitting the poly-A pulldown and instead of starting at the RNA fragmentation step.Three of the samples were subjected to the standard TruSeq Stranded mRNA-seq workflow, using the poly-A pulldown and standard protocol with 1 µg total RNA as input. The other three samples  were first rRNA-depleted using the Ribo-Zero Gold ribosomal RNA Removal Kit  capture or rRNA depletion.A. queenslandica, AqS1, AqS2 and AqS3 genomes  in length were processed using Trimmomatic (version 0.36)  was counted by htseq-count (v. 0.11.2.) from the HTSeq framework, using default parameters except for –stranded = reverse  with previously published deep RNA-Seq data  analyses were performed for the genes captured by the three different library preparation methods, rRNA-depleted-RNA-Seq, Poly(A)-RNA-Seq and bacterial-unenriched Poly(A)-RNA-Seq. GO term annotations of the holobiont gene models were performed using Blast2GO (version 5.2.4)  and the three replicate Poly(A)-RNA-Seq samples , respectively. After filtering low-quality reads with Trimmomatic -RNA-Seq groups, with Spearman’s correlation efficiencies ranging from 0.907 to 0.969 -RNA-Seq replicates, with AqS1 ranged from 0.915 to 0.920, AqS2 from 0.843 to 0.855 and AqS3 from 0.695 to 0.733. Overall, the strong correlations between the biological replicates from each experimental group suggested the gene expression profiles of the four species in the A. queenslandica holobiont were comparable within the rRNA-depleted- and the Poly(A)-RNA-Seq samples.The reads aligned to each gene were considered a measure of gene expression. Correlation of the gene expression between the technical triplicates was estimated by gene read counts (gene depth) between individuals  within each experimental group. All read counts were included in calculating the Spearman's correlation efficiencies. For the host sponge 969 Fig. C, D. ForTranscript quantification is one of the most common applications of RNA-Seq; it estimates the gene expression levels based on the number of reads mapped to each gene. This approach is widely used to identify differential expression (DE) of genes between different treatments and contexts  was very similar to that of the Poly(A)-RNA-Seq data (57.38%) , and both of these were lower than the bacterial unenriched-Poly(A)-RNA-Seq data (66.62%)  AqS2 genes and 80.36% (± 0.0260) AqS3 genes were captured by rRNA-depleted-RNA-Seq libraries. These gene coverages were much higher than those captured by Poly(A)-RNA-Seq libraries, which, on average, were 70.07% (± 0.0119) for AqS1, 65.99% (± 0.0181) for AqS2 and 44.46% (± 0.0259) for AqS3 . The coverages also were higher than that of the bacterial unenriched-Poly(A)-RNA-Seq samples, which were 18.14% in AqS1, 24.06% in AqS2 and 1.13% in AqS3. This gene coverage comparison indicated that rRNA-depleted-RNA-Seq and Poly(A)-RNA-Seq could capture the animal host transcriptomes at the same level, but rRNA-depleted-RNA-Seq was better at capturing the bacterial symbiont transcriptomes. The unenriched-Poly(A)-RNA-Seq was less likely to comprehensively capture bacterial symbiont transcriptomes compared to the rRNA-depleted-RNA-Seq and Poly(A)-RNA-Seq.When we used the minimal threshold of 1 read pairs mapped, the percent of data 66.6% -RNA-Seq data, respectively, with 58.66% of the genes represented in bacterial unenriched-Poly(A)-RNA-Seq data  AqS2 genes and 75.63% (± 0.0344) AqS3 genes were captured in the rRNA-depleted-RNA-Seq data. By comparison, in the Poly(A)-RNA-Seq data, on average, only 54.22% (± 0.0016) AqS1 genes, 41.07% (± 0.0123) AqS2 genes and 19.57% (± 0.0130) AqS3 genes were captured. In the bacterial unenriched-Poly(A)-RNA-Seq samples, only 3.22% AqS1 genes, 5.43% AqS2 genes and 0.20% AqS3 genes were captured. These gene coverages indicate that rRNA-depleted-RNA-Seq captured a similar proportion of sponge genes and a higher proportion of bacterial genes than Poly(A)-RNA-Seq. For the unenriched-Poly(A)-RNA-Seq sample, the coverage was representative only for the sponge transcriptome but not for the symbiotic bacterial transcriptomes.When the threshold was raised to at least 5 read pairs per gene, on average, 50.94% and 49.92% A. queenslandica, on average, the median gene depths were 110 (± 15.95) and 113 (± 1.73) reads per gene for the rRNA-depleted- and Poly(A)-RNA-Seq libraries, respectively, and 83 for the bacterial unenriched-Poly(A)-RNA-Seq library. For each bacterial symbiont, the rRNA-depleted-RNA-Seq data showed the highest average gene depths, which were higher than these of the Poly(A)-RNA-Seq data and bacterial unenriched-Poly(A)-RNA-Seq data. On average, the rRNA-depleted-RNA-Seq median gene depths were 446 (± 89.37) in AqS1, 162 (± 84.50) in AqS2 and 47 (± 13.75) in AqS3; the Poly(A)-RNA-Seq median gene depths were 14 (± 0.58) in AqS1, 7 (± 0.58) in AqS2 and 4 (± 0) in AqS3 -RNA-Seq samples implied that there was no noteworthy gene depth difference among the host sponge transcriptome captured by both methods, but sponge transcript depths were lower than obtained by bacterial unenriched-Poly(A)-RNA-Seq - and bacterial unenriched-Poly(A)-RNA-Seq samples Fig. B. For thA. queenslandica genes, 2417 AqS1 genes, 1193 AqS2 genes and 1977 AqS3 genes were assigned to 6357, 1317, 904 and 1030 GO terms, respectively.A first step towards understanding gene functions in host and symbionts is to annotate expressed and enriched genes in RNA-Seq data sets using Gene Ontology (GO) , Poly(A)-RNA-Seq data (97.43% ± 0.0036) and bacterial unenriched-Poly(A)-RNA-Seq data (98.47%). For the bacterial symbionts, the expressed GO term percentages were 98.96% ± 0.002 in AqS1, 99.34% ± 0.001 in AqS2 and 98.32% ± 0.006 in AqS3 for the rRNA-depleted-RNA-Seq samples ; and also higher than that of bacterial unenriched-Poly(A)-RNA-Seq sample, which was 38.72% in AqS1, 46.02% in AqS2 and 2.91% in AqS3 -RNA-Seq and bacterial unenriched-Poly(A)-RNA-Seq.To identify any differences in GO functional annotations among the different RNA-Seq libraries, the percentage of expressed GO terms in each sample was calculated for each species. That is, for a given species, if any gene in the genome that had been assigned to a particular GO term was expressed with at least 1 read pair in a given sample, that GO term was considered an expressed GO term. The expressed GO percentage was estimated as the percent of total GO terms for the genome that were represented in the RNA-Seq data, with replicates averaged Fig. . For A. A. queenslandica, on average, the expressed gene percentage of each GO term was higher in the bacterial unenriched-Poly(A)-RNA-Seq data (93.64%) compared to the rRNA-depleted-RNA-Seq data (86.57%) and the Poly(A)-RNA-Seq data (90.62%). For each bacterial symbiont, the rRNA-depleted-RNA-Seq data had the highest GO term expressed gene percentages compared to the Poly(A)-RNA-Seq data and bacterial unenriched-Poly(A)-RNA-Seq data -RNA-Seq and bacterial unenriched-Poly(A)-RNA-Seq for the three symbionts varied considerably. For the Poly(A)-RNA-Seq, the average GO term expressed gene percentages decreased from 84.49% in AqS1 to 77.45% in AqS2 and 67.85% in AqS3. For the bacterial unenriched-Poly(A)-RNA-Seq, this percentage decreased from 21.88% in AqS1 and 31.27% in AqS2 to 0.43% in AqS3  genes in the three RNA-Seq data sets, with replicates averaged, were compared to determine if GO categories were equally represented in the different types of RNA-Seq libraries Fig. . This apeq data 9.64% compeq data 9.64% compA. queenslandica genes involved in integral component of membrane, membrane, ATP binding and protein binding , four cellular component GOs  and 13 molecular function GOs  -RNA-Seq capture sponge host transcripts at similar levels with at least 5 read pairs aligned . In contrast, only the rRNA-depleted-RNA-Seq captured a large proportion of the expressed genes in the symbionts , compared to much smaller proportions obtained by Poly(A)-RNA-Seq . This difference potentially reflects the different capacities of these two RNA-Seq approaches to efficiently capture bacterial mRNAs, which are relatively unstable , we find there are 54 AqS1, 50 AqS2 and 89 AqS3 GO terms that are present only in rRNA-depleted-RNA-Seq transcriptomes. This bias appears not to support bacterial polyadenylation being a ubiquitous and random post-transcriptional modification -RNA-Seq method does not appear to provide a random sample of the bacterial transcriptome profile. If random, we expect all GO categories to reduce proportionally with the reduction in transcript coverage observed when comparing Poly(A)-RNA-Seq and rRNA-depleted-RNA-Seq libraries. Although many GO terms are shared between libraries -based RNA-Seq approach that we tested. Importantly, there was not a marked difference between sponge host transcriptome representation in rRNA-depleted- and Poly(A)-RNA-Seq data sets that were derived from bacterial cell enrichment. The similar output from both rRNA-depleted- and Poly(A)-RNA-Seq datasets indicate that (i) a sufficient number of sponge cells are captured using this bacterial cell enrichment method to generate highly representative RNA-Seq libraries and (ii) there is no substantial loss of host transcript abundance or representation in the rRNA-depleted-RNA-Seq dataset. In contrast, all three bacterial symbiont transcriptomes are significantly better covered using the rRNA-depleted-RNA-Seq approach. This high bacterial gene coverage in the rRNA-depleted-RNA-Seq libraries is similar to the human-pathogen system where an rRNA-depleted-RNA-Seq method captured 88% of the AqS1 alone comprises well over 50% of the bacterial community , is less well representative (gene depth is similar to AqS1 but gene coverage is lower — 80.4% vs 89.7%), assuming this difference is not related to biological differences (i.e. proportion of the genome expressed). Increasing the amount of input RNA from bacterial enrichments, and/or increasing the number of reads generated, could relatively easily increase the amount of available sequence data to be mapped to the genomes of less abundant members of the holobiont. This will increase the transcript representation of numerically minor members of the holobiont.Despite the ability of rRNA-depleted-RNA-Seq to capture a better representation of bacterial transcripts, our study also demonstrates that this approach may not obtain a near-complete representation of transcripts from the less abundant bacterial members of the holobiont. We provide here the first, to the best of our knowledge, quantitative and qualitative comparison of RNA-Seq methods that address the high proportion of rRNAs in both eukaryotic and prokaryotic transcriptomes by either selecting for polyadenylated mRNAs or depleting rRNAs. In doing so, we empirically demonstrate that the latter approach — depleting rRNAs prior to construction of an RNA-Seq library — can be applied to a marine animal holobiont to successfully achieve simultaneous and quite comprehensive capture of transcripts of both the animal host and its bacterial symbionts. Although selection for polyadenylated mRNAs, which is the approach routinely used for animal transcriptomes, does indeed capture some of the bacterial symbiont transcripts, it does so in a biased manner that renders it unsuitable for symbiosis studies where the goal is to study gene expression in both host and symbionts. Instead, depleting rRNA in both host and symbionts in a single step provides a time- and cost-efficient approach that can proceed through to simultaneous capture of transcriptomes from all partners using the same biological samples. The approach should be relatively easily applied to any holobiont comprising a eukaryotic host and bacterial symbionts.Supplementary file1 (XLSX 814 KB)Below is the link to the electronic supplementary material.


Pipistrellus pygmaeus from Uppsala, Sweden. Total RNA was extracted from the saliva and feces of individual bats and analyzed using Illumina sequencing. The results identified sequences related to 51 different viral families, including vertebrate, invertebrate, and plant viruses. These viral families include Coronaviridae, Picornaviridae, Dicistroviridae, Astroviridae, Hepeviridae, Reoviridae, Botourmiaviridae, Lispviridae, Totiviridae, Botoumiaviridae, Parvoviridae, Retroviridae, Adenoviridae, and Partitiviridae, as well as different unclassified viruses. We further characterized three near full-length genome sequences of bat coronaviruses. A phylogenetic analysis showed that these belonged to alphacoronaviruses with the closest similarity (78–99% at the protein level) to Danish and Finnish bat coronaviruses detected in Pipistrellus and Myotis bats. In addition, the full-length and the near full-length genomes of picornavirus were characterized. These showed the closest similarity (88–94% at the protein level) to bat picornaviruses identified in Chinese bats. Altogether, the results of this study show that Swedish Pipistrellus bats harbor a great diversity of viruses, some of which are closely related to mammalian viruses. This study expands our knowledge on the bat population virome and improves our understanding of the evolution and transmission of viruses among bats and to other species. Increasing amounts of data indicate that bats harbor a higher viral diversity relative to other mammalian orders, and they have been recognized as potential reservoirs for pathogenic viruses, such as the Hendra, Nipah, Marburg, and SARS-CoV viruses. Here, we present the first viral metagenomic analysis of  Bats belong to the order Chiroptera and represent the second most diverse mammals after rodents, comprising over 1423 recognized species .Bats have, however, also been shown to be natural reservoirs of a variety of viruses, and viral spill-over events from bats to humans and/or animals have led to infectious disease outbreaks ,7, incluMyotis daubentonii bats from southern Sweden [Pipistrellus pygmaeus  bats from two locations in Uppsala, Sweden. In addition, through these studies, near full-length genomes of bat corona- and picornaviruses were characterized.Viral metagenomics is a target-free and unbiased approach that has been proven to be a powerful tool for characterizing viral nucleic acids from any type of sample and has also enabled the identification of unknown viruses ,23. Seven Sweden . HoweverPipistrellus pygmaeus, were trapped at two different locations at Hammarskog, a nature reserve in Uppsala, Sweden . The bats were trapped using mist nets when leaving their roosts. The field work was performed during June and July 2020. Four of the bats were collected at location 1 and six at location 2. The species were determined by a bat specialist, and the saliva samples were collected from individuals using swabs that were then put in PBS solution. Prior to sampling, the bats were placed in a cloth bag to keep warm, and any droppings  were collected from the cloth bag. Through this approach, feces were obtained from all bats at location 1 and from 5 of the 6 bats trapped at location 2. The bats were released immediately after the sample collection. The swabs and feces were stored on ice in the field, and then transferred to −80 °C until further use.In total, 10 Soprano pipistrelle, g for 5 min at 4 °C and filtered using ultracentrifuge membrane filters  at 13,000× g for 4 min. The total RNA was extracted using a combination of TRIzol and GenJet RNA extraction columns  and eluted in 40 μL of nuclease-free water. The quality and quantity of the RNA was measured using a 4200 TapeStation system  and a Qubit high sensitivity RNA assay kit . The extracted RNA was stored at −80 °C until further use. For the saliva samples, 200 μL of the PBS/swab solution was used, and RNA was extracted using a combination of TRIzol and GeneJet RNA extraction kit as previously described. Next, 5–10 μL of RNA from each bat (No. 10) was pooled based on sample type and geographical location, and a total of 6 pools (up to 3 RNA samples/pool) were obtained. The pooled RNA was treated with DNase using an RNAse-free DNase set  and purified with an RNeasy MinElute Cleanup kit . Ribosomal RNA was depleted using a RiboMinus Eukaryotic System v2 , according to the manufacturer’s instructions. The RNA clean-up was performed with an RNeasy MinElute Cleanup kit  as before and eluted in nuclease-free water in the final step. Complementary DNA synthesis and SPIA amplification of RNA were performed using an Ovation RNA-seqV2 system , as per the manufacturer’s protocol, and the products were purified using a GeneJet PCR Purification Kit  and eluted in 30 μL of nuclease-free water. The quality and size distribution of the products were estimated using a 4200 TapeStation System . The amplified ovation products (6 amplicons) were pooled again to 3 samples based on sample type and location as before and submitted to the SNP&SEQ Technology platform in Uppsala, Sweden for library preparation and sequencing. The libraries were prepared using a SMARTer ThruPLEX DNA-seq library preparation kit , and high-throughput sequencing (HTS) was performed with 150 cycles of paired-end sequencing on one SP flow cell using a NovaSeq 6000 system and v1 sequencing chemistry .The fecal samples were transferred to Precellys CK14 tubes containing 1.4 mm ceramic beads  and 450 μL of TRIzol™ LS reagent . Then, they were mechanically homogenized using a Precellys Evolution tissue homogenizer , according to the following protocol: 4 cycles with 30 s/cycle and 30 s pause between each cycle at 6500 RPM. This step was performed under cold conditions using liquid nitrogen. The supernatant was collected after the centrifugation at 13,000× de novo assembler, Megahit (v.1.2.9) [The raw data from the NovaSeq 6000 sequencing run were quality checked with fastp (0.23.2) by trimming adapter sequences, low sequencing quality tails, and removing duplicate reads . Then, tv.1.2.9) , and thev.1.2.9) , and aliv.1.2.9)  for visuv.1.2.9)  to estimTM DNA Polymerase . Thermal cycling was initiated with a denaturation step at 95 °C for 10 min, followed by 35 cycles of 95 °C for 30 s, 59–60 °C for 30 s, 72 °C for 1 min, and a final extension at 72 °C for 7 min. The PCR primer pairs used in this study, for coronavirus (BtCoV/F-MV2/P.pyg/SE/2020), were 5′-TGAAGGCTGAAGGTGATGG-3′ and 5′-GCAAATCCAAGTTTCTGAAGC-3′; 5′-GCACTTACTACTTACCATACC-3′, and 5′-ACCGCCAAGATACAA CTTGG-3′. The amplified products were purified with a GeneJet PCR purification kit  and sequenced at Macrogen Europe (Macrogen Europe BV).Viral contigs were used to manually design specific primers to confirm the presence of a virus as well as to amplify the missing regions of the selected viruses from the unprocessed total RNA. Reverse transcription was performed using a SuperScript III reverse transcriptase , and PCR amplification was performed using AmpliTag Goldp-value was ≤0.05 for at least three different models and when the beginning and end breakpoints related to each recombination event could be determined.Using the MEGAx tool (10.2.6) , the seqThe short paired-end sequence read data are available at the NCBI’s Sequence Read Archive (SRA) under BioProject number PRJNA823526, and the viral genomes are available at GenBank with accession numbers as represented in Six saliva and nine fecal samples were collected from ten Pipisterllus pygmaeus bats trapped from roosts, from two different locations in Uppsala, Sweden . Total RCoronaviridae, Retroviridae, Herpesviridae, Hepeviridae, Tymoviridae, and unclassified RNA viruses , while Alphatetraviridae, Astroviridae, Dicistroviridae, Luteoviridae, Iflaviridae, Mimiviridae, Paramyxoviridae, Partitiviridae, Picornaviridae, Poxviridae, Solemoviridae, and Virgaviridae-related sequences were detected in feces (F-MV and F-HS pools). The exact viral read counts for each viral family are provided in  viruses . Viral sThe assembly of good quality sequences generated several longer contigs , some ofde novo assembly and Sanger sequencing. The near full-length bat coronavirus (BtCoV) genome  consisted of 27,865 nucleotides (nt), encoding for all the coronavirus reference proteins , partial 5′ UTR (231 nt), and 3′ UTR (674 nt). The BLAST search of individual ORFs showed that this genome was most similar to the Danish BtCoV (MN482242.1) from P. pygmaeus bats [p-values ≤0.05); the p-values from each method are shown in Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and Deltacoronavirus genera. The phylogenetic analysis of full-length ORF1ab protein showed that Swedish BtCoVs sequences from this study clustered with viruses within the Alphacoronavirus genus. Two of the BtCoVs, BtCoV/F-MV1/P.pyg/SWE/2020 and BtCoV/F-HS1/P.pyg/SWE/2020, were closely clustered with other BtCoVs from Denmark and Italy within the subgenus Nyctacovirus, and BtCoV/F-MV2/P.pyg/SWE/2020 grouped with Finish and Danish BtCoVs within the subgenus Pedacovirus BtCoVs .In total, there were 145,383 corona viral reads in the F-MV pool and 226 reads in the S-HS pool. Most coronavirus reads, 1,210,402, were found in the F-HS pool, accounting for 1.51% of the total reads. From the F-MV pool, two near full-length coronavirus genomes were obtained through Megahit oV MN4822.1 from PoV MN4822.1 from PoV MN4822.1 from Ps BtCoVs . From saPicornaviridae family were detected in all the samples, 6 reads in S-HS, 170 reads in the F-MV pool, and the most in the F-HS pool with 23,858 reads. A near full-length genome and a partial bat picorna viral genome (BtPVs) were assembled from the F-HS pool and further analyzed for phylogenetic relationships. The near full-length BtPV genome  consists of 7033 nt and encodes a long single polyprotein (2207 amino acids) that is flanked by partial UTRs (5′ (336 nt) and 3′ (76 nt)). This genome shared the closest identity of 83% and 94% at the nucleotide and protein levels, respectively, to a BtPV (KJ641697.1) identified in Nyctalus valutinus bats from China [Shanbavirus genus in the Picornaviridae family and closely with BtPV (KJ641697.1) from China , and these contigs showed the closest protein identity (82–96%) to the non-structural and capsid proteins of a known bat hepevirus (MT815970.1) previously identified in P. nathusii bats from Switzerland and covering approximately 85% of ORF1 in the reference genome, including the RNA-dependent RNA polymerase (RdRp) domain. No contigs related to hepeviruses could be assembled from S-HS and the F-HS pool. The multiple sequence alignment and phylogenetic analysis using the conserved RdRp protein from other hepeviruses in the family showed that the identified sequence clustered with bat hepeviruses and closely with P. nathusii bat hepevirus  and further highlighting the bat’s bio-insecticidal role in the environment. In saliva, many viral reads related to viruses identified in fungi and plants were obtained, such as Alphaflexiviridae, Potyviridae, Totiviridae, and Tymoviridae, which might reflect the plant-based diet eaten by insects. Moreover, viruses from the Astroviridae, Herpesviridae, Paramyxoviridae, Poxviridae, and Retroviridae families, which all have members with zoonotic potential, were identified in this study , birds (Orthohepevirus B), and trout (Piscihepevirus) [Orthohepevirus D viruses. Bats have previously been shown to harbor different hepeviruses, and they are distributed geographically in different species [Hepeviruses are small, non-enveloped, positive sense, single stranded RNA viruses with a genome size from 6.6 to 7.2 kb. These viruses have, so far, been identified only in vertebrate hosts such as mammals (pevirus) . Virusespevirus) . This ge species ,60,61,62In addition, a large number of viral reads and contigs was not assigned to any known viral family (unclassified), and many showed low identity to known viruses. These may represent novel (previously uncharacterized) viruses, and further studies are needed to both genetically and biologically characterize these viruses.P. pygmaeus bat population from two locations in Uppsala, Sweden. Sequences related to 51 viral families and unclassified viruses were identified, and genomes of alphacoronaviruses and picornaviruses were characterized. This study significantly contributes to a better understanding of the global diversity of bat viruses in general. Further studies are recommended to characterize the bat virome from different sample types, bat species, and locations, which will increase our knowledge of the viral diversity, transmission, and evolution of bat viruses, as well as help predict future emerging viruses circulating in Sweden.The viral metagenomic data presented in this study provide an overview of the virome of the Swedish


Positive staining for L1CAM and YAP/TAZ was associated with inferior survival, and YAP/TAZ was related to final liver metastasis. Initial hepatic metastasis was associated with LMOS, especially confluent mass formation, and infiltrative liver metastasis pattern was associated with poor survival. Positive staining for YAP/TAZ and L1CAM was associated with inferior LMOS, and YAP/TAZ was related to final liver metastasis.Breast cancer with liver metastasis (BCLM) frequently cause hepatic failure owing to extensive liver metastasis compared to other cancers; however, there are no clinicopathologic or radiologic parameters for estimating BCLM prognosis. We analyzed the relationship between radiologic and clinicopathologic characteristics with survival outcomes in BCLM. During 2009–2019, baseline and final abdomen computed tomography or liver magnetic resonance imaging of BCLM patients were reviewed. Liver metastasis patterns were classified as oligometastasis (≤3 metastatic lesions), non-confluent or confluent mass formation, infiltration, and pseudocirrhosis. Thirty-one surgical or biopsy specimens for liver metastasis were immunostained for L1 adhesion molecule (L1CAM), Yes-associated protein 1/Transcriptional co-activator with PDZ-binding motif (YAP/TAZ), and β1-integrin. Out of 156 patients, 77 initially had oligometastasis, 58 had nonconfluent mass formation, 14 had confluent mass formation, and 7 had infiltrative liver metastasis. Confluent or infiltrative liver metastasis showed inferior liver metastasis-associated survival (LMOS) compared to others ( Breast cancer is the most common cancer among women in South Korea and globally ,2. Most The liver is a metastasis-permeable organ that functions through various mechanisms ,6. ColorThe etiologies of different histopathologic growth patterns (HGPs) between BCLM and CRLM are poorly understood . During Previous studies focused on gene-level analyses that may explain the selective organ-trophism of breast cancer metastasis and prognosis ,17, but We aimed to analyze the relationship of radiologic and clinicopathologic characteristics with survival outcomes in BCLM. Particularly, we report about the growth pattern of liver metastasis based on contrast-enhanced CT or MRI findings and its association with BCLM prognosis. We performed a pilot study analyzing immunohistochemical markers  possibly associated with the radiologic characteristics and survival of BCLM.From January 2009 to May 2019, the medical records of patients with histologically confirmed MBC were retrospectively reviewed in Seoul St. Mary’s Hospital, Catholic University of Korea. Patients diagnosed with liver metastasis based on radiologic studies, such as contrast-enhanced abdominal CT or liver MRI, were enrolled in the study. All patients received systemic treatment for MBC. Other inclusion criteria were as follows: (1) lesions that could be evaluated based on Response Evaluation Criteria in Solid Tumors, version 1.1; (2) Eastern Cooperative Oncology Group performance status of 0–2; and (3) adequate bone marrow and renal function.This study was approved by the Institutional Review Board (IRB) of Seoul St. Mary’s Hospital, Catholic University of Korea (KC19SESI0001). The information of the medical records of patients were adequately coded and stored as file with security password. The requirement for written informed consent was waived according to the IRB’s decision.BCLM was diagnosed using iodine contrast-enhanced abdominal CT or gadoxetic acid (Primovist)-enhanced liver MRI. BCLM was classified into five categories based on the number and configuration of the metastatic lesions: oligometastases (≤3 metastatic hepatic lesions with clear margins), nonconfluent metastases (≥4 nonconfluent metastatic hepatic lesions), confluent metastasis (hepatic metastasis with confluent nodules), infiltrative liver metastases , and pseudocirrhosis (development of a diffuse nodular contour of the liver with widespread hepatic metastases) ,21,22,23HER2 copy number: ≥6.0 signals/cell) ..27]. Forp-values are presented for all analyses, with p < 0.05, which was considered statistically significant. R ver. 3.6.3  was used for all statistical analyses.Liver metastasis-associated survival (LMOS) was defined from the initial diagnosis of liver metastasis through radiologic studies conducted on a date near the patient’s death, or during the last follow-up date. Continuous variables are presented as median values, and categorical variables are presented as percentages. Continuous variables were compared using the Mann–Whitney U test, whereas categorical variables were compared using the chi-square test and Fisher’s exact test. Survival analyses were performed using the Kaplan–Meier method and compared using the log-rank test. Hazard ratios (HRs) for LMOS were estimated using a Cox proportional hazards model with a 95% confidence interval (CI). Two-sided Between January 2009 and May 2019, 156 patients with breast cancer who were diagnosed with liver metastasis were enrolled. The median follow-up duration in the study was 59.37 months (range: 0.43–161.03 months). Baseline patient characteristics are described in The percentage of liver metastasis pattern was calculated based on the total number of patients. In each initial liver metastasis column, the percentage of patients was calculated based on the number of patients included in each liver metastasis pattern.Half of the patients  were classified as having luminal B breast cancer. There was no significant association between the molecular subtype and initial or final liver metastasis. Most patients had multiple metastatic sites, including the liver, when diagnosed with liver metastasis irrespective of the molecular subtype .p = 0.001) . The initial presentation as oligometastasis or nonconfluent mass-forming liver metastasis was associated with superior LMOS compared with confluent mass-forming or infiltrative liver metastasis pattern  A. Based = 0.001) B. A smal= 0.001) .p = 0.001). TNBC or HER2-positive breast cancer (BC) also showed poorer LMOS than luminal BC . When adjusted for molecular subtypes, the pattern of initial liver metastasis still showed a significant association with LMOS   .p = 0.006; p < 0.001; In 31 evaluable patients with adequate tissues, the expression patterns of L1CAM, YAP/TAZ, and β1-integrin were analyzed. Although the evaluable sample size was small in number, a positive expression of L1CAM was associated with inferior LMOS when compared with its negative expression   . HoweverIn the present study, we have shown that the radiologic pattern of liver metastasis may reflect the prognosis of BCLM. We also discovered that positive staining for L1CAM and YAP/TAZ in BCLM cells was related to LMOS, although small in number.In this study, the baseline metastatic pattern assessed using liver CT or MRI showed a significant association with LMOS. Not all patients with BCLM died from hepatic dysfunction in the study. However, there was a borderline association between baseline liver metastasis pattern and the cause of death. Extensive initial liver metastasis was significantly associated with poor hepatic function, as represented using CPS. There were trends in the relationship between the liver metastasis pattern and the time point of presentation of liver metastasis during the diagnosis of metastatic BC. Based on our analysis, we hypothesized that the initial pattern of liver metastasis may play a role in predicting the clinical course of patients; therefore, clinicians can consider these radiologic findings while selecting treatment options such as chemotherapy regimens.In addition to the initial liver metastasis pattern, molecular subtype showed a significant association with LMOS in the analysis. Breast cancer molecular subtype is one of the major established prognostic factors in breast cancer. However, the initial liver metastasis pattern still showed a significant association with LMOS when adjusted for molecular subtype in the multivariate analysis. Previous pivotal clinical trials suggest that the presence of liver metastasis is a poor prognostic factor in breast cancer . Not simPrevious studies reported that luminal B, HER2-enriched, or ER-expressing BC is associated with liver metastasis ,30. HoweMetastasectomy in CRLM is a standard treatment option; however, the results of local treatment such mas metastasectomy or radiofrequency ablation (RFA) for BCLM are controversial ,32. AlthAs previously mentioned, acquisition of the hepatic tissue from BCLM patients is limited and there are relatively few studies assessing whether the pathologic characteristics of BCLM are reflected in radiologic findings. We hypothesized that the expression pattern of adhesion molecules in hepatic tissues might be associated with the clinical growth pattern of BCLM represented by radiologic findings. We focused on YAP/TAZ as one of the contributing factors for perivascular proliferation, extravasation, and spread of tumor cells in the hepatic environment . A recenThis study has some limitations. Although the medical records of the enrolled patients were extensively reviewed, there were some missing data in terms of laboratory findings and incomplete assessment of hepatic function as patients showed progression during treatment. Furthermore, there was a limited number of archival BCLM tissues available for analyses, whose results require careful interpretation. However, considering there are relatively few studies focusing on the radiologic and pathologic characteristics of BCLM, our study’s strengths lie in the comprehensive description of the clinical characteristics, survival outcomes, and radiologic as well as pathologic characteristics of BCLM from diverse viewpoints. Recruiting a larger cohort of BCLM patients based on a multicenter study and extending the pathologic analysis at the genetic level may provide more detailed in-depth results in the near future.Initial liver metastasis based on radiologic findings may reflect the prognosis of BCLM patients. Infiltrative tumor growth represented in the radiologic study was associated with poor prognosis. In fewer cases, the positive staining of YAP/TAZ in BCLM cells was associated with the final radiologic features of BCLM, and the positive staining of L1CAM was associated with inferior survival. Based on a non-invasive radiologic study, clinicians may predict patient prognosis and therefore select a certain systemic treatment suitable for each patient.


Accordingly, regulation of RNA abundance, processing, and modification can have a profound impact on cellular and organismal biology. In recent years, significant progress has been made to decipher the complex molecular details and functional interplay between RNA and regulatory processes have begun to emerge. These studies have shed light on how perturbation to these mechanisms can yield aberrant biology, including formation of disease states such as cancers. In this Research Topic “RNA-Mediated Epigenetic and Transcriptional Regulation,” several articles highlight the importance of RNA-mediated regulation in different pathological contexts such as cancers and retinal degeneration, and developmental/adaptation processes such as sperm development, resistance to pathogens, and adaptation to cold. Altogether, this Research Topic further highlights the importance of RNA as an effector molecule in biology and underscores its biochemical versatility as a regulatory molecule.RNA plays major regulatory roles in many processes beyond transcription, including protein complex formation, maintenance of genomic stability, and establishment of higher-order cellular structures 6-methyladenosine (m6A) is the most prevalent, conserved and abundant co-transcriptional modification observed in eukaryotic mRNAs. It plays important roles in splicing, translation, RNA stability, and higher-order RNA structures  AML cohort and identified seven long non-coding RNAs (lncRNAs), whose expression not only correlated with at least one m6A regulator, but also could be used as prognostic markers for AML and its immunotherapy response in patients. By using matched tumor and normal tissues Huang et al., performed m6A sequencing to generate an m6A map of AEG. Their work identified several AEG-specific m6A sites on important cancer-related mRNAs, suggesting their importance in this cancer. Finally, Liu reviewed m6A modification and its potential regulatory role in bladder cancer proliferation and infiltration. Altogether, these articles showcase m6A dynamics and its potential impact on a disease state such as cancer.Nructures  among otDang et al., assessed the differences in the transcriptomes and m6A profiles of two distinct cattle breeds, differentiated based on their muscle composition. They identified several differentially m6A-modified mRNAs important in muscle development and related pathways. These data suggest that differential m6A modification of these mRNAs could play a role in the distinct muscle profiles found in these cattle breeds. In another report, Liu et al. investigated the role of m6A modification in bovine spermatogenesis during three developmental stages . Their analyses revealed important genes and developmental pathways display differential RNA expression and m6A profiles at distinct ages, correlating m6A regulation with sperm development.Recent studies have shown that m6A modification also plays important roles in organismal development. Wei et al. performed RNA-seq on tissues obtained from three different mouse models for inherited retinal degeneration (RD) and identified important regulatory networks such transcription factors, lncRNAs and circular RNAs, whose expression change in RD. In another study Zheng et al., performed transcriptomic and metabolomic profiling of a yak breed during the cold season and generated an integrative transcriptome and metabolome map revealing several differentially regulated metabolic pathways which could help the animals survive long periods of malnutrition and cold temperatures.RNA regulation also has a profound impact on development and tuning the metabolic state of organisms, especially in response to stress. Focusing on retinal development, Yang et al. investigated the role of miRNAs in pesticide resistance of the diamondback moth, which is one of the deadliest and most destructive pests worldwide. By performing small RNA sequencing from the guts of drug-resistant and sensitive moths, the authors identify dozens of differentially expressed miRNAs whose predicted target genes may be involved in pesticide resistance.MicroRNAs (miRNAs) constitute a highly conserved family of eukaryotic regulatory small non-coding RNAs, typically 19–24 nucleotides in length, formed by cleavage of endogenous hairpin non-coding RNAs. They mediate silencing of the complementary transcripts by promoting degradation or inhibiting translation of the complementary transcripts. They function in a myriad of developmental and pathological processes. Further implicating the importance of RNA-mediated gene regulation, Altogether, these descriptive studies further establish the link between RNA and its regulatory potential in governing a wide array of biological processes. They also underscore the importance of answering several outstanding mechanistic questions about how cells harness the unique biochemical properties of RNA to mediate mechanisms impacting many important biological activities. Indeed, such molecular insight will not only extend our understanding of the basic biology of cellular processes, but also help us develop potential therapeutics for several diseases.


Photo-induced cross-linking is a mainstay technique to characterize RNA-protein interactions. However, UV-induced cross-linking between RNA and proteins at “zero-distance” is poorly understood. Here, we investigate cross-linking of the RBFOX alternative splicing factor with its hepta-ribonucleotide binding element as a model system. We examine the influence of nucleobase, nucleotide position and amino acid composition using CLIR-MS technology (crosslinking-of-isotope-labelled-RNA-and-tandem-mass-spectrometry), that locates cross-links on RNA and protein with site-specific resolution. Surprisingly, cross-linking occurs only at nucleotides that are π-stacked to phenylalanines. Notably, this π-stacking interaction is also necessary for the amino-acids flanking phenylalanines to partake in UV-cross-linking. We confirmed these observations in several published datasets where cross-linking sites could be mapped to a high resolution structure. We hypothesize that π-stacking to aromatic amino acids activates cross-linking in RNA-protein complexes, whereafter nucleotide and peptide radicals recombine. These findings will facilitate interpretation of cross-linking data from structural studies and from genome-wide datasets generated using CLIP (cross-linking-and-immunoprecipitation) methods. Although UV-induced cross-linking is a widely used method to study RNA-protein complexes, the cross-linking reactions are poorly understood. Here, the authors show that π-stacking interactions between nucleobases and aromatic amino acids play a key role in the cross-linking process. In order to understand fully the structure-function relationship of an RBP, it is necessary to identify to which RNAs it binds in vivo, and how non-covalent interactions occur in the binding site. RNA-protein binding occurs at conserved RNA binding domains, such as RNA recognition motifs (RRM), heterogeneous nuclear ribonucleoprotein (hnRNP) K-homology domains and zinc finger (ZnF) domains5. These domains recognize short, usually single-stranded regions of 3–8 nucleotides (nt) known collectively as consensus RNA binding elements (RBE)7 that often contain degenerate positions. Additional binding affinity and selectivity can be generated via supplementary contacts between the RNA and the protein8; for example, the RBP FUS has a bipartite binding mode comprising its ZnF domain and its RRM9. RNA-protein binding has also been observed with proteins that lack canonical RNA binding domains (RBDs)1. Taken together, these features render difficult the prediction of an RBP’s substrates based only on a computational search for its consensus RBE. Indeed, recent studies of the RBFOX protein family showed that only one-half of the isolated RNA targets contain the RBFOX consensus binding motif and that other motifs presumably are responsible for some of its splicing activities11.The human genome encodes more than 1500 RNA binding proteins (RBPs) that regulate key processes, including translation, localisation, stability and splicing15. For example, by combining UV cross-linking with mass spectrometry approaches, proteins bound to given RNAs can be identified21. Conversely, UV cross-linking and immunoprecipitation (CLIP) protocols are commonly used to identify RNA-binding sites for given proteins on a transcriptome-wide scale26. Technical advances constantly improve these techniques28, however, a long-standing challenge in structure/mechanism-oriented studies is to identify the points of cross-linking on both the RNA and the protein with site-specific resolution. Recently, we  introduced cross-linking of segmentally isotope-labelled RNA and tandem mass spectrometry (CLIR-MS), which identifies the sites of amino acid/ribonucleotide cross-links in a single protocol28.Many state-of-the-art methods to identify RNA-protein interactions in vivo employ RNA-protein cross-linking induced by UV light31. Reactions involve mainly uridines and guanosines33, but most amino acids can participate18. Nevertheless, cross-links typically only occur at specific positions in the RNA-RBP motif, for which there is currently no mechanistic rationale34. Moreover, it has proven difficult to investigate and identify factors that promote cross-linking, largely because i) the RNA-protein binding site environment, which is critical for cross-linking chemistry, cannot be simulated in simple solvents, and ii) the chemistry usually produces complex product mixtures that are difficult to characterize on a background of protein and nucleic acid UV damage35.The photo-induced reaction between amino acids and ribonucleotides occurs between free radical species at “zero distance”RRM) and its RNA consensus binding motif U1G2C3A4U5G6U7 (FOXRBE) as a model system, exploiting the high affinity of the complex forms and its well-characterized NMR structure36. We introduce 13C-labelled ribonucleotides into the FOXRBE heptanucleotide and use CLIR-MS to identify RNA-protein cross-links with site-specific resolution. Cross-linking on the protein clusters at amino acids around two phenylalanines, consistent with previous findings37. However, with few exceptions, it only occurs on the RNA at U1, G2 and G6. We then employ site-specific mutagenesis to probe systematically the influence of nucleobase, nucleotide position and amino acid composition on the cross-linking profile. This reveals that cross-linking only occurs with guanosine or uridine at three of the seven nucleotide positions, and only when bases are stacked to aromatic amino acid side chains. Remarkably, this primary stacking interaction is required for neighbouring amino acids to participate in cross-linking. We identify and confirm the importance of this structural feature in selected published examples from other groups as well with an unbiased analysis of three large datasets, suggesting that it is of primary importance for zero-length cross-linking in native RNA-protein binding sites. Moreover, we expect that this finding will facilitate the interpretation of RNA-protein cross-linking data, especially for non-canonical binding motifs. It will also help guide the design of future cross-linking experiments and will aid the development of new tools for de novo motif discovery (see ref. 33).Here, we investigate the structural requirements for the cross-linking of an RNA to its RBP partner. We use the RRM domain of the RBFOX family . This does not allow site-specific nucleotide labelling which is needed to unambiguously assign the reactive nucleotide. A second limitation is the nuclease digestion step, which typically produces short oligonucleotides  and is probably less efficient on nucleotides that are structurally changed by cross-linking17. In this study, we implemented chemical solutions to help circumvent these problems; we employed 13C-labelled phosphoramidites  the nucleobase, ii) its positions in the RBE, and iii) how it is affected by amino acid composition in the binding site. We were mindful of the fact that mutating sites in the RNA and the protein might alter the mode of (or even abolish) RNA-protein binding, and therefore for each mutant we measured the binding affinity to FOXRRM using surface plasmon resonance spectroscopy (SPR).We employed a systematic approach in an effort to identify key structural requirements for cross-linking of RBFOX to RNA. We first used 13C-labelled versions of FOXRBE and confirmed the correct incorporation of the label by liquid-chromatography mass spectrometry (LC-MS) . We incubated each version of FOXRBE with FOXRRM and performed the CLIR-MS protocol. The mass analysis identified short oligonucleotide fragments cross-linked to peptides in clusters close to F126 and F160 . We noted that all (>99%) of the detected fragments contained at least one uridine or guanosine, consistent with literature reports39 that uracil and guanine mainly participate in cross-linking. Here, the use of alkaline hydrolysis for RNA digestion proved advantageous, since it digests a larger fraction of the RNA to mononucleotides  Fig. . We incu160 Fig. . Each olsee Fig. , thereby G6 Fig. . The abs2 and G6 was consistent with published CLIP data40  or U7, consistent with our hypothesis that strict structural parameters govern the photo-induced reactions between FOXRRM and FOXRBE. Isolated cross-links were also observed in some of the spectra of Fig. 1G2 in the binding pocket occupied mainly by U5G6. Likewise, cross-links around F126 in the lower panels of Fig. 5G6U7. One cross-link from P125 to cytidine is visible in Fig. 4 did not cross-link to FOXRRM .The largest number of spectra corresponded to U1 reacting with P125 and F126, and to a lesser extent with I124 and R127 , as well as with neighbouring amino acids at positions 158-164; F158 contacts the ribose of G6. Notably, several close RNA-protein contacts that are visible in the NMR structure (i.e. C3 interacting with F126 (but not stacking), G6 stacking with R194 and U5 stacking to H120)36, did not produce extensive cross-linking.The cross-links of UE36 Fig.   the chemical reactivities of the nucleobases and the amino acids, and ii) the relative positioning of the reactive pair. By mutating selected nucleotides and amino acids in the binding pocket, we created a cross-linking structure-activity relationship for the FOXRRM-FOXRBE interaction.The current understanding of RNA-protein cross-linking is that close contact between nucleotides and amino acids is the main pre-requisite for a cross-linking eventRRM using SPR. In this assay, parent UGCAUGU bound strongly to FOXRRM with a Kd = 4.1 nM. Substitution of the 5’-uridine reduced the strength of the interaction by 4-6-fold for the three variants   Fig. . This wa126 Fig. .Fig. 2FORRM and irradiated the complexes with increasing doses. Work-up and analysis by SDS-PAGE for the three NGCAUGU mutants revealed a new slow-migrating band on the gels, similar to that of the wild-type FOXRBE (N = U), consistent with RNA-protein cross-linking . In contrast to U1, cross-linking hardly occurred at A1, G1 or C1  is present, we performed CLIR-MS on six additional positional FOXRBE mutants. Thus, we exchanged *U for C3 and A4 in FOXRBE , and *G for C3, A4, U5 and U7 . In each case, we first confirmed that the mutants bound and cross-linked to FOXRRM using SPR and SDS-PAGE gels  was particularly surprising given the close proximity of C3 to F126 in the NMR structure.In order to determine systematically the propensity for cross-linking at each position in FOXels Fig. . Remarkaels Fig. . The lac2 and G6 in wild type FOXRBE cross-linked to FOXRRM, guanosine did not cross-link efficiently at any other of the other five locations in the FOXRBE. Similarly, uridine readily cross-linked to FOXRRM from position N1 - where A, C and G were unreactive - but not from the four other locations in the FOXRBE. Taken together, the data from this controlled model study confirmed that RNA-protein cross-linking events have strict requirements, beyond simply the proximity of a reactive nucleotide and a reactive amino acid.In summary, while GAnalysis of the aforementioned CLIR-MS data Figs. , and S6 RRM to histidine, tyrosine and leucine. An effort to perform CLIR-MS on a tryptophan mutant failed because of protein precipitation. We have previously shown using SPR that aromatic amino acids at position 126 are crucial for binding FOXRBE , although a sterically-fitting aliphatic amino acid such as leucine can partially substitute for the phenylalanine (F126L: Kd = 374 nM)36. We irradiated these variants in the presence of FOXRBE. All three protein mutants cross-linked to FOXRBE, as evident from SDS-PAGE  or CG dinucleotides (turquoise) in Fig. 2 is disturbed in this particular complex. Unexpectedly, a H120 cross-link occurred with the three FOXRRM mutants, which was hardly observable in the wild type FOXRRM , and comprised uridines, as reported by ref. 28. Correlating these observations with the NMR solution structure of PTBP1 bound to short poly-pyrimidine sequences (CUCUCU)44, confirmed that these amino acids were indeed all π-stacked to pyrimidines   might occur at π-stacked cytidines during cross-linking or in sample work-up/analysis. Although the cytosine group itself is stable to the conditions used to fragment RNA by base hydrolysis or enzyme digestion (refs. 47), the exocylic amino group of cytidine is more susceptible to hydrolysis when its 5–6 double bond is reduced, i.e. in dihydrocytidine49  produced large numbers of cross-links that - consistent with the NMR structure - could only have derived after C-to-U conversion; for example, AU and AUGU from UGCACGU, bound to H120; and AUUU and UUU from UGCAUCU, bound to F160  of encephalomyocarditis virus (EMCV)DC) Fig. , with crDC) Fig. . Intrigue49 Fig. . Since c N7 Fig. . Indeed,160 Fig. . Taking 18O-RNA labelling and targeted mass spectrometry were used to localise the cross-link of U11 in a let-7 microRNA to a π-stacked phenylalanine (F55) in the LIN28 cold shock domain  underwent UV-cross-linking, most likely with the same uracil but not unambiguously proven by nucleotide labelling. For example, Panhale et al. report a cross-link between F19 and a uridine in hnRNPC, from which the NMR structure with poly-U sequences confirms the π-stacking interaction with F1954  isolated from human and yeast cells18. By correlating their cross-linking data from ribosomal yeast protein S1 with the crystal structure of the protein (PDB ID: [4V88]) 56. Bae et al. showed that tyrosines Y325  protein all cross-link with RNA17; the crystal structure of spCas9 shows that all three residues are π-stacked to uridines or guanosines57.Next, we sought to confirm the importance of π-stacking to RNA-protein cross-linking in datasets that were generated using techniques other than CLIR-MS. Thus, we searched for structurally well-characterized examples in literature that would speak to the generalization of our findings. A unique strength of the CLIR-MS technique is that in many cases it is possible to simultaneously identify both the precise amino acid and the ribonucleotide in a cross-linked fragment. Indeed, we identified only one published example where cross-linking at both the ribonucleotide and the amino acid were unambiguously defined by isoptopic labelling, and where these sites could be mapped to a high resolution structure. In this case, ain Fig. 50–52. On954 Fig. . Kramer 8]) Fig. 55, we coRNA Fig.   and if yes, whether said aromatic side-chains π-stack to nucleobases.These well-characterized, selected examples already provided supporting evidence for the generality of our findings. However, the aforementioned examples of Kramer et al. and Bae et al. were extracted from large well annotated datasets in which, collectively, more than 100 RNA-protein cross-links from a wide variety of RBPs are catalogued. These datasets therefore offered an opportunity to analyze in an unbiased fashion the putative link between π-stacking and cross-linking. The two proteome-wide datasets reported by Kramer et al. each comprise approximately 60 RNA-protein cross-links generated from affinity-captured nuclear pre-mRNAs from human cells18 details 60 cross-links to approximately 35 proteins, with 37 cross-links that are localized on defined amino acids, in mostly RRM binding domains . In addition, the lack of structural information for several cross-links in the human RNP and spCAS9 datasets may have prevented an even higher correlation. Finally, it is also apparent that a π-stacking interaction is not a strict requirement for all cross-linking events. Cysteine, which is prone to cross-linking, does not require a π-stacking interaction in order to produce a long-lived, highly reactive radical43. This is consistent with the lack of aromatic amino acids proximal to cysteine-containing cross-links in the KH domains of proteins in the yeast and human RNP data-sets18. Thiol-containing molecules present in buffer may also initiate UV-induced cross-linking of proteins and nucleic acids58. In addition, recent publications described cross-linking of dsDNA to histones using conventional cross-linking59, where π-stacking of the side chain is more difficult to envision because of the double-stranded helical structure; although this may partly explain why double-stranded oligonucleotides are reported to cross-link less efficiently than single-stranded oligonucleotides61.Taken together with the aforementioned specific examples from literature and our analysis of the FOX62, but less thoroughly investigated for RNA-protein interactions41. However, a description of the photo-induced intramolecular cyclization of 5-benzyluracil and 6-benzyluracil via benzyl and uracil radical intermediates suggests a plausible model for the cross-linking of U1 with F126  . Subsequent protonation of the uracil radical anion can yield a neutral α-hydroxy radical43, whereas ready deprotonation of the F126 radical cation will produce a stabilized benzylic radical. In the absence of oxygen, the major fate of these free radicals is recombination with the formation of the direct U1-F126 cross-link . An analogous mechanism has been proposed for the reaction between uracils/halogenated uracils and tyrosine derivatives63.Free radical reactions of nucleic acids and proteins have been well studied in the context of oxidative damage and electron transfer126 Fig. 35. Hence43, or via oxidation of amide carbonyls 64, yielding free radicals at peptide α-carbon sites on the protein backbone. Viehe et al have proposed that α-carbon radicals are especially stabilized thermodynamically by capto-dative effects, i.e. simultaneously by electron-withdrawing (-C = O) and electron-donating (-NR2) groups65 and, furthermore, that they readily combine with other radicals.Alternatively, the F126 radical, or radical cation, may rearrange to neighboring amino acids in processes mediated by hydrogen atom abstractions1 may form, yielding products that are identified by mass spectral analysis after controlled digestion , this may not necessarily correlate with the measured redox potentials of isolated nucleotides or aromatic amino acid side chains. Together, our observations demonstrate the importance of local environment to cross-linking in the RNA-protein binding site, and at least partly explain why cross-links occur only at specific positions in an RNA-RBP motif.Hence, depending on the lifetimes and the locations of these radicals on the protein backbone, “indirect” cross-links to U G2 Fig. , it seemFor a complete understanding of the roles that RBPs play in cellular processes, it is necessary to understand at the atomic level how RNA binding domains in proteins engage with RNAs. RNA-protein interactions are generally characterized in two main ways in vivo: isolating proteins and sequencing the bound RNAs (CLIP methods), and identifying proteins bound to RNAs, for example, by mass spectrometry. Most of these approaches rely upon photo-induced cross-linking, which provides direct evidence of binding under native conditions. However, presently, native cross-linking-based methods suffer from two drawbacks: i) it is challenging to identify simultaneously sites of cross-linking on the RNA and protein, ii) cross-linking in an RNA-RBP motif typically proceeds inefficiently and in an unpredictable fashion. Therefore, any progress that furthers our understanding of this chemistry is of high value.28 employs isotope-labelled RNAs to resolve amino acid/ribonucleotide cross-links in a single protocol, whereby segments of labelled RNA are produced by in vitro transcription prior to ligation-assembly into a full-length RNA. In this study, we have broadened the application of CLIR-MS through the use of chemically synthesized 13C-labelled RNAs. This enables site-specific incorporation of labelled nucleotides into the RNA. After irradiation of the RNA-protein complex, and controlled digestion to nucleotide-peptide adducts, the locations of cross-linked nucleotides are pinpointed site-specifically. We demonstrated this methodological advance with a study of the interaction of the RRM domain of the RBFOX family bound to its consensus binding element (U)GCAUGU, for which we have previously determined an NMR structure36 and studied cross-linking37. Photo-irradiation of the FOXRRM-FOXRBE complex led to key observations with potentially wide-ranging implications: 1) strong cross-linking occurred between U1, G2 and G6 with clusters of amino acids centred around the phenylalanines F126 and F160; 2) very little cross-linking was observed at other uridines in the parent or a mutated FOXRBE; and 3) amino acids that flank F126 and F160 also cross-linked efficiently to U1, G2 and G6, but not to other nucleotides of FOXRBE. Since the NMR structure of FOXRRM-FOXRBE36 shows that U1 and G2 π-stack to F126, and that G6 π-stacks with F160, the data suggested that a π-stacking interaction is a requirement for cross-linking events in an RNA-protein interaction, at least for this RRM domain. Indeed, other aromatic side chains could substitute for F126 in cross-linking, but incorporation of leucine abolished direct and almost all indirect (flanking) cross-linking to U1/G2. Other researchers have noted in passing the increased presence of aromatic amino acid side chains in UV cross-linking datasets (see refs. 68), but have not to our knowledge recognized its role as a trigger for cross-linking, nor distinguished between direct and indirect cross-link events. We validated our results on the RBFOX system with the correlation of published cross-linking and structural data from CLIR-MS data generated with the PTBP1 protein, and selected examples from LIN28, hnRNPC, U2AF, ribosomal yeast protein S1 and bacterial spCAS9, that were produced using different methods. Our findings were further strengthened by an unbiased analysis of more than 100 cross-links in large-scale data-sets comprising various RNA-binding domains, where in one case up to 78% of the cross-links showed π-stacking to a proximal aromatic amino acid side chain. It is clear that factors in addition to π-stacking also contribute to cross-linking events in RNA-protein sites, including efficiency of the photo-induced electron transfer between nucleobase and amino acids, the ability to stabilize free radicals, the flexibility of the structure to adopt to the configurations that are required for the radical reactions69 and the proximity of reacting radical pairs70. Furthermore, our findings do not explain all RNA-protein cross-linking reactions, including those involving cysteine, which is highly photoreactive and prone to cross-link probably due to the high reactivity of the thiyl radical43.The CLIR-MS method15.The major findings in this study were enabled by the combination of site-specific labeling with the CLIR-MS protocol, which together provides enhanced knowledge of cross-linking sites at single-nucleotide and amino acid resolution. These included the surprising discovery that cytidine residues which are π-stacked to aromatic residues can undergo partial hydrolysis during photo-induced cross-linking. This observation may explain discordance in some cases between structural- and cross-linking data. Furthermore, the hydrolysis of cytidine should be anticipated in the analysis of CLIR-MS data and may also be relevant to the interpretation of data from CLIP experiments, which is currently an area of intense activity37. However, the method described here requires the use of chemically synthesized, isotope-labelled RNA and is currently restricted to the study of purified individual RNA-protein complexes. Nevertheless, the data produced can aid the interpretation of that from unbiased complex systems. For instance, our findings extended the knowledge on the role of the local environment to cross-linking in the binding site, i.e. beyond the simple proximity of photo-reactive nucleotides and amino acids. This helps at least partly to explain why cross-links occur only at specific sites in an RNA-RBP motif. Furthermore, the localization of π-stacking interactions will aid the interpretation of proteome-wide datasets, for example in cases where proteins lack canonical RNA-binding domains, and in the analysis of CLIP datasets. Thanks to the inherent variations in the ways that RBPs recognize their RNA targets, predictive modeling of RBP selectivity is extremely challenging; our findings can be implemented into the development of new tools71 for de novo motif discovery.CLIR-MS technology is inherently flexible and we are exploring further improvements to the method17). A fuller understanding of how RNA binding domains in proteins engage with RNAs can support the development of new methods of targeting RBPs via the RNA binding site72.In a broader sense, the RNA-binding sites of RBPs have garnered attention in the context of disease and drug targeting; for example, the RNA binding site in the intrinisically disordered region of TDP43 contributes to its aggregation in amyotrophic lateral sclerosis . After washing with buffer 50 mM Na2HPO4, 3 M NaCl, pH=8, the protein was eluted with a step gradient of imidazole (40–500 mM). The purest fractions as judged by 5–20 % SDS–PAGE were combined, and the column was repeated. Pure fractions were dialyzed against 5 L 20 mM NaCl, 10 mM NaH2PO4, pH=6.5 overnight. The identity of the FOXRRM and its mutants was confirmed using LC-MS/MS measured by top-down analysis and data analysed using ToPIC74.The FOXRRM, the 15-amino acid E. coli biotin ligase recognition sequence GLNDIFEAQKIEWHE was introduced between the TEV cleavage site and the gene encoding FOX-1 using standard PCR mutagenesis. E. coli protein ligase BirA was cloned, expressed and purified as previously described75. The generation of biotinylated FOXRRM was achieved in a 10 ml batch-mode cell-free synthesis reaction which was conducted for 3.5 h in presence of 2 μM BirA and 400 μM D-biotin76. The proteins were purified as described above and the biotinylated proteins were cleaved overnight at 4 °C with 0.5 mg TEV protease76.For the biotinylated FOXThe synthesis of all oligonucleotides was carried out with the MM12 synthesizer  on a 50 nmol scale using 500 Å UnyLinker CPG using standard conditions. Synthesis conditions, purification methods and characterisation . For coating, the amine chip was first treated with PBS buffer at a flow rate of 12.5 μl/min at a pH of 7.5. Next, a solution of 1 M NaCl and 1 M NaOH was injected to all 16 channels for 2 min. Afterwards, 100 μl of a mixture of 200 mM EDC and 100 mM NHS was added. For coating of the streptavidin, an acetate buffer (10 mM sodium acetate) at a pH of 5.5 was used and a 100 μl injection resulted in an approx. response of 2500 RU. The running buffer was switched to a HEPES buffer  Tween 20) before capturing the analyte. Approximately 10 μl of a 75 nM solution of biotinylated FOXRRM and the desired RNA were prepared by mixing both components in equimolar ratios at the desired concentration of 10 µM in 10 mM sodium phosphate (pH = 6.5) and 50 mM NaCl and incubated for 10 min on ice. 15 μl of the sample solutions were placed in a 96 well-plate on ice and irradiated at 800 mJ/cm2, 1600 mJ/cm2, 2400 mJ/cm2 and 3200 mJ/cm2 at 254 nm in a CL-1000 Ultraviolet Crosslinker . The samples were then loaded on a 4-20% Tris-Glycine SDS-Gel with a 1xTris/Glycine/SDS running buffer. The gels were stained using the Pierce Silver Stain Kit and uncropped pictures of the gels can be found in the Supplementary Information.Complexes of FOX13C-labelled RNA and irradiated four times with 800 mJ/cm2 as described above. Each irradiation step was separated by 1 min for sample cooling. After irradiation, samples were precipitated with 3 volumes of ethanol at −20 °C and 1/10 volumes 3 M sodium acetate (pH 5.2), left at −20 °C for at least 2 h, and centrifuged at 4 °C for 30 min at 13,000 × g. Resulting pellets were washed by brief vortexing in 80% ethanol at −20 °C, and centrifugation was repeated. For the digestion with alkaline hydrolysis: Pellets were air dried for 10 min, then were resuspended in 50 μl of 50 mM Tris-HCl (pH 7.9). 1 ml 0.1 M NaOH was added, and the sample incubated at 70 °C for 10 min on a shaking incubator. The sample was neutralized with 105 μl 1 M HCl, cooled on ice, purified using solid-phase extraction and evaporated to dryness in a vacuum centrifuge. The sample was resuspended in 50 μl 50 mM Tris-HCl, pH 7.9, 4 M urea and then diluted with 150 μl 50 mM Tris-HCl, pH 7.9. The exact procedures of the digestion using RNases and trypsin, the enrichment using titanium dioxide affinity chromatography, and LC-MS analysis28 are described in the Supplementary Information. All identified cross-links are listed in Supporting Data 75 µg of RNA-protein complexes were made of equimolar mixtures of unlabelled and Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


Thermus thermophilus, and we quantify the structural effect of the inhibitors.RNA molecules play crucial roles in various biological processes. Their three-dimensional configurations determine the functions and, in turn, influences the interaction with other molecules. RNAs and their interaction structures, the so-called RNA–RNA interactions, can be abstracted in terms of secondary structures, i.e., a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Each secondary structure, in turn, can be abstracted into cores and shadows. Both are determined by collapsing nucleotides and arcs properly. We formalize all of these abstractions as arc diagrams, whose arcs determine loops. A secondary structure, represented by an arc diagram, is pseudoknot-free if its arc diagram does not present any crossing among arcs otherwise, it is said pseudoknotted. In this study, we face the problem of identifying a given structural pattern into secondary structures or the associated cores or shadow of both RNAs and RNA–RNA interactions, characterized by arbitrary pseudoknots. These abstractions are mapped into a matrix, whose elements represent the relations among loops. Therefore, we face the problem of taking advantage of matrices and submatrices. The algorithms, implemented in Python, work in polynomial time. We test our approach on a set of 16S ribosomal RNAs with inhibitors of  Each nucleotide is linked to the next one by a phosphodiester bond, referred to as a strong bond. It can at most interact with another non-contiguous one, establishing a hydrogen bond, referred to as a weak bond, and forming mainly Watson–Crick (G–C and A–U) and wobble (G–U) base pairs. Such a process, known as the folding process, induces complex three-dimensional configurations (or shapes). Each of them is strictly related to the molecular functions. RNA molecules play numerous roles in cellular processes. Usually, they do not act in isolation, but they express their biological roles by interacting with other molecules , includiIn this paper, we face the problem of identifying a given structural pattern into the secondary structure of RNAs and or RNA–RNA interactions. We propose a method that works on patterns and structures characterized by arbitrary pseudoknots. Following the approaches proposed in , 23], w, w23], wThermus thermophilus to quantify the structural effect of the inhibitors.In the literature, the concept of collapsing has already been introduced to reduce the complexity and classify the RNAs in equivalent classes based on topological concepts. From each diagram, it is possible to associate a shadow by removing all the non-crossing arcs and all the unpaired vertices, and then collapsing parallel zigzag arcs into an arc . For exaThe paper is organized as follows. In 2RNA secondary structure can be represented by a diagram. Formally,(Diagram). A diagram is a labeled graph over the ordered set of vertices [ℓ] = {1, …, ℓ}, in which each vertex has degree ≤3, and the edges are all the segments  for i = 1, …, ℓ − 1 and some semi-circular arcs  in the upper half-plane, with 1 ≤ i < j ≤ ℓ.ω is the string that corresponds to the sequence of labels over the ordered set [ℓ] and B is the set of all arcs . In the literature, this notation represents arc annotated sequences. To not introduce many symbols, we denote the arc diagram by the pair  since only arc diagrams are considered in this work. Moreover, a diagram corresponds to an arc annotated sequence, whose nodes have a degree less than or equal to 3. As an example, the structure illustrated in i1, j1) and  are parallel if and only if i1 = i2 − 1 and j2 = j1 + 1 or vice-versa. As an example, the core of the structure in ϵ, {, , , , , }). Note that since the core is obtained by deleting nucleotides, the string ω of the core is empty, ϵ. Similarly, the shadow of the structure in ϵ, {, }). To obtain the secondary structure, core, or the shadow in terms of loops, we define three procedures: loop determination, core determination, and shadow determination. These procedures take as input a secondary structure of an RNA or RNA–RNA interaction encoded as a Bpseq notation, and return a set of loops of arc diagram related to the secondary structure  if the vertices of the relative pairs ,  satisfy the following relation is < js < it < jt. We say Ls is nested into Lt (Ls ⋒ Lt) if it < is < js < jt and we say Ls crosses with Lt (LsLt) if is < it < js < jt. Without loss of generality, we enumerate loops of the structure starting from the loop whose last nucleotide is the most left. In other words, the first loop L1 is formed by the pair  such that j1 is the last paired nucleotide of the structure considering the 5–3′ direction. As a consequence, given two loops Ls and Lt respectively formed by  and , if s < t then by definition js > jt. As an example, we can consider the structure illustrated in L1 = , L2 = , L3 = , L4 = , L5 = , L6 = , L7 = , L8 = , L9 = , and 36 relations among them, i.e., L1L2, L1 ⊙ L3, L1 ⊙ L4, L1 ⊙ L5, L1 ⊙ L6, L1 ⊙ L7, L1 ⊙ L8, L1 ⊙ L9, L2L3, L2L4, L2L5, L2 ⊙ L6, L2 ⊙ L7, L2 ⊙ L8, L2 ⊙ L9, L3L4, L3L5, L3 ⊙ L6, L3 ⊙ L7, L3 ⊙ L8, L3 ⊙ L9, L4 ⋒ L5, L4 ⋒ L6, L4 ⋒ L7, L4L8, L4L9, L5L6, L5L7, L5 ⊙ L8, L5 ⊙ L9, L6 ⋒ L7, L6 ⊙ L8, L6 ⊙ L9, L7 ⊙ L8, L7 ⊙ L9, L8 ⋒ L9.The diagram is denoted by tructure , to the tructure  respectiLs and Lt with s < t, it is equivalent to consider the two pairs of natural number  and  such that is < js, it < jt and js < jt. It follows that js is the greatest number. From the theory of combinations, we have 6 different order relations over is, js, and it, which became 3 considering the constraints is < js and it < jt. For each structure, we can uniquely associate a relation matrix. Each element aij of the matrix is the relation between the loops Li and Lj. The relation matrix of the structure in Taking advantage of such an enumeration, we impose an order on the loops. Each nucleotide can interact at most with another one. As a consequence, each nucleotide can be involved at most in a pair. This means that the choice of a loop is unique. Moreover, the three relations, concatenation, nesting, and crossing, are necessary and sufficient to describe any RNA secondary structure with arbitrary pseudoknots. In fact, given two loops, 3n = |ω| and m = |ω′| with n ≥ m, the APS problem asks whether i, j), whose paired nucleotide j is inside the considered substructure. Furthermore, we do not impose restrictions for the paired nucleotide i. The reason for this choice concerns the nature of the folding process: a nucleotide can perform a hydrogen bond with another already synthesized one. In our formalism, the nucleotide i of the pair  is synthesized before nucleotide j. Operationally, we enumerate the loops in the structure starting from the one whose last nucleotide is the most right, and we extract substructures determined by M consecutive loops Li. An illustration of the APS problem and the one with our restriction is given in We face the problem of searching a given structural pattern into a secondary structure or its abstraction, i.e., core and shape, of RNAs and RNA–RNA interactions with arbitrary pseudoknots. Formally, we address the problem of the arc-preserving subsequence (APS) problem with a particular restriction. Let udied in , 27], [, [D= and returns a matrix, whose element ak,t represents the relation between the loops Lk and Lt. It is computed with time complexity of N is the number of loops in the structure that corresponds to the cardinality of B, i.e., the set of pair list of the arc diagram L1, L2, L3, while the latter is formed by loops, L4, L5, L6. In general, the output of the structural relation matching algorithm is a matrix characterized by M columns and P rows, where M is the number of loops of the pattern and P is the number of occurrences of the pattern in the structure.For each RNA secondary structure represented as an arc diagram, we uniquely determine its relation matrix using In the literature, as mentioned in the Introduction, different approaches have been introduced to extract patterns of RNA molecules using several data structures, including arc-annotated sequences, affix trees, and suffix arrays. In O(n ⋅ m) by considering pairwise relations between loops. It corresponds to the arc-preserving subsequence problem with a particular restriction. In particular, we require that no arcs will be deleted if the last paired nucleotide is inside the considered substructure. This constraint follows the folding process: each nucleotide performs a hydrogen bond with another already synthesized one. In our formalism, the first nucleotide of a loop is synthesized before the last one. This restriction allows us to extract patterns, which correspond to the local substructure taking into account the folding process. This approach has a strong impact on the RNA structures analysis because it can consider the structural formations of the molecule.Our approach allows us to search a given structural pattern into an RNA secondary structure with arbitrary pseudoknots in 4T. thermophilus. The aim is to study the effect of some inhibitors, including antibiotics, tetracycline, hygromycin B. Accordingly, we take into account the RNA molecules with ID PDB_00478, PDB_00408, PDB_00436, PDB_00438, PDB_00589 from the RNA strand database [http://www.rnasoft.ca/strand. In our experiment, we first consider the shadows of the selected molecules . We observe that the same shadow, shown in We test our approach on a set of 16S ribosomal RNAs of database . RNA StrL4, L5, L6, L7, L8 of the other substructures. However, the pattern in L1, L2, L3. In Moreover, we consider the core of these molecules between the 800th and 900th nucleotides. The core of substructure molecules with ID PDB_00478, PDB_00408, PDB_00436, PDB_00438 is shown in 5T. thermophilus to understand the effects of some inhibitors. The Python code is available on https://github.com/michelaquadrini/RNARelationPattern. The results show that the approach can capture the local, intermediate and global structural changes by extracting patterns from the molecule, its core, and its shadow, respectively, and taking into account the folding of RNA molecules. The approach can be applied in different scenarios with different aims.RNA functions depend on their three-dimensional configuration. Understanding the relationship between structure and biological function has been considered one of the challenges in biology. In this work, we have faced the problem of identifying a given structural pattern into secondary structures of RNA and RNA–RNA interactions or their abstractions (cores and shadows) with arbitrary pseudoknots. We have used algebraic operators to formalize such RNA secondary structures and their abstractions as a combination of loops. Moreover, we have defined procedures to represent the secondary structure in terms of loops to determine the core and shadows. Finally, we have defined two procedures: determination of the relation matrix and structural relation matching. The former maps each RNA secondary structure into a matrix and the latter identifies each pattern of the RNA structure by searching for a submatrix. We have implemented the proposed methodology in Python, and we have tested our approach on a set of 16S ribosomal RNAs of Now, we are working on the tool development by improving its computational performance and making it user-friendly for biologists. Moreover, we want to add other molecular encodings as accepted input, i.e., including dot-bracket and CT files. Dot-bracket is a notation used to encode RNA secondary structure topology, and CT is a format that describes molecules and chemical reactions. Both represent the secondary structures of RNAs and RNA–RNAs interactions as well as the Bpseq notation. However, adding all these input types makes the tool more user-friendly by avoiding format change problems due to the non-existence of a universal notation for encoding secondary RNA structures.T. thermophilus and Escherichia coli to evaluate the effects of inhibitors as a function of thermal differences. This evaluation will be carried out in collaboration with experts of the biological domain to test the impact of our approach on biology. In future work, we want to generalize the approach considering the sequences of nucleotides. In other words, we want to face the problem of finding a given structural pattern into an RNA with arbitrary pseudoknots taking into both the primary and secondary structure of molecules. Although functional RNAs exhibit a highly conserved secondary structure with little sequence similarity, the sequence influences the molecular interactions. In other words, the nucleotide sequence plays a role in the study and prediction of the RNA–RNA interaction structures.Moreover, we are analyzing RNAs of 16S ribosomal of


Efficient detection and observation of dynamic RNA changes remain a tremendous challenge. However, the continuous development of fluorescence applications in recent years enhances the efficacy of RNA imaging. Here we summarize some of these developments from different aspects. For example, single-molecule fluorescence in situ hybridization (smFISH) can detect low abundance RNA at the subcellular level. A relatively new aptamer, Mango, is widely applied to label and track RNA activities in living cells. Molecular beacons (MBs) are valid for quantifying both endogenous and exogenous mRNA and microRNA (miRNA). Covalent binding enzyme labeling fluorescent group with RNA of interest (ROI) partially overcomes the RNA length limitation associated with oligonucleotide synthesis. Forced intercalation (FIT) probes are resistant to nuclease degradation upon binding to target RNA and are used to visualize mRNA and messenger ribonucleoprotein (mRNP) activities. We also summarize the importance of some fluorescence spectroscopic techniques in exploring the function and movement of RNA. Single-molecule fluorescence resonance energy transfer (smFRET) has been employed to investigate the dynamic changes of biomolecules by covalently linking biotin to RNA, and a focus on dye selection increases FRET efficiency. Furthermore, the applications of fluorescence assays in drug discovery and drug delivery have been discussed. Fluorescence imaging can also combine with RNA nanotechnology to target tumors. The invention of novel antibacterial drugs targeting non-coding RNAs (ncRNAs) is also possible with steady-state fluorescence-monitored ligand-binding assay and the T-box riboswitch fluorescence anisotropy assay. More recently, COVID-19 tests using fluorescent clustered regularly interspaced short palindromic repeat (CRISPR) technology have been demonstrated to be efficient and clinically useful. In summary, fluorescence assays have significant applications in both fundamental and clinical research and will facilitate the process of RNA-targeted new drug discovery, therefore deserving further development and updating. Discovered in the 1930s, RNA is an essential nucleic acid molecule that participates in genetic information storage, gene expression, and regulation in living cells , microRNFluorescence in situ hybridization (FISH) was first introduced around 1980 . Th. Th77]. Along with the advent of severe acute respiratory syndrome coronavirus 2, also known as SARS-CoV-2, the virus that causes COVID-19, rapid and accurate testing has been vital in slowing its spread. The current gold standard of testing utilizes quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). The basic idea of this method is that viral RNA will be repeatedly copied and duplicated. Fluorescent dyes are added during the copying process, allowing for the amplification and visual detection of viral RNA. The test is extremely accurate, being able to detect just one copy of viral RNA per μL. However, the downside of qRT-PCR is that it requires specialized equipment and, therefore, a centralized lab facility. The run time for the reaction is also several hours. This is why results typically take 1–2 days after collected samples are sent to a lab to be completed.To address these challenges, a clustered regularly interspaced short palindromic repeat (CRISPR)-based technology called Fast Integrated Nuclease Detection In Tandem (FIND-IT) was developed by the Doudna group . As FiguThe tandem use of enzymes is necessary because each enzyme on its own either exhibits reduced efficiency or is unable to produce a detectable signal. For example, when Cas13 is paired to cleave fluorescent dye–quencher pairs, it takes several hours to reach a detectable signal. As for Csm6, recognition of the target sequence normally triggers the synthesis of cyclic tetra- or hexa-adenylates to serve as activators, which are rapidly degraded by the same Csm6. This means that the amount of fluorescent signal Csm6 can generate is limited. Instead, by using both enzymes in sequence, Cas13 is able to cleave a chemically modified activator which is not degraded by Csm6. As a direct result, Csm6 can cleave an indefinite number of dye–quencher pairs, leading to a large, detectable signal from a relatively small amount of substrate. The significance of this assay is that no initial amplification of the viral RNA is necessary, allowing relatively low amounts to be accurately detected. The potential of this technology is immense because, depending on the guide RNA is used, the assay could be programmed to detect a wide array of RNA species associated with different diseases .Overall, this paper summarizes a few developing assays and tools regarding fluorescent probes in combination with RNA, some principles and explanations concerning fluorescence spectroscopy, and the potency and capacities of fluorescence assays in the development of RNA therapeutics in the past few years. However, there are still two significant barriers that need to be overcome. First and foremost, many fluorescence methods are aimed at fixed cells and living cells. The results related to dynamic RNA activities are not as convincing in living organisms. Future efforts could be concentrated on in situ RNA imaging of living animals. Secondly, fluorescence targeting RNA therapeutics are mainly focused on liver cells. In combination with RNA research, an advanced fluorescence assay should be conducted to target cancer or tumor cells in other organs.