Although metagenomics and metatranscriptomics are commonly used to identify bacteria and viruses in human samples, few studies directly compare these strategies. We wished to compare DNA and RNA sequencing of bacterial and viral metagenomes and metatranscriptomes in the human cervix. Total nucleic acids from six human cervical samples were subjected to DNA and RNA sequencing. The effect of DNase-treatment before reverse transcription to cDNA were also analyzed. Similarities and differences in the metagenomic findings with the three different sequencing approaches were evaluated. A higher proportion of human sequences were detected by DNA sequencing (93%) compared to RNA sequencing without (76%) and with prior DNase-treatment (11%). On the contrary, bacterial sequences increased 17 and 91 times. However, the number of detected bacterial genera were less by RNA sequencing, suggesting that only a few contribute to most of the bacterial transcripts. The viral sequences were less by RNA sequencing, still twice as many virus genera were detected, including some RNA viruses that were missed by DNA sequencing. Metatranscriptomics of total cDNA provided improved detection of mainly transcribed bacteria and viruses in cervical swabs as well as detection of RNA viruses, compared to metagenomics. Viruses are also very abundant in the human body, at a similar level as the bacteria4, and they make up the virome. The human virome includes viruses that infect archaea and human cells, bacteriophages that infect bacteria, but also transient viruses. Amplicon sequencing is applicable to analyze organisms that share common genes, like prokaryotes, fungi and eukaryotes that all share common parts in their genomes. Bacterial species present in different specimens for example, are commonly identified using amplicon sequencing of the 16S ribosomal RNA gene5. However, viruses do not share any specific gene or other DNA/RNA sequence and hence, other sequencing approaches than amplicon sequencing are necessary to identify and study them.The number of bacterial cells within the human body is approximately the same as the number of human cells, 106. However, several disadvantages might exist when trying to enrich viruses in a biospecimen: (a) unknown viruses not targeted with the enrichment approach might escape identification, (b) a proportion of viruses might be lost depending on the enrichment method, (c) if the human genome is depleted in the sample in order to enrich for viruses, information regarding the human genome will not be included and possible viral integration may also fall undetected and, (d) RNA viruses will not be identified when using a DNA extraction/DNA sequencing approach.Different viral target enrichment methods like filtration can be used prior massively parallel DNA sequencing in order to study the virome9. With this, all DNA viruses and other microbes present within a specimen (known and unknown) should be detected if sequencing is performed at sufficient depth. Similarly to shotgun DNA sequencing, massively parallel cDNA sequencing from total RNA can be used to explore the metatranscriptomes within a specimen in order to perform microbiome and virome profiling10 in addition to analysis for differential gene expression of both human, microbial and viral genes. Microbiome and virome profiling using RNA sequencing gives the additional information about actively transcribed genes and has also the possibility to detect viruses with RNA genomes, which cannot be detected by shotgun DNA sequencing if RNA is not reverse-transcribed first.Nowadays, one approach to study the whole virome (as well as the human genome) is based on sequencing all the DNA present in a specimen, so called shotgun DNA sequencing13. The most oncogenic HPV types that cause neoplasia and cancer of the cervix are HPV types 16 and 18, but other HPV types are also found in cervical neoplasia16. Cervical squamous intraepithelial neoplasia stage 3 (CIN3), cervical cancer in situ, may continue to develop into invasive cancer if not treated, but it is not entirely clear which factors are involved in the persistence of the infection and the progression into cancer or clearance of the infection. The vaginal microbiota has been characterized in both normal and cervical intraepithelial neoplasia as well as cervical cancer17. Metagenomic studies suggest that only one or a few species of Lactobacillus within the phylum Firmicutes are usually dominating the vaginal microbiota18. However, the diversity tends to increase with neoplasia and HPV-positivity19. The vaginal microbiota may have an important role to protect against harmful infections like for instance HPVs, hence, the cervical microbiota may serve as a potential biomarker for cancer progression risk21.Cervical cancer is caused by persistent infection of oncogenic high risk human papillomavirus (HPV) types22 and early biomarkers with high specificity and sensitivity in order to identify women with low grade neoplasia with high risk of progression into cancer is important. In the present study, the aim was to compare massively parallel DNA and RNA sequencing of total nucleic acid extracted material for a comprehensive detection of the detectable and actively transcribing DNA and RNA microbes in cervical specimens.To eliminate cervical cancer, a combination of broad vaccination programs23. All women 23–64 years of age in this sample collection were diagnosed with CIN2 +  during the years 2013–2015 and identified by linkage to the National Cervical Screening Registry (NKCx), which also has the Center for Cervical Cancer Prevention as responsible organization. A random of 6 cervical samples  were included for the present study from different women (n = 6). The CIN3 samples were matched with the normal samples by age in 5-year bands and calendar year of liquid-based cytology (LBC) test. Ethical approval was granted by the Regional Ethical Review Board of Stockholm, Sweden (EPN-Dnr: 2014/1242-31/4). Written informed consent was obtained from all participants. All methods were carried out in accordance with relevant guidelines and regulations.The sample collection of the Swedish Center for Cervical Cancer Prevention currently holds cervical samples from > 400,000 women, stored under validated methods in − 25 °CIsolation of total nucleic acid from the 6 LBC swab samples was performed using MagNA Pure LC instrument and total nucleic acid isolation kit  in accordance with the manufacturer´s protocol . 100 ul were used for total nucleic extraction and total nucleic acid was eluted in 50 µL elution buffer. The extracted DNA concentration was quantified in all 6 total nucleic acid extracted samples using a fluorometric assay  according to manufacturer´s instructions, and ranged from 0.08 to 2.9 ng/ul (average 1.04 ng/ul).Total nucleic acid extracted material from the 6 samples was thereafter subjected to 3 different approaches to analyze the microbiota: metagenomic DNA sequencing, metatranscriptomic RNA sequencing and metatranscriptomic RNA sequencing including a step of DNase-treatment of the total nucleic acids before RNA reverse transcription to cDNA. In total, 18 DNA/cDNA libraries were sequenced.For DNA sequencing, DNA libraries were prepared directly from the total nucleic acid extracted material from the 6 cervical samples using Nextera XT DNA library preparation kit  following the manufacturers’ reference guide, starting with 1 ng of DNA (as recommended by the manufacturer) and using unique indexed adapters to facilitate pooling of the libraries. The 6 DNA libraries were normalized to 1 nM and pooled prior paired-end sequencing at 1.8 pM for 2 × 150 bp using NextSeq500 .For RNA sequencing, RNA libraries were prepared directly from the total nucleic acid extracted material  from the 6 cervical samples using Smarter stranded total RNA-seq kit v2—pico input mammalian  following the user manual. Starting material was 8 ul as recommended in the guidelines, prior fragmentation for 2 min. After cDNA synthesis, adapters including an index part, unique for each individual sample, were added to the cDNA fragments, following ribosomal cDNA depletion and enrichment of uncleaved fragments by 14 cycles of PCR. The 6 RNA libraries were then normalized to 4 nM and pooled prior paired-end sequencing at 1.2 pM for 2 × 75 bp using NextSeq500 .To observe if there was an effect of DNA presence in RNA sequencing, another 6 RNA libraries were prepared and sequenced as described in the RNA approach, by treating the total nucleic acid with DNase prior library preparation. 8 ul of total nucleic acid extracted material from the 6 cervical samples (recommended input for library preparation) were treated with DNase using Turbo DNA-free kit  according to manufacturer’s user guide in order to remove DNA.24 with default parameters and 18 bp and 36 bp as minimal read length for RNA and DNA sequenced specimens, respectively. The first 3 nucleotides from every R2 read were trimmed for the RNA sequencing fastq-files using cutadapt version 1.18, as advised within the Smarter stranded total RNA-seq kit used for library preparation.Indices, included in the Illumina adaptors, were used to assign raw sequence reads obtained from the NextSeq500  platform to the originating samples. Demultiplexing was performed using bcl2fastq2 conversion software version 2.19 . All reads were filtered based on quality and adaptors were trimmed using Trimmomatic version 0.3625. The program was run under default settings, except for -i 0.95, -R 0.75 and–silent-clip. Reads that did map to the human reference genome  were used to assess sample and protocol adequacy by analyzing presence of reads mapping to the human reference protein coding gene actin beta (ACTB).High-quality paired reads were screened against the human reference genome version GRCh38 using NextGenMap version 0.5.226, which was run against a reference database containing all RefSeq bacterial and viral genomes (built in December 2020) with a 0.1 confidence threshold. A cut-off of 10 classified reads was used to discriminate positive genera for bacteria and viruses, and results reported all genera which comprised more than 1% of total bacterial or viral reads, respectively.High-quality non-human reads were classified using Kraken2 v. 2.1.1https://www.hpvcenter.se, accessed on 2020-01-20), together with complete genome sequences from HPV types that are not officially established yet , using NextGenMap version 0.5.225 with the same parameters as described previously. Reads that mapped with more than 90% identity over 75% of their length (-i 0.9 -R 0.75) were included for further analysis and subjected to visual inspection using Integrative Genomics Viewer to confirm mapping. Samples were considered positive for HPV (cut-off) if a minimum of 10 reads were detected for any HPV type with at least 90% identity and with an HPV genome coverage of above 10% of that particular HPV type (approximately 750 bp).As HPV is a necessary cause for cervical cancer, we also queried all non-human reads to several HPV databases. Non-human DNA reads were queried against a database of known HPV sequences including all HPV genomes officially established by the International HPV Reference Center .Non-human reads from RNA sequencing were queried against all HPV protein sequences included in the PaVE database , using the open source software DiamondEthical approval was granted by the Regional Ethical Review Board of Stockholm, Sweden (EPN-Dnr: 2014/1242-31/4). Written informed consent was obtained from all participants.The metagenome analysis from DNA sequencing of 6 cervical swab samples showed a median number of 138 million (M) high quality reads per sample. The metatranscriptome analysis from RNA sequencing without and with prior DNase-treatment of the extracted genetic material from the swab samples had a median number of 94 M and 46 M high quality reads per sample, respectively  for the RNA approach without prior DNase-treatment, and only reads covering exons for the RNA sequencing with prior DNase-treatment approach, implying that RNA sequencing of the total nucleic acids contains a small fraction of DNA, but almost no DNA left after DNase-treatment prior RNA sequencing , analysis of the ACTB human gene, which is known to be expressed in cervical cells and commonly used as a reference gene in normalization of RT-qPCR data in cervical cancer cell linesThe cervicovaginal bacteriome was analyzed using the Kraken2 program. Bacterial genera with at least 1% of the total number of classified bacterial reads in each of the 18 libraries among the 3 sequencing approaches  are shown in Fig. Lactobacillus genus showing the highest percentage of reads  for the 3 sequencing approaches. Sample 3 revealed most annotated reads corresponding to Gardnerella genus . In sample 4 most of the annotated reads corresponded to both Gardnerella and Lactobacillus genera . Sample 5 showed no reads belonging to the mentioned genera (Lactobacillus and Gardnerella) and instead, most of the annotated reads belonged to Bifidobacterium  and Streptococcus  .Sample 1, 2 and 6 shared a similar bacteriome pattern for the annotated bacterial reads, with Staphylococcus was present in all 6 samples from the DNA sequencing at 3% to 12% of the total bacterial reads. On the contrary, RNA sequencing detected Staphylococcus in only one sample without the DNase-treatment  and in none of the samples with prior DNase-treatment , Ralstonia, detected in 4 of 6 samples (1% to 3%), Escherichia, detected in 1 sample (4%) and Fannyhessea, detected in 1 sample (15%)  and Anaerococcus (2%) in one sample each  Fig. .Figure 4Siphoviridae, a family of viruses with double-stranded DNA genomes of about 50 kb that infects bacteria and archaea, and the family is known to infect the vagina 4. In 5 of 6 samples the Gorganvirus genus contributed to most of the viral reads  by DNA sequencing. Gorganvirus did also contribute to most of the viral reads in 4 of 6 samples by RNA sequencing without and with DNase-treatment   which are known to infect the mucosal cells of the cervix33.We detected 14 viral genera with at least 1% of the total viral reads whereof 4 genera were detected by DNA sequencing, 10 genera by RNA sequencing and 8 genera by RNA sequencing with prior DNase-treatment Table . One virs) Table . Only ons) Table . The absAlphapapillomavirus genus consists of small double-stranded DNA viruses with genome size of about 8 kb and includes the causative virus types for CIN3-lesions which are commonly detected in cervical swabs. Alphapapillomavirus was present in 3 of 6 cervical samples  by DNA sequencing and in one additional sample (sample 3), but with less than 1% and therefore not included in the table , where it contributed to most or second most of the viral reads  DNA sequencing, (ii) RNA sequencing and (iii) RNA sequencing with prior DNase-treatment. Analysis of the metagenomes and metatranscriptomes in 18 cervical swab libraries revealed distinct difference in relative detectability between microbial and viral genomes and the human genome. While the number of human reads and its relative abundance decreased when subjecting the samples to RNA sequencing in comparison with DNA sequencing, bacterial genomes generated many more reads in both absolute number of reads as well as in relative abundance. Human DNA depletion is known to favor and optimize the effective detection of bacterial communities by increasing the microbial sequencing depth, especially in clinical samples where approximately 90–95% of metagenomic sequencing reads from samples are annotated as humanMetagenome analysis from DNA sequencing using Kraken2 program revealed a wider distribution of the bacterial reads to different genera compared to metatranscriptome analysis from RNA sequencing for all samples. In 5 of 6 samples, most of the bacterial reads (about 90%) was generated from only a couple of genera, between 1 and 3 genera had more than 1% of the total bacterial reads, from both RNA sequencing approaches (without and with prior DNase-treatment). By DNA sequencing, between 4 and 7 genera had more than 1% of the total bacterial reads in all 6 samples. One explanation could be that only a few bacteria in each sample contribute to most of the bacterial transcripts, which are sequenced by RNA sequencing. However, several more bacteria are present, but with less transcription activity and hence, detected by DNA sequencing. Despite those differences, the detected bacteriome is overall in concordance between the 3 different sequencing approaches.Viral metagenome and metatranscriptome analysis revealed the opposite pattern compared to the bacteriome. RNA sequencing generated a wider distribution of viral reads to different genera  than DNA sequencing . The virus genera detected with most of the viral reads showed a strong concordance between the 3 sequencing approaches, except for the RNA viruses, which were only detected by both RNA sequencing approaches (without and with prior DNase-treatment) and not by DNA sequencing. This fact is also a strong indicator that RNA sequencing of cDNA libraries from total nucleic acids indeed sequence RNA genomes and transcripts.All the viral genera with DNA genomes that were detected with more than 1% of the total viral reads were double-stranded DNA viruses, but with different genome sizes, from small genomes like Alphapapillomavirus of 8 kb to large genomes like Roseolovirus around 200 kb and giant genomes like Mimivirus of above 1 Mb. Alphapapillomavirus, the known cause of cervical cancer and pre-cancer lesions, was not detected when samples were subjected to DNase-treatment. Therefore, the DNase-treatment should be avoided if the relative abundance or the number of reads of a specific target is expected to be low, as for small virus genomes, and/or if the virus is known to integrate within the human genome, as the DNase will deplete it.10.One theoretical advantage of subjecting specimens to RNA sequencing will be the possible detection of RNA viruses, which could be missed when only sequencing the total DNA without prior enrichment for viruses. Indeed, three virus genera with RNA genomes were detected in 5 of the 6 samples by RNA sequencing, but not with DNA sequencing. Furthermore, RNA sequencing provides the result of transcription status, proving that there is an infection. Some viruses (e.g. HPV detected in skin) can be the result of environmental deposition or a transient microbe and DNA detection is in this case not proof of an active infectionGardnerella is known to be a biomarker for HPV progression, whereas abundance of Lactobacillus has been associated with clearance of HPV infections38. The two samples without HPV infection had no Gardnerella detected above 1% of the total bacterial reads. On the contrary, 3 of 4 HPV positive samples had Gardnerella reads between 15 and 83% of the total bacterial reads. The detection of Lactobacillus showed no difference between normal and CIN3 samples, where 2 of 3 of both normal and CIN3 samples were positive for Lactobacillus. However, the study contained only a small pilot of 6 samples, and hence, case–control analysis cannot be conducted.Analysis of detectability of HPV types found few or no differences between DNA and RNA sequencing. While all CIN3 samples showed presence of HPV types, normal samples showed negativity in 2 of 3 samples and one sample being positive for 4 HPV types. The relative read abundance of Alphapapillomavirus of the total viral reads was 37%, 50% and 6% for DNA sequencing and 4%, 29% and 2% for RNA sequencing in the 3 positive samples and less than 1% in one sample by Kraken2 analysis. However, Alphapapillomavirus is a small DNA virus (approximately 8000 bp) with few genes compared to e.g. Roseolovirus (200 kb), which has approximately 25 times larger genome. The 15 times more reads of Roseoloevirus compared to Alphapapillomavirus would actually be the sequencing result of an equal proportion between the two virus genera or even a dominance of the Alphapapillomavirus. In conclusion, DNase-treatment of nucleic acids prior RNA sequencing tends to decrease human DNA sequences, but also loose to much of the virome nucleic acids. RNA sequencing generated much more bacterial reads than DNA sequencing and has the advantages of detecting actively transcribed infections, enabling differential gene expression analysis and the possibility to detect RNA genomes. By RNA sequencing, the variety of bacterial genera decreased despite more bacterial reads compared to DNA sequencing, suggesting that just a few of the bacterial genera contribute to most of the bacterial transcripts. More virus genera were detected by RNA sequencing, including both DNA and RNA virus genomes, the latter not possible to detect by DNA sequencing.


T = 3 icosahedral, larger T = 4 icosahedral, prolate, oblate forms, and a small prolate form elongated along the 3-fold axis. These particles are all produced during a normal infection, as well as when overexpressing the CPs. When overexpressing the shorter RNA fragments encoding only the CPs, we observed an increased percentage of the smaller VLPs, which may be sufficient to encapsidate a shorter RNA.The coat proteins (CPs) of single-stranded RNA bacteriophages (ssRNA phages) directly assemble around the genomic RNA (gRNA) to form a near-icosahedral capsid with a single maturation protein (Mat) that binds the gRNA and interacts with the retractile pilus during infection of the host. Understanding the assembly of ssRNA phages is essential for their use in biotechnology, such as RNA protection and delivery. Here, we present the complete gRNA model of the ssRNA phage Qβ, revealing that the 3′ untranslated region binds to the Mat and the 4127 nucleotides fold domain-by-domain, and is connected through long-range RNA–RNA interactions, such as kissing loops. Thirty-three operator-like RNA stem-loops are located and primarily interact with the asymmetric A/B CP-dimers, suggesting a pathway for the assembly of the virions. Additionally, we have discovered various forms of the virus-like particles (VLPs), including the canonical  A1, which is a read-through extension caused by a leaky stop codon for the major CP; and the β-subunit of the RNA-dependent RNA replicase (Rep) . In. In3]. Iotein A1 . The othotein A1  formed botein A1 . The seqT = 3 capsid, which consists of twelve pentamers and twenty hexamers of the CPs  or C/C dimers (located on the edge between two hexamers) ,42. NotaT = 4 capsids have been observed when overexpressing the covalently-linked CP dimers in ssRNA phages PP7 and MS2 [T = 4 capsids are also made, albeit a small percentage (0.5%) of the overall number of particles  fraction of the Qβ genome from a plasmid, the percentage of the oblate and small prolate forms increase further to 35% and 12%, respectively (cp and A1 as opposed to the entire 4217nt in the gRNA). There are also host RNAs with operator-like sequences which may also be encapsidated by smaller particles of the oblate and small prolate forms [When over-expressing the gene containing only the ectively C. The inte forms . AdditioT = 3 and T = 4 VLPs, both are formed by only one type of hexamer. For the oblate and small prolate VLPs, there are two types of hexamers in each form. For the prolate VLP, there are three types of hexamers. This would give rise to a total of nine types of hexamers . Previously, it has been reported that some Qβ CP mutants can form rod-like particles [All these different forms of the VLPs confer different curvatures in the capsids. To investigate the underlying structures that support these different morphologies, given that they are formed by the same CP, we analyzed the assembly and structural plasticity in the CPs. Notably, all the CP dimers for these different forms of the VLPs exhibit small structural variations, which are mostly localized in the flexible loop regions . The samarticles , which sTo further investigate how plasticity within the hexamers is allowed, we examined the interface between two neighboring CP monomers within one hexamer. This interface is formed between the FG-loops of neighboring subunits, and between the C-terminus of one CP and the CD/DE-loops of its neighbor . Though In this paper, we have presented the complete model of the Qβ gRNA in its capsid, which provides insight into the assembly process of the wild-type Qβ in vivo. The location of the operator-like RNA stem-loops in the entire gRNA suggests an order for the RNA packaging and capsid formation in Qβ A. In pacT = 3 icosahedral, T = 4 icosahedral, prolate, oblate, and a small prolate form elongated along the 3-fold axis. Though there are always twelve copies of the pentamers in different forms of the Qβ VLPs, the numbers of the hexamers vary significantly. Interestingly, the geometries of a few forms of these capsids are related. For example, one can convert an oblate capsid to a T = 3 capsid by separating the two end-shells from the equator and adding five more hexamers where the two half-shells rejoin  and the small prolate (from 3% to 12%) forms, suggest the length of the RNA being encapsidated may affect the ratio of different forms of the VLPs. In the wild-type Qβ, the entire wild-type gRNA forms operator-like stem-loops (It has not been studied how the CPs structurally protect the RNA of different lengths. Our results, showing that over-expressing the 987-nt em-loops  to coordem-loops E that se


Flaviviruses use a ~70 nucleotide stem-loop structure called stem-loop A (SLA) at the 5′ end of the RNA genome as a promoter for RNA synthesis. Flaviviral polymerase NS5 specifically recognizes SLA to initiate RNA synthesis and methylate the 5′ guanosine cap. We report the crystal structures of dengue (DENV) and Zika virus (ZIKV) SLAs. DENV and ZIKV SLAs differ in the relative orientations of their top stem-loop helices to bottom stems, but both form an intermolecular three-way junction with a neighboring SLA molecule. To understand how NS5 engages SLA, we determined the SLA-binding site on NS5 and modeled the NS5-SLA complex of DENV and ZIKV. Our results show that the gross conformational differences seen in DENV and ZIKV SLAs can be compensated by the differences in the domain arrangements in DENV and ZIKV NS5s. We describe two binding modes of SLA and NS5 and propose an SLA-mediated RNA synthesis mechanism. Flaviviruses use a ~70 nucleotide stem-loop structure called stem-loop A (SLA) at the 5’ end of the RNA genome as a promoter for RNA synthesis by the viral polymerase NS5. Here the authors describe the structures of dengue and Zika virus SLAs, identify the SLA-binding site on NS5, and propose models for how NS5 recognizes the RNA promoter. The incidence of DENV infection has increased dramatically in recent decades and the CDC estimates 400 million dengue infections occur each year2. Similarly, recent outbreaks of ZIKV in the Americas show that the virus is linked to microcephaly in newborns and Guillain-Barre syndrome in adults3. Despite the significant impact of flavivirus infection on human health, vaccines are available only for a limited number of flaviviruses, and antiviral therapies to treat viral infections are not available. Thus, there is pressing need to develop additional therapeutics and vaccines that interfere with essential steps in the flaviviral lifecycle.Flaviviruses such as dengue (DEN), West Nile (WN), Japanese encephalitis (JE) and Zika virus (ZIKV), cause viral hemorrhagic fever and/or encephalitis in humans. For example, dengue virus (DENV) causes diseases ranging in severity from mild dengue fever with non-specific flu-like symptoms to fatal dengue hemorrhagic fever and dengue shock syndrome4. Among the NS proteins, NS3 and NS5 enzymatic activities are required for genome replication. NS3 consists of an N-terminal serine protease, which requires NS2B as a cofactor, and a C-terminal helicase. The helicase domain also has 5’-RNA triphosphatase activity (which hydrolyzes the γ-phosphate of RNA for capping)7. NS5, the largest NS protein (103 kDa), consists of an N-terminal methyltransferase (MTase) domain and a C-terminal RNA-dependent RNA polymerase (RdRp) domain. The MTase domain has guanylyltransferase and methyltransferase activities and is involved in capping and methylation to form a type 1 cap structure at the 5’-end of the genome10. The 5′- and 3′-UTRs have conserved RNA secondary structures that are essential for viral replication. In particular, the 5’-UTR includes a ~70 nucleotide long stem-loop, called stem-loop A (SLA) that functions as a promoter for viral RNA synthesis by NS5 polymerase14. NS5 recognizes and interacts with SLA at least twice during genome replication. NS5 first recognizes the SLA to initiate negative-strand RNA synthesis at the 3’ end of the genome (RdRp function)16. Later, during positive-strand RNA synthesis, NS5 again recognizes SLA and methylates the 5’ guanine cap to form an m7GpppAm type 1 cap (MTase function)19.Flaviviruses are enveloped viruses that encapsidate a positive-sense RNA genome of ~11 kb, which consists of a 5’-cap, a 5’ untranslated region (5’-UTR), a single open-reading frame (ORF), and a 3’-UTR. The ORF is translated as a single polyprotein, which is later processed into three structural proteins (capsid (C), pre-membrane (prM), and envelope (E)), and seven non-structural (NS) proteins . The viral NS proteins, along with viral RNA and unidentified host proteins, self-assemble on the cytoplasmic side of the ER membrane to form a viral replication complex that carries out genome replication20. Flavivirus NS5 from one species can bind an SLA from a related species with similar affinities as for its cognate SLA, and successfully replicate the genome of the related species22. For example, chimeric WNV containing DENV serotype 2 (DENV2) SLA at its 5’-end is able to replicate similarly to wild-type WNV21. Despite the essential role SLA plays in flavivirus genome replication, little is known regarding the structure of SLA or how NS5 recognizes and engages SLA for replication or methylation. Here we report crystal structures of flavivirus SLA; DENV and ZIKV SLA were determined to 3.4 and 3.8 Å resolution, respectively. Both structures show similar structural elements, consisting of a top stem-loop, a side loop, and a bottom stem, but differ in size and the relative arrangements of the individual elements. We identified the SLA-binding sites in their respective NS5 viral polymerases and show that the structural differences in DENV and ZIKV SLAs correlate with the differences in the domain arrangements of their NS5s.SLA-mediated RNA replication is likely conserved in flaviviruses. Flavivirus SLAs share a similar, predicted ‘Y’-shaped 3-way junction structure despite sharing only low sequence identityLys  and for tRNA-SLAZIKV (142 nt) structures could be traced in the electron density maps. Data collection and refinement statistics for DENV and ZIKV SLA are shown in Supplementary Table The SLA structures were determined using a chimeric RNA, wherein the DENV2 SLA (nucleotides 1–70) or ZIKV SLA (nucleotides 1–71) was inserted into the anticodon loop of human tRNADENV. The tRNA-SLADENV binds viral polymerase NS5 with binding constant of 4.8 × 106 M−1 using a fluorescence competition assay 22. To further test if tRNA-SLA constructs form similar NS5 complexes as the unmodified SLA, interactions of tRNA-SLADENV and tRNA-SLAZIKV with their respective NS5s were analyzed using electrophoretic mobility shift assays (EMSA) , indicating that SLA retains its native fold in the tRNA-SLA chimera , analytical ultracentrifugation, and X-ray crystallography28. We found that SLA also forms a dimer in solution (see below), and thus, the multiple bands in EMSA may represent NS5 monomer/dimer and SLA monomer/dimer complexes. We next tested if NS5 and tRNA-SLA interactions are specific using a competition assay with different forms of nucleic acids, such as yeast tRNAPhe, single-stranded DNA (ssDNA), and single-stranded RNA (ssRNA). DENV NS5 specifically binds to tRNA-SLADENV in the presence of yeast tRNAPhe, ssDNA or ssRNA  between the top and bottom stem does not exist. Instead, the 18GA19 form Watson-Crick base pairs with the 53UC54 that was predicted to be in the side stem, resulting in the bottom stem being extended by 3 base pairs compared to the prediction. Second, as a consequence of the extension of the bottom stem , the internal base pairs between 44GAGC47 and 51GCUC54 in the predicted side stem cannot form. Thus, the predicted side stem becomes a single-stranded side loop. Surprisingly, the side loop reveals the previously undetected self-complementary sequence (46GCUAAGC52) that forms intermolecular Watson-Crick base pairs with the equivalent region from the second SLA molecule in palindromic order with one mismatch , predicted to be between the top and bottom stem, does not exist, and is inserted in the extended top stem-loop. Because of base pairing between 15GU16 and 46GC47, the internal base pairs in the predicted side stem-loop (between 45AGC47 and 52GCU54) do not form, and the side loop of ZIKV SLA is single stranded. Surprisingly, the side loop also contains a previously unidentified, palindromic sequence (52GCUAGC57) that forms intermolecular Watson-Crick base pairs with equivalent region from the neighboring molecule  and ZIKV SLA (17GAA19) show a relatively low SHAPE reactivity, as the bulges are incorporated into the stems and constrained by base pairing and stacking interactions The DENV and ZIKV SLAs have different tertiary structures in terms of the length of the individual RNA elements  and the relative orientations of the top and bottom stems Fig. . When th31. Thus, NS5 likely recognizes the overall shape and common structural features of SLAs rather than specific sequences. We analyzed functionally important regions in the SLA structures. First, the bottom stem is the most conserved region in SLAs and contains the absolutely conserved ‘U-bulge’ that consists of at least one unpaired U  in DENV SLA was shown to be lethal for viral replication, but did not affect affinity for NS5 or in vitro RNA synthesis32. In both DENV and ZIKV SLA structures, the bottom stem forms a long dsRNA helix with an unpaired U-bulge located in the same position relative to the 5’ terminus  in DENV SLA is flipped out from the dsRNA helix, while the U-bulge (63U) in ZIKV SLA is not. It has been shown that base pairing patterns within the bottom stem, rather than the presence of specific bases, are important for viral replication31. Thus, the base pairing in the bottom stem helix likely provides structural stability and helps orient the U-bulge in relation to the 5’ terminus of SLA for interaction with other components of the replication complex.Flavivirus NS5s are shown to use SLAs from related flaviviruses for viral replication despite low sequence identity between the SLAs Fig. 21,31. Thd U Fig. . Deletionus Fig. . The U-b31, suggesting that the position of the AG motif within SLA is important for replication. The top loop (30CAGA33) in DENV SLA was also identified as the NS5 interaction site in a DENV SLA-NS5 foot-printing experiment32. Although the top stem-loops in DENV and ZIKV SLA are related by 180° rotation, the unpaired AG motif is facing the same side because the top stem loop in ZIKV SLA is longer by an additional half turn  , indicating that DENV SLA can form both an SLA homodimer and an SLA-SLA(-) heterodimer. A random 8-mer RNA (negative control) does not bind SLA, indicating that the RNA-RNA interactions involving SLA are specific.Because both DENV and ZIKV SLAs crystallized as a dimer, we tested if SLA forms a dimer in solution. The tRNA-SLAium Fig. . We also(-) Fig. . If SLA((-) Fig. . The tRNTop and ssRNASide, respectively) were designed with a fluorescein tag at the 5’-end 4 extension  with that of ZIKV (SLA-ZIKV) or the side loop sequence of DENV SLA (44GAGCUAAGC52) with 44AAAAAAAA52 (SLA-9A) in the full-length DENV2 infectious RNA. The SLA-ZIKV mutant would maintain dimer formation, while the SLA-9A mutant would not allow dimer formation. After viral RNAs were transfected into baby hamster kidney cells BHK-21, viral NS1 protein expression and virus production were monitored. WT DENV2 RNA and the SLA-ZIKV mutant showed immunofluorescence-positive cells on day 2 post-transfection and cytopathic effect (CPE) on day 6  mutations in the entire genome.The kissing loop interactions observed in both DENV and ZIKV SLAs, and the conservation of similar self-complementary sequences in other flaviviruses suggest that RNA-RNA interactions via the side loop in SLA may have biological significance. As precedence, intermolecular RNA-RNA interactions in viral genomes of retrovirus and hepatitis C virus have been reported as essential features for viral replication and genome packagingy 6 Fig. . In conty 6 Fig. . The cely 6 Fig. . They hay 6 Fig. . These 432. Accordingly, flavivirus NS5 binds the SLA promoter with high affinity and high selectivity, yet how NS5 specifically engages SLA is not clear36  in the priming loop of the thumb subdomain had been deleted. The priming loop deletion mutant (NS5-Δ6) binds SLA with a wild-type affinity of 181 nM, indicating that SLA does not bind in the template-binding channel  suggests that the complementary SLA-binding surface on NS5 is similarly large. The full-length DENV NS5 is ~90 ×70 Å in size28, slightly larger than SLA. Thus, both RdRp and MTase domains of NS5 likely interact with SLA. Functionally, both NS5 domains are required to interact with SLA, i.e., the MTase for 5’-cap methylation and the RdRp for viral genome replication43. We identified positively charged MTase and RdRp surfaces on the same face of NS5 that likely bind the negatively charged SLA, R22 and K23 in the MTase and K841, R842, and R856 in the thumb subdomain of RdRp  of 417 nM (43% of WT), 357 nM (51% of WT) and 455 nM (40% of WT), respectively, indicating that the SLA-binding site in DENV NS5 involves R22-K23 and K841-R842  and R771-R772-R775-K843-K844 (thumb subdomain of RdRp)  in the top stem-loop and 50A in the side loop were protected by binding either full-length NS5 or the RdRp domain alone, suggesting that the RdRp domain interacts with the top stem-loop and the side loop  in DENV2 NS5 recognize the ACAG nucleotide in the top stem-loop of SLA50. Thus, the top stem-loop and the side loop of SLA were positioned toward the RdRp domain near the positively charged surface involving K841-R842 (DENV3) or K843-K844 (ZIKV) in their respective NS5  or K28, K29, R41 and R42 (ZIKV NS5)  Fig. . In bothS5) Fig. . In part51. Because the SLA-binding site does not overlap with the template-binding channel, SLA would likely stay bound to NS5 during the elongation phase, until it is time for the SLA at the 5’-end to act as a template to finish replication of the end of the genome. This NS5-SLA interaction would provide a simple mechanism for accomplishing three essential tasks during replication: 1) selective synthesis of the viral RNA genome from the myriad of cellular mRNAs in the cytoplasm, 2) sequestering only fully intact viral RNAs on NS5 for replication as opposed to prematurely aborted transcripts, and thus 3) ensuring that a complete copy of the 5’-end of the genome is produced.How NS5 interacts with the SLA structure at the 5’-end of the genome to promote RNA synthesis at the 3’ end is poorly understood. To initiate negative strand RNA synthesis at the 3’-end of the viral genome, the NS5 and SLA complex should recognize and interact with the 3’-end. It is not known whether NS5 can bind SLA and the 3’-end of the viral genome simultaneously during RNA synthesis. Alternatively, SLA and the 3’-end of genome could compete for the template-binding channel in NS5. Our binding assay shows that the deletion of the priming loop of NS5 has no effect on SLA binding Fig. , indicat52. The DENV2 SLA sequence, containing the first 70 nucleotides was inserted into the anticodon loop of human tRNALys to generate tRNA-SLADENV. Two additional mutations were introduced to the chimeric tRNA-SLADENV to stabilize the structure by base pairing in the linker region between the tRNA and SLA moieties. The 3U (DENV2 SLA numbering) was replaced with C for base pairing with 70G, and two nucleotides CU were inserted after 70G to form base pairs with 1AG2 , and inserted into pBluescript II SK vector with lpp promoter, the promoter of most abundant E.coli lipoprotein, and E.coli rrnC terminator. The tRNA-SLAZIKV was generated from tRNA-SLADENV by several mutagenesis that replaced the sequence of DENV2 SLA to ZIKV SLA, the first 71 nucleotides of the epidemic strain of ZIKV (KU527068). The chimeric tRNA-SLAZIKV also contains the modification at 3U (ZIKV SLA numbering) to C (to base pair with 71G) and the insertion of CU after 71G. Because the tRNA-SLAZIKV construct did not yield diffraction-quality crystals, additional constructs were generated by modifying the linker region between the SLA and the tRNA, from one base pair insertion to 10 base pair deletion by one base pair. The tRNA-SLAZIKV containing the U-A base-pair insertion in the linker region (142 nt) showed best diffraction and used for structure determination  for ion-exchange chromatography. The extracted RNA was loaded on HiLoad 16/60 Q ion-exchange column  equilibrated with buffer A. After a washing step with 0.4 M NaCl, RNA was eluted with the 0.4–0.7 M NaCl gradient in buffer A. The fractions were analyzed by electrophoresis on 8 % urea-acrylamide gel. The pooled samples were buffer-exchanged to 20 mM Tris-HCl, pH 7.4, containing 100 mM NaCl and 2 mM MgCl2, and concentrated to ~20 mg/ml for crystallization. For size-exclusion chromatography, the RNA was loaded on Superdex S75 HiLoad 16/600 column . The tRNA-SLADENV eluted in two separate peaks that correspond to a dimer and monomer, respectively. The fractions from the two peaks were resolved on 8% urea-PAGE in Tris/borate/EDTA buffer.The plasmids containing either tRNA-SLADENV was crystallized by the sitting-drop vapor diffusion method at 20 °C by mixing the RNA with an equal volume of a reservoir solution containing 100 mM sodium cacodylate, pH 6.9, 9% (w/v) polyethylene glycol 8,000, 200 mM KCl, and 100 mM magnesium acetate. Crystals grew to a full size within a week. For data collection, crystals were transferred to mother liquor supplemented with 20% (v/v) ethylene glycol and flash-frozen in liquid nitrogen. Diffraction data to 3.4 Å resolution were collected at 100 K with a wavelength of 0.9785 Å at the Advanced Photon Source beamline 21-ID-F . The data set was processed using HKL200054. The crystal belonged to space group I222 with unit cell dimensions of a = 30.9 Å, b = 137.5 Å, c = 336.8 Å, and contained one molecule in the asymmetric unit with solvent content of 69%. The initial solutions were found by molecular replacement with yeast tRNAPhe (PDB 1TN1) and partial RNA helix (PDB 5EW4) as search models using the program PHASER in the PHENIX suite55. Manual model building was carried out with Coot and iterative refinement was performed with phenix.refine56. The final model contained one tRNA-SLADENV molecule, one magnesium ion, and 7 water molecules with the R and Rfree factors of 22.6 and 26.8%, respectively  polyethylene glycol 3350, 200 mM LiSO4, and 67 mM CsCl. The crystals grew with salt crystals in the same drop and took 2-3 weeks to reach full size. For X-ray data collection, the crystals were soaked into mother liquor supplemented with 20% (v/v) ethylene glycol for 5-10 min and flash-frozen in liquid nitrogen. Diffraction data to 3.8 Å resolution were collected at 100 K with a wavelength of 1.1271 Å at the Advanced Photon Source beamline 21-ID-D . The data set was processed with HKL200054 and the crystal belonged to space group C222 with unit cell dimensions of a = 157.1 Å, b = 156.6 Å, c = 72.3 Å. The asymmetric unit contained one molecule with solvent content of 74%. The initial solution was found by molecular replacement with tRNA-SLADENV as search model using the program PHASER in the PHENIX suite55. Extensive model building was required to trace the entire tRNA-SLAZIKV molecule. Manual model building and iterative refinement were carried out with Coot and phenix.refine56. The final model contained one tRNA-SLAZIKV molecule with the R and Rfree factors of 22.7 and 27.2%, respectively  were incubated with increasing amount of NS5 protein (1 to 4 μM) in 20 μl of binding buffer  for 30 min at room temperature. Each reaction mixture was then mixed with 2 μl of 0.01% bromophenol blue dye and resolved on 1% agarose gel in 1x Tris/borate/EDTA (TBE) buffer. ZIKV NS5 interactions with tRNA-SLAZIKV and in vitro transcribed ZIKV SLAs were similarly determined by EMSA. The ZIKV NS5 was expressed with a SUMO-tag in E.coli and purified as previously described40. ZIKV RNAs containing the first 71 and 81 nucleotides (SLA71 and SLA81) were prepared using in vitro RNA transcription.DENV NS5 interaction with tRNA-SLADENV interaction was also examined by competitive binding reaction with 1 μM of yeast tRNAphe (Sigma-Aldrich), single-stranded 43mer DNA , or fluorescein-labeled 8mer RNA . The tRNA-SLADENV and competitor mixture was incubated with increasing amount of DENV NS5 (1-4 μM) for 30 min at room temperature and resolved on 1% agarose gel in 1x TBE buffer. The fluorescein-labeled RNA and other nucleotides including tRNA-SLADENV were visualized by ethidium bromide staining under UV (302 nm). The specificity of the ZIKV NS5 and tRNA-SLAZIKV interaction was similarly examined by competitive binding assay with yeast tRNAphe, 43mer DNA, and 8-mer RNA.The specificity of the DENV NS5 and tRNA-SLADENV through the side loop, DENV2 SLA containing the first 80 nucleotides (SLA) and the 3’-end of DENV2 negative strand that is complementary to SLA, SLA(-), were synthesized with fluorescein at the 5’-end by IDT  , the side loop region , and modified sequence of ‘Side’   would generate RNA dimer through the (GC)4 region, allowing the side loop sequence available for SLA interaction. The tRNA-SLADENV (1 μM) was incubated with increasing amount of fluorescein-labeled RNAs (1 to 4 μM) in binding buffer  for 30 min at room temperature (20 °C). The resulting RNA-RNA complexes were separated by 2% agarose gel in 1x TBE buffer at room temperature. The fluorescein labeled RNAs were analyzed with Amersham Typoon  using the LD488 laser for excitation and the Cy2 525BP20 filter.To analyze the intermolecular interaction of tRNA-SLA795WSIHAH800) in the priming loop28. NS5 mutants R22A/K23A, K841A/R842A, K841E/R842E and R856A were generated by site-directed mutagenesis. The wild-type DENV3 NS5 and mutants  were expressed and purified as previously reported28. DENV SLA80 was synthesized with 3’-end fluorescein , and interaction between DENV NS5 proteins and SLA80 was determined by fluorescent titration using a PC1 spectrofluorometer 47. Briefly, the direct binding of DENV NS5 to the labeled SLA80 was monitored by the fluorescence of fluorescein  in 50 mM Tris-HCl (pH 7.5) containing 100 mM NaCl, 1 mM MgCl2, 1 mM DTT, and 10% (w/v) glycerol. The relative fluorescence change is defined as Fi/F0, where Fi is the fluorescence of the nucleic acid solution at a given titration point i, and F0 is the initial fluorescence of the sample. Binding curves were then fit to the following equation using KaleidaGraph software 22. The binding constant, K1, characterizing the association of SLA with NS5, is defined asF is the concentration of the formed complex. The observed fluorescence of the sample at any point of the titration is defined asFF and FC are the molar fluorescence intensities of the free SLA and the formed complex, respectively. Thus, the relative observed change of the SLA fluorescence, ΔFobs, is thenFmax = FC/FF is the maximum value for the observed relative fluorescence quenching. The assays were performed in triplicate and the mean value was used to calculate the apparent binding constant K1. The binding constant K1 for wild-type NS5 is 5.5 ×106 M−1 (Kd of 181 nM).The priming loop deletion mutant of DENV3 NS5 (NS5-Δ6) was constructed by removing 6 amino acids (Escherichia coli shuttle vector as previously described58. The entire SLA sequence (1-70 nt) was replaced with that of ZIKV in SLA-ZIKV, and the side loop sequence (44GAGCUAAGC52) was replaced with 44AAAAAAAA52 in SLA-9A. The DNA fragment containing the desired mutations were generated by overlapping PCR using the primers in Supplementary Table st and 3rd PCR fragments) and pRS424-FLZIKV . The SLA-9A mutant was generated with two PCR amplifications using the pRS424-FLDV2 template (Supplementary Table Full-length cDNA of DENV2 (New Guinea C strain) containing SLA mutations were constructed in yeast/57. Briefly, ∼1 × 106 cells were resuspended in 100 μl of Ingenio solution . After pulsing, cells were resuspended in 3 ml of prewarmed complete medium (Dulbecco’s modified Eagle’s medium (DMEM), supplemented with 10% fetal bovine serum and 1× streptomycin/penicillin) in a T-12.5 flask at 37 °C in an CO2 incubator. On day 2, cells were trypsinized and transferred into T-75 flask. This procedure was repeated using one-third of the trypsinized cells every 4 days. For immunofluorescence assay, RNA-transfected cells were seeded into a slide (LabTek) and fixed by treatment with acetone. Cells were incubated with a 1:200 dilution of 7E11, a monoclonal antibody against DENV2 NS1. Fluorescein isothiocyante (FITC)-labeled, goat anti-mouse immunoglobulin G conjugate (Kirkegaard & Perry Laboratories) was used as a secondary antibody at a 1:100 dilution. Immunofluorescence photomicrographs (×200 magnification) were acquired using an Olympus IX-71 inverted epifluorescence microscope coupled to the Olympus automated photographic system.The DENV2 RNA (∼3 μg) containing either the WT DENV2 SLA, SLA-ZIKV, or SLA-9A was transfected by electroporation  into BHK-21 cells , as previously described57 to make overlapping fragments of ∼800 bp each that spanned the entire genome  with the viral sequence-specific primers57. Briefly, the debris in the supernatants was removed by centrifugation at 355 x g for 5 min, and a 2.5 ml of 40% PEG 8000 was added to 10-ml supernatants. The mixed solutions were stirred at 4 °C for 2 h and refrigerated further for 3 h without stirring. After centrifugation at 15,170 x g for 45 min at 4 °C, each pellet was collected with ∼0.5 ml of the remaining solution and mixed with 1.5 ml of TRIzol-LS (Ambion). Chloroform (0.4 ml) was added and mixed further. The solution was centrifuged at 12,000 x g for 15 min, and the upper layer was collected and precipitated by the addition of ethanol. The pellet was resuspended in nuclease-free water, and the final RNA concentrations were prepared in a range between 25 and 100 ng/μl. cDNA library preparation and next-generation sequencing procedures were performed by LC Science (Houston).For next-generation sequencing, viral RNAs were extracted from supernatants (∼10 ml) of BHK-21 cells, as previously describedAfter removing ribosomal RNA (rRNA) using NEBNext rRNA depletion kit, RNA samples were treated with the Illumina TruSeq stranded mRNA library prep kit, which consists of RNA fragmentation followed by first- and second-strand cDNA synthesis, adenylation of the 3′-ends, adapter ligation, and DNA fragment enrichment via PCR (10 cycles). The quality control check of the libraries using an Agilent 2100 Bioanalyzer showed a range of library sizes, with the curve being mostly between 200 and 400 base pairs long. The libraries were pooled together with equal amounts based on the Agilent quantification. Real-time qPCR was used to quantify the pooled libraries for optimal clustering during sequencing. All samples were sequenced on a single Illumina Novaseq 6000 run, generating paired-end 150-base-pair readings. The .bcl files generated by the sequencer were converted to Fastq data using the software bcl2fastq v2.17.1.14. DENV2 genomes were aligned with Geneious prime software (Biomatters), which differentiates statistically significant variants from total SNPs identified within reads. Briefly, paired-end sequences were made from Fastq data R1 and R2. Sequenced fragments (contigs) were trimmed with the quality scores limit set to 0.05. The trimmed contigs were aligned to the reference  sequence, and the SNPs were analyzed. Nucleotide alterations at more than 3% frequency are shown in Fig. Further information on research design is available in the Supplementary InformationReporting Summary


Aberrant RNA–protein complexes are formed in a variety of diseases. Identifying the ligands that interfere with their formation is a valuable therapeutic strategy. Molecular simulation, validated against experimental data, has recently emerged as a powerful tool to predict both the pose and energetics of such ligands. Thus, the use of molecular simulation may provide insight into aberrant molecular interactions in diseases and, from a drug design perspective, may allow for the employment of less wet lab resources than traditional in vitro compound screening approaches. With regard to basic research questions, molecular simulation can support the understanding of the exact molecular interaction and binding mode. Here, we focus on examples targeting RNA–protein complexes in neurodegenerative diseases and viral infections. These examples illustrate that the strategy is rather general and could be applied to different pharmacologically relevant approaches. We close this study by outlining one of these approaches, namely the light-controllable association of small molecules with RNA, as an emerging approach in RNA-targeting therapy. Initially thought to just act as the intermediate between DNA and protein synthesis, RNA is now known to perform essential and diverse functions within cells. It forms part of the ribosome , the splAntisense oligonucleotides and synthetic RNAs that redirect the cellular RNA interference machinery or activate CRISPR-based systems have provided a proof of principle for drugs targeting RNA–protein complexes . HoweverSeveral experimental approaches have provided fundamental insight into RNA–ligand interactions. These include UV–Vis spectroscopy, fluorescence-based methods, isothermal titration calorimetry, and surface plasmon resonance, along with NMR, X-ray and cryo-EM, and chemical probing . HoweveMolecular Dynamics (MD) simulations significantly help to investigate RNA–ligand interactions ,16,17, cThus far, MD simulations of RNA in complexes with small molecules have focused mostly on the so-called “hairpin”, a highly pharmaceutically relevant RNA structural element spanning from viruses such as HIV-1 RNA  to humaImproper RNA folding leads to the dysregulation of cellular processes  either bWe focus here on the most known of these diseases, HD. Here, the CAG repeat expansion mutation is located on the huntingtin (HTT) gene. This leads to an aberrant hairpin structure of HTT mRNA with Watson–Crick pairing at the G/C position and intermediate mismatches at the AA position. In addition to these double-stranded structures, these hairpins also form three-dimensional helical structures . The haiMore than 40 proteins bind to HTT RNA in a CAG repeat length-dependent manner, the majority of which are proteins that play a role in RNA splicing . TrappinA second well-established group of proteins that are recruited to mutant HTT RNA are translation regulators. Specifically, a protein complex containing the E3 ubiquitin ligase MID1, when aberrantly bound to mutant CAG repeat RNA, recruits the translation initiation machinery and thus induces protein synthesis of neurotoxic polyglutamine protein ,45. In dA final group of RNA-binding proteins sticking to mutant CAG repeat RNA are RNA-processing enzymes of the RNAi machinery. Specifically, the ribonuclease Dicer, when bound to mutant HTT RNA, cleaves small CAG repeat RNAs (sCAGs) of <21 nucleotides ,49. ThesIn summary, the CAG repeat expansion mutation in HD leads to an aberrant hairpin structure A that isWe show here how the combinations of advanced molecular simulations approaches and experiments led to the identification of RNA CAG binders able to counteract the pathogenic effects of aberrant RNA CAG hairpin–protein interactions in living cells.RNA ). ThWe saw that mutations in CAG repeat expansion diseases create aberrant hairpin structures that lead to the formation of non-physiological RNA–protein complexes. These induce diverse cellular processing, including an abnormal translation of the mutant RNA ,45. SimiIn such viral infections, hairpin motifs may be involved in the translation of viral RNAs by the host’s ribosomes. Ligands that could block these aberrant RNA–protein interactions would be beneficial in the context of antiviral therapy.Molecular dynamics simulations and free energy calculations, as described above, can obviously be applied to viral RNA as well. Here, we focus on HIV-1’s genome. This genome contains several structural elements, including hairpins, that have been successfully targeted with small molecules to inhibit viral replication . Among tTAR activates elongation of the transcription of the virus by forming a complex with both the virally encoded Tat protein and human cyclin T1 . Indeed,The studies above, along with many other NMR studies ,103,104,One of the peptides used experimentally, the cyclo-RVRTRKGRRIRIPP peptide (referred to as L22 hereafter), mimics the TAR-binding region of the Tat protein . It bindBy building on this research, a method was developed to predict the pose and affinity of highly charged molecules binding to RNA . This woMore and more pathogenic mechanisms at the base of human diseases are recognized to be connected with aberrant toxic RNA–protein interactions. The general concept underlying RNA-mediated toxicity is that a disease-linked RNA aberrantly recruits RNA-binding proteins, which results in a toxic gain of function of the RNA in conjunction with the protein. Interestingly, increasing evidence has shown that actually similar pathogenic pathways may occur across a variety of diseases. Thus, similar pathogenic mechanisms could be involved in genetic diseases, in which mutant transcripts aberrantly trap RNA-binding proteins, as well as infectious diseases, where aberrant viral RNA–host-protein interactions take place.Here, we presented a few computational studies of RNA in complex with ligands interfering with the formation of aberrant RNA–protein interactions. The strategy outlined for our case studies, namely CAG repeat expansion diseases and HIV-1 TAR, can be transferred in a straightforward manner to other RNAs and RNA–protein complexes involved in diseases, with a major benefit of reducing time and efforts in the wet lab.Molecular dynamics of RNA simulations have progressed dramatically in quality and scope over the past four decades, providing important atomistic insights into RNA structure, dynamics, and biological response to cellular and small-molecules modulators. These successes have been driven by the development, assessment, and progressive refinement of the atomistic force field, which in turn have benefited from growing repositories of experimental and quantum mechanics (QM) target data .Benchmarks for RNA FFs have been published in reviews ,109. TheIn conclusion, MD simulations approaches are valuable tools to predict the pose and affinity of ligands targeting RNA along with RNA structural plasticity in response to ligand binding ,108. The


MicroRNA can be found in various body fluids, including breast milk. MicroRNA may be transferred from mother to infant via breast milk and potentially regulate the development of the infant's immune system on a post‐transcriptional level. This study aimed to determine the microRNA extraction efficiency of five RNA extraction kits from human skim milk samples. Their efficiency was determined by comparing microRNA concentrations, total RNA yield and purity. Furthermore, hsa‐miR‐148a‐3p expression and the recovery of an exogenous control, cel‐miR‐39‐3p, were quantified using qPCR. Each kit extracted different amounts of microRNA and total RNA, with one kit tending to isolate the highest amount of both RNA species. Based on these results, the extraction kit ReliaPrep™ miRNA Cell and Tissue Miniprep System from Promega was found to be the most appropriate kit for microRNA extraction from human skim milk. Moreover, further research is needed to establish a standardized protocol for microRNA extraction from breast milk. However, as standardized protocols for BM miRNA extraction are lacking, it is critical to establish appropriate methodology as has been done for other biofluids like plasma22.1BM was collected at the first morning feeding by ten healthy women 4 months post‐partum and stored at −20°C. After transportation to the university, all samples were kept at −70°C until analysis. The milk was thawed and fractionated by centrifugation, 800 × g, 10 minutes, 4°C; skim milk was further purified by repeating the centrifugation in new tubes, see Figure 2.2This study investigates the performance of five phenol/chloroform‐free RNA isolation kits that, at the time of the study, were commercially available on the international market.Caenorhabditis elegans  was added before extraction, and the elution step was repeated to extract as much miRNA as possible from the matrix.Although the manufacturers' protocols were generally followed, some modifications were established, as shown in Appendix 2.3RNA quantification was performed using Qubit microRNA Assay kit and Qubit RNA HS Assay kit together with the Qubit 3.0 Fluorometer (Invitrogen), following guidelines; Nanodrop ND‐1000 spectrophotometer (Thermo Fisher Scientific) was used for purity assessment.2.4The exogenous miRNA cel‐miR‐39‐3p and the endogenous miRNA hsa‐miR‐148a‐3p were quantified by qPCR. cDNA template was synthesized using TaqMan Advanced miRNA cDNA Synthesis Kit (Applied Biosystems), following the manufacturers' guidelines. The qPCR product was preformed using TaqMan Fast Advanced Master Mix (Applied Biosystems), TaqMan Advanced miRNA Assay primers cel‐miR‐39‐3p  and hsa‐mir‐148a‐3p . The reactions were processed in a 7500 Fast Real‐Time PCR instrument (Applied Biosystems) using the following settings: 95°C for 20 seconds, followed by 40 cycles of 95°C for 3 seconds and 60°C for 30 seconds. All reactions, including the non‐template controls, were run in duplicates. Ct values were determined using fixed‐threshold and analysed using the Thermo Fisher Connect Software (Thermo Fisher Scientific) available online.2.5P values of <.05 were considered significant.Statistical analysis and data visualization were performed using GraphPad prism version 7.04 . The performance of the extraction kits was compared in terms of (a) their miRNA and total RNA recovery, (b) their hsa‐miR‐148a‐3p yield and (c) their ability to recover the exogenous cel‐miR‐39‐3p. Wilcoxon matched‐pairs signed rank test was used to evaluate statistical significance between the extraction kits. Two outliers were removed from the RNA purity data using the ROUT method33.1Refer to Table P < .05), and the Promega kit yielded higher concentrations compared with Zymo (P < .05).The total RNA concentration after extraction with Norgen Single Cell (NSC) kit was lower compared with Promega, Zymo and Norgen RNA (NR) (P < .05), the Promega kit yielded higher concentrations compared with Zymo and Sigma‐Aldrich (P < .05), and NR yielded higher concentrations than that of the Sigma‐Aldrich kit (P < .05).The NSC kit had lower miRNA yield compared with Promega, Zymo and NR (3.2P < .01), and the Sigma‐Aldrich kit had a higher ratio than Zymo (P < .05). All the included kits produced 260/230 ratios <1 except the Sigma‐Aldrich kit, which was higher than the other kits (P < .01). Also, a higher 260/230 ratio was observed in the Promega kit compared with the NSC (P < .05).Only the Promega and Sigma‐Aldrich kits yielded reasonable 260/280 ratios, Table 3.3P < .05). The Zymo kit recovered the most hsa‐miR‐148a‐3p, significantly more than the NR, the NSC and the Sigma‐Aldrich kits (P < .05). No difference was found between Zymo and Promega, or the Promega and Sigma‐Aldrich kits; NSC had the lowest recovery of all five kits.The Promega, Zymo and NR kits recovered similar amounts of cel‐miR‐39‐3p Figure , showing4miRNA is present in various biofluids and in high quantity in BM, where it has a potential role in regulating gene expression in the breastfed infant.for example the Norgen Single Cell kit. In such cases, the excess miRNA may have been washed away. Secondly, the effectiveness of the column matrix binding capacity may vary. Although the matrices are most commonly constituted of silica, both the Norgen kits in this study used silicon carbide. The Norgen Total RNA kit had quite high miRNA yield, which might be attributed to the binding capacity of the matrix.Even though none of the kits we tested here were specifically designed to be compatible with BM, the majority of the kits performed reasonably well. The varying performance of the evaluated kits may be due to a number of factors that might influence the kits' ability to extract miRNA. Firstly, the input volume may affect the extraction capacity as some kits have a low RNA binding capacity, for example exosomes. The kits varied in their suggested volumes of samples and solutions; in addition, the reagents in the kits had somewhat varying composition. Together, these factors contribute to small but important differences, for example in lysis and binding abilities, and thus limit comparability between the kits and between different studies. In the optimization process proceeding the laboratory work for this study, we made some minor changes to the protocols in order to make them work more efficiently with our samples. Only the Norgen kits had a protocol for biofluids and did not need any major modifications; we followed the protocol described for blood. The other kits, however, were primarily designed for RNA extraction from cells. Hence, we modified the lysis step slightly by adjusting the amount of ethanol added and disregarded the steps for purification of sample from cellular debris. We, however, followed the proportion stated for lysis and ethanol for each kit. It is possible that, although we increased performance with these set of changes, the increased exposure of the samples to ethanol may have contributed to the lower than expected RNA quality in our isolates. However, in our case when we have a low miRNA concentration and likely salt contamination from the lysis buffer, spectrophotometry is potentially a less suitable method to estimate RNA quality.Moreover, variations in the performance of the kits might have been due to differences in the lysis step. The effectiveness of the lysis buffer is an important step to release RNA and miRNA that is not already in solution and to stabilize RNA molecules, including inhibition of RNase activity. In BM, this would, for example, include RNAs that are trapped in extracellular vesicles, that is RNA and DNA, will absorb at 260 nm and common contaminants will appear at the other wavelengths.The Nanodrop measurements of RNA purity are evaluated by the ratio of absorbance at the 260/230 nm and the 260/280 nm spectrum. This is because nucleotides, One strength with this study is that this is the first study, to the best of our knowledge, that uses Qubit for miRNA quantification and not exclusively the Bioanalyzer or Nanodrop in the BM field. We chose to include this method because of the previously reported high selectively and reproducibility of Qubit, as compared to Bioanalyzer and Nanodrop.Lastly, one additional strength with this study is that an exogenous control, cel‐miR‐39‐3p, was added in a standardized amount to all samples prior to the RNA extraction. This control allowed for normalization of technical variability between the samples and was used to assess miRNA recovery. A low coefficient of variance (CV) between the samples within a kit would indicate consistency in the amount of miRNA recovered; in this study, Promega had the lowest CV (4.53%) of the included kits.5ie total RNA extraction efficiency, extraction of hsa‐miR‐148a‐3p and recovery of cel‐miR‐39‐3p), as of today, this would be our primary choice for miRNA extraction from BM. For future validation studies, it is important to keep in mind that the input volume must be standardized, as must the elution volume, to produce comparable results between kits. A spike‐in of an exogenous miRNA is also needed to control for technical variability and for evaluating miRNA recovery. The exogenous control should also be used to normalize levels of endogenous miRNAs and is particularly important in the absence of reliable reference miRNAs comparable to the ‘housekeeping genes’ utilized in mRNA qPCR. Furthermore, we would recommend the Qubit miRNA assay as a reliable, sensitive and reproducible method to estimate miRNA content in RNA isolates.In conclusion, our results suggest that at least two of the extraction kits, the ones from Promega and Zymo, were reasonably efficient in terms of recovery and consistency. Acceptable 260/280 ratios were also observed for the Promega kit, although this kind of spectrophotometry method is not so well suited to evaluate purity in miRNA samples. As the Promega kit was also one of the best performers on the other investigated parameters ; Data curation (lead); Formal analysis (lead); Investigation ; Methodology ; Visualization ; Writing‐original draft ; Writing‐review & editing . Maria C Jenmalm: Project administration (supporting); Supervision (supporting); Writing‐review & editing (supporting). Lina Tingö: Conceptualization ; Data curation (supporting); Formal analysis (supporting); Funding acquisition (lead); Investigation ; Methodology ; Project administration (lead); Supervision (lead); Writing‐original draft ; Writing‐review & editing .