The respiratory system is one of the most affected targets of SARS-CoV-2. Various therapies have been utilized to counter viral-induced inflammatory complications, with diverse success rates. Pending the distribution of an effective vaccine to the whole population and the achievement of “herd immunity”, the discovery of novel specific therapies is to be considered a very important objective. Here, we report a computational study demonstrating the existence of target motifs in the SARS-CoV-2 genome suitable for specific binding with endogenous human micro and long non-coding RNAs , which can, therefore, be considered a conceptual background for the development of miRNA-based drugs against COVID-19. The SARS-CoV-2 genome contains three motifs in the 5′UTR leader sequence recognized by selective nucleotides within the seed sequence of specific human miRNAs. The seed of 57 microRNAs contained a “GGG” motif that promoted leader sequence-recognition, primarily through offset-6mer sites able to promote microRNAs noncanonical binding to viral RNA. Similarly, lncRNA H19 binds to the 5′UTR of the viral genome and, more specifically, to the transcript of the viral gene Spike, which has a pivotal role in viral infection. Notably, some of the non-coding RNAs identified in our study as candidates for inhibiting SARS-CoV-2 gene expression have already been proposed against diverse viral infections, pulmonary arterial hypertension, and related diseases. The virus causing COVID-19 disease, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a member of the coronaviruses family, causing acute respiratory distress syndrome (ARDS), acute lung injury (ALI), and in chronic stages, pulmonary failure and death , making Small interference RNAs (siRNAs) and long RNA antisense locked nucleic acid (LNA) oligos are catching the interest of the scientific community as therapeutic strategies in diverse pathologies, due to low side-effects and high efficiency ratio in vivo ,8. IndeeThis study aimed to achieve two goals: first, to identify the existence of endogenous ncRNAs able to interact with the SARS-CoV-2 genome and transcripts that can serve as a template to design RNA-based sequences with antiviral effects; second, to identify and characterize SARS-CoV-2 motifs that might be recognized by selective ncRNAs and therefore, utilized to design RNA-based antiviral drugs and to increase their efficiency.http://www.ebi.ac.uk/ena/data/view/MN908947.3); 3′- 5′-UTR and the Spike portion have been collected from the same entry. A total of 2656 human miRNA sequences have been downloaded from miRBase (May 2020) [The complete genome of Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1 has been downloaded from ENA (MN908947.3) (ay 2020) . The seqay 2020) : FENDRR,Three RNA–RNA binding site prediction methods have been considered: IntaRNA , RNAplexIntaRNAIntaRNA -t <input_file_query> -q <input_file_target> > <output_file>RNAplexRNAplfold -W <mirna_length> -u <mirna_length> -O --plex_output < <input_file>RNAplex -l <mirna_length> -q <input_file_query> -t <input_file_target> -a ./RNAupRNAup -w <mirna_length> -b -o -3 -5 --interaction_first < <input_file>Examples of the running commands are:https://github.com/lucaparca/mirna_covid (09/02/2021 version).The complete procedure, with command line instructions, parameters, and final script for the merging of the results of the three different prediction methods can be found at the following link: Results of the three methods have been analyzed and merged, giving high priority to consensus matches. Match ranges, the overlap of which has been expressed through the Jaccard indexes of the matching residues in both RNAs, and interaction energy  are then considered . We consEnriched motifs have been searched with the MEME suite  with defnucl) and stability (ΔGhybrid) of target site annealing was used to measure the total energy change of the hybridization   for each nucleotide below 0.05 corresponded to a significantly high propensity of interaction. In contrast, high PU values correspond to miRNA:RNA loops.The interaction propensity between lncRNAs and the Spike transcript, SARS-CoV-2 5′, or 3′UTR was predicted using the RNAfold web tool , followiMiRNAs act as translational repressors as part of an RNA-induced silencing multiprotein complex (RISC) where they target mRNA transcripts at their 3′-UTR with a sequence of 7–8 nucleotides located at their 5’ end, termed miRNA seed ,26.Animal miRNAs can also recognize and bind mRNAs at their 5′UTR, acting as enhancers or inhibitors of translation ,28.We analyzed the 3′UTR and the 5′UTR of the viral genome in order to build up a set of miRNA candidates able to interact with the genome of SARS-CoV-2  and we iInitially, we screened 2656 human mature miRNA sequences using three separate RNA–RNA binding site prediction methods ,18. AlthAfter analysis and data merging, we gave high priority to consensus matches among the three different prediction methods. In detail, we considered only match ranges with a Jaccard index threshold of ≥0.8 and with an MFE <−5 for miRNA BS against the 5′UTR, and an MFE<−20 for miRNA BS against the viral and Spike 3′UTR  motif in their seed  . The “GGAnalysis of the enriched motifs in the leader sequence of SARS-CoV-2 identified three consensus motifs recognized by miRNAs with a GGG-enriched seed and containing G:U pairs . In partp-value < 0.01, two-sided)  . These gThe next step was the logistic prediction of miRNA:leader RNA folding and pairing using the StarMir algorithm , based oIn order to provide a list of the best candidates that might be considered designing RNA-based antiviral drugs, we screened the currently available literature to select only the miRNAs involved in viral response and pulmonary and cardiovascular diseases. Among the thermodynamically stable ones, we identified 35, 16, and 44 miRNA candidates able to bind the leader sequence, the viral 3′UTR, and the Spike mRNA, respectively . FifteenFour miRNAs have been independently identified in a previous work : let-7c-Binding sites for four additional miRNAs on the 5′UTR of the viral genome have been found in another work . InteresExcept for a new class of functional RNAs, i.e., SRA , that arBased on the available literature, we selected 12 potential human lncRNAs with a function related to pulmonary hypertension and cardiovascular and inflammatory diseases, to be considered as candidates for a functional interaction with SARS-CoV-2 in the cytoplasm. a. We alsThree LncRNAs, such as FENDRR, HOTAIR, and LINC01505, were found to potentially interact with Spike mRNA. Given that the secondary structure of HOTAIR has been experimentally determined , we wereOur computational study provides an original starting background to facilitate the identification of novel effective RNA-based anti-COVID-19 therapies as an expedient alternative to “conventional” anti-inflammatory and antiviral treatments.We performed a thorough screening of the viral genome in order to identify sites of possible interaction with human non-coding RNAs by a computational analysis.According to our analysis, we propose a set of miRNAs and lncRNAs as candidates to negatively modulate viral genome expression, possibly resulting in a reduction in SARS-CoV-2 infectivity.Notably, we identified miRNA BS able to bind the mRNA encoding for the glycoprotein Spike, a protein playing a central role in the infection as it facilitates SARS-CoV-2 envelope fusion with the cell membrane and the virus endosomal entrance by interacting with the cellular receptor ACE2. Although ACE2 has been proposed as a target for antiviral treatments , its funAs reported in the results section, we identified a “GGG” motif in the seed of miRNAs binding the viral leader sequence through noncanonical and G:U paired BS. Conversely, this motif was absent in miRNA seeds binding the genomic 3′UTR or the Spike transcript. Indeed, we did not identify conserved motifs among all 2656 human miRNAs, nor references reporting conserved motifs in miRNAs seed sequences that might be used to increase miRNA selectivity against their mRNA targets. The presence of a specific group of miRNAs with UGUGU able to activate targets associated to interferon induction and carcinogenesis has been previously reported . The samThe logistic prediction of miRNA:leader RNA folding and pairing using the StarMir algorithm indicated that leader-enriched motifs might increase the stability of GGG-enriched miRNAs that target the SARS-CoV-2 genome through noncanonical bindings. These motifs might be promising candidate sites to design antiviral RNA-based drugs.The COVID-19 global pandemic and following re-emergence events subjected the public health system to a tremendous crisis. Considering the dramatic respiratory failure to which patients severely affected by COVID-19 are subjected and the potential lethality, priority is to be given in reducing viral-related complications .Endogenous ncRNAs, known modulators of viral and inflammatory mechanisms, can represent a class of molecules offering an evolutionary and adaptive advantage in humans and a more attractive and promising strategy against COVID-19. As observed in this and in previous works, an additional layer of viral infection regulation and modulation is provided by the possible crosstalk between different non-coding RNAs competing for the same binding sites and with RNA binding proteins. The interplay among these molecules can be perturbed and taken advantage of by the viral infection, providing interesting and novel targets for the design of novel therapies. However, ncRNA-based drug specificity against the viral genome may represent an obstacle when modulating endogenous ncRNAs. Here, we have identified and characterized selective motifs located in the SARS-CoV-2 genome, and in the seed of certain miRNAs, which may represent the background to generate viral-selective RNA-based drugs with unwanted side effects.LNA-based anti-miRs are already reported to efficiently target endogenous miRNAs in vivo , and canIn this work, we consider lncRNAs as well, which can interplay with miRNAs, for example by acting as sponges of miRNAs, and as we demonstrated in this work, can bind to the viral genome and Spike transcript. Our data provide novel insights on lncRNAs as additional ncRNA molecules with the potential to interact with the SARS-CoV-2 genome and Spike transcript, and therefore, be candidates to design lncRNA-based oligosequences, in example circular RNAs. Indeed, lncRNAs and cirRNAs are promising novel RNA-based therapeutic strategies, already used in patients with heart failure as well as on wound healing . OverallAlthough strategies such as monoclonal antibodies against Spike , or inhiIt is an important note to readers to consider that our study is not claiming efficacy/potential for a specific treatment. The authors acknowledge that a different, more integrated experimental approach is indispensable to design a clinically testable compound.


Taken together, this system could be applied to the safe and effective antiviral evaluation without using infectious SARS-CoV-2. Because this is a PCR-based and transient replicon system, further improvement including the establishment of stable cell line must be achieved.The development of specific antiviral compounds to SARS-CoV-2 is an urgent task. One of the obstacles for the antiviral development is the requirement of biocontainment because infectious SARS-CoV-2 must be handled in a biosafety level-3 laboratory. Replicon, a non-infectious self-replicative viral RNA, could be a safe and effective tool for antiviral evaluation. Herein, we generated a PCR-based SARS-CoV-2 replicon. Eight fragments covering the entire SARS-CoV-2 genome except S, E, and M genes were amplified with HiBiT-tag sequence by PCR. The amplicons were ligated and in vitro transcribed to RNA. The cells electroporated with the replicon RNA showed more than 3000 times higher luminescence than MOCK control cells at 24 h post-electroporation, indicating robust translation and RNA replication of the replicon. The replication was drastically inhibited by remdesivir, an RNA polymerase inhibitor for SARS-CoV-2. The IC To date, several antiviral drugs such as remdesivir  have been repurposed for COVID-19 therapy2. It is important to develop antiviral agents that can specifically inhibit the propagation of SARS-CoV-2. One of the obstacles for the antiviral evaluation of SARS-CoV-2 is biosafety concern. Because SARS-CoV-2 was classified as a biosafety level-3 (BSL-3) pathogen, it must be handled in a BSL-3 laboratory. The construction of a safe antiviral evaluation system has been coveted.Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has been causing a catastrophic pandemic worldwide. The symptoms of SARS-CoV-2 infection (coronavirus disease 2019 [COVID-19]) ranges from asymptomatic to fever, acute respiratory distress, pneumonia, and ultimately death4. Because the replicon lacks viral structural genes, infectious virions are not produced from the transfected cell, thus reducing the biosafety concern. Additionally, the insertion of reporter gene into the replicon genome enables us to easily monitor the translation and replication of the replicon. The construction of a replicon system would accelerate the development of anti-SARS-CoV-2 agents.The replicon system could be a useful tool for safe and efficient antiviral evaluation. Replicon is a non-infectious, self-replicative RNA that lacks the viral structural genes and retains the genes necessary for RNA replicationbetaoronavirus of the family coronaviridae5. The genome of coronaviruses is single-stranded RNA ranging from 27 to 32 kb, the largest of any other known RNA viruses. Its large genome size and the existence of bacteriotoxic elements hindered the generation of reverse genetic systems and replicon. Several strategies have been adopted to overcome this obstacle: multiple plasmid system followed by in vitro DNA ligation or single bacterial artificial chromosome (BAC) plasmid system8. With these strategies, the infectious clones of SARS-CoV-2 and its reporter variants have been developed13.SARS-CoV-2 belongs to the genus Herein, we generated a SARS-CoV-2 replicon by the in vitro ligation of PCR amplicons. The results demonstrated its use for antiviral evaluation without using the infectious SARS-CoV-2 virion.9  into CHO-K1, BHK-21, or HEK-293T cells to determine the most robust replicon system. In the CHO-K1 cell, the HiBiT signals started to increase as early as 4–6 h post-transfection (hpt), indicating translation and replication of the replicon Fig. . Thus, t9. A decrease was observed for both RNAs at 4 hpt, indicating RNA degradation . The mRNA encoding N gene was also electroporated as a non-replicative RNA controlion Fig. B. At 6–2ion Fig. , indicat15. The ratio of viral protein positive cell was less than 1%. These data indicated that the replicon was successfully constructed and replicative.The viral N protein and NSP8 (a component of RNA replication complex encoded in ORF1a) expressions were confirmed by immunofluorescence assay (IFA) Figs. C,D, S3. 50) and 50% cytotoxicity concentration (CC50) values were calculated to 0.29 μM and more than 50 μM, respectively (selectivity index [SI] > 172.4) 16. A previous study infected Vero E6 with infectious SARS-CoV-2 in the presence of remdesivir, and quantified the virus released in the supernatant by qRT-PCR at 48 h post-infection16. The differences of our replicon assay and previous infectious SARS-CoV-2 assay including cell line (CHO or Vero), incubation time (24 h or 48 h), and action point of analysis  might cause the difference in IC50. Indeed, the difference of the cell line caused different IC50 values of remdesivir17. Nevertheless, the result was generally consistent with the previous report, thus demonstrating that our replicon system could be used for antiviral evaluation.Next, we tested if this RNA replicon could be used for antiviral evaluation. Remdesivir, an RdRp inhibitor effective for SARS-CoV-2, was used as a control compound. In total, 10 μM of remdesivir significantly inhibited the translation and replication of the replicon, whereas dimethyl sulfoxide (DMSO) control did not Fig. A. The 50.4) Fig. B. The IC19. Here, we reported a SARS-CoV-2 replicon system with PCR amplicon-based strategy. The advantage of this system is its technical simplicity. Additionally, this system enabled us to produce a replicon without generating genetically modified E. coli. Thus, bacteriotoxic elements in the SARS-CoV-2 genome do not affect the construction of the replicon. However, the PCR-based strategy might be inferior to the plasmid-based strategy in terms of the yield of replicon RNA and usability of genome modification. Additionally, PCR-based replicon might contain the undesired mutations, which are undetectable by Sanger sequence. Nevertheless, this PCR-based replicon system offered an alternative way over plasmid-based replicon, especially in the resource-limited settings.SARS-CoV-2 is an emergent threat worldwide. A high throughput and safe antiviral evaluation system is urgently needed to identify the anti-SARS-CoV-2 compound, which has not yet been developed. Several plasmid-based SARS-CoV-2 replicons have been reportedThe cells electroporated with the replicon RNA showed more than 3,000 times higher luminescence as compared to the MOCK control cells at 24 hpt Fig. , suggest4. However, only CHO-K1 supported the robust replication of the replicon. Interestingly, even BHK-21, which is defective of IFN production, showed less replication of the replicon20. This was not attributed to the electroporation efficacy because the difference of input RNA in each cell was observed to exhibit an enhancement within twofold  cell (ATCC: CCL-61) was maintained in MEM supplemented with 10% FBS, non-essential amino acids at 37 °C with 5% CO2. HEK-293 T cell (ATCC: CRL-3216) was maintained in the DMEM supplemented with 10% FBS.A clinical SARS-CoV-2 isolate from Japan  was used for the construction of replicon. Baby hamster kidney-21 (BHK-21) cell (ATCC: CCL-10) was maintained in the Eagle’s minimal essential medium (MEM) supplemented with 10% fetal bovine serum (FBS) at 37 °C with 5% COThe viral RNA extracted from the culture fluid of SARS-CoV-2–infected Vero E6 cell  was reverse transcribed into cDNA by the SuperScript III First Strand Synthesis system (Thermo Fisher Scientific) with random hexamer primers. The fragments were amplified by primer sets Table  and highFor assembly, all the fragments were digested with BsaI-HF v2 (New England Biolabs) and purified directly using NucleoSpin Gel and PCR clean-up (Macherey–Nagel). Then, two adjacent fragments of equimolar amount were mixed and ligated with 400 units of T4 DNA ligase (New England Biolabs) at 4 °C overnight: F1 (1.45 μg) and F2 (1.56 μg) for F1–2, F3 (0.86 μg) and F4 (0.85 μg) for F3–4, F5 (1.54 μg) and F6 (1.24 μg) for F5–6, and F7 (1.14 μg) and F8 (0.66 μg) for F7–8. The assembled fragments were electrophoresed on a 1% agarose gel and extracted using Monofas DNA extraction kit (GL Science). Then, extracted fragments were mixed and further assembled with 2,000 units of T4 DNA ligase at 4 °C overnight. The assembled DNA was directly purified by phenol–chloroform–isoamyl alcohol (25:24:1), by chloroform, and isopropanol precipitate. The pelleted DNA was washed once with 70% ethanol, dried by air, and finally dissolved in 10 μl of DEPC-treated water.9. After removing the DNA template following the manufacturer’s protocol, RNA was extracted by phenol–chloroform and isopropanol precipitated. The pelleted RNA was washed once with 70% ethanol, dried by air, and dissolved in 40 μl of DEPC-treated water. The RNA was electrophoresed using DynaMarker RNA High for Easy Electrophoresis (BioDynamics Laboratory. Inc.) for the rough quality check.The replicon RNA was transcribed by the mMESSAGE mMACHINE T7 Transcription Kit (Thermo Fisher Scientific) according to the manufacturer’s instruction with some modifications. Cap analog to GTP ratio was set to 1:1. About 1 μg of the assembled DNA was subjected to RNA transcription. The reaction was incubated at 30 °C overnight. Additionally, a SARS-CoV-2N gene mRNA was in vitro transcribed following a report6 cells) were mixed with 5 μg of replicon RNA in 100 μL of Opti-MEM. Electric pulses were given by NEPA21. The parameters for BHK-21 and CHO-K1 cells were as follows: voltage = 145 V; pulse length = 5 ms; pulse interval = 50 ms; number of pulses = 1; decay rate = 10%; polarity + as poring pulse and voltage = 20 V; pulse length = 50 ms; pulse interval = 50 ms; number of pulses = 5; decay rate = 40%; and polarity + / − as transfer pulse. The parameters for 293 T cell was same as above except voltage 150 V and pulse length of 2.5 ms for poring pulse. After electroporation, the cells were seeded as 1.5 × 104 cells/well in a 96-well plate. At various time points post-transfection, the cells were lysed with 25 μl of Nano-Glo HiBiT lytic detection system (Promega) plus 25 μl of PBS. The luminescence signal was detected by CentroPRO LB962 (Berthold Technologies).The RNA was electroporated using NEPA21 electroporator (Nepagene). The cells were trypsinized and washed twice with Opti-MEM (Thermo Fisher Scientific). The washed cells . Extracted RNAs were then eluted in 50 μl of RNase-free water. The RNA copy number was measured using a QuantiTect Probe RT-PCR Kit (Qiagen). CDC-approved primers (CDC_2019-nCoV_N2-F and CDC_2019-nCoV_N2-R) and a probe (CDC_2019-nCoV_N2-P) targeting the SARS-CoV-2N gene were used29. The probe contained a 6-carboxyfluorescein (FAM) reporter dye at the 5′-end and a Black Hole Quencher (BHQ) at the 3′-end. An in vitro transcribed SARS-CoV-2N gene was used as the RNA standard for the qRT-PCR. The mRNA levels of IFN-β, Mx1, and the housekeeping gene encoding glyceraldehyde-3-phophate dehydrogenase (GAPDH) were measured using an iScript one-step RT-PCR kit with SYBR green (Bio-Rad). The probe and primers used for qRT-PCR were listed in Table Cells electroporated with 10 μg of the replicon RNA or N gene mRNA were seeded at a concentration of 1.0 × 1030.At 24 hpt, the cells were fixed with 4% paraformaldehyde, followed by permeabilization with 0.5% Triton-X. After blocking with normal goat serum, the cells were incubated with primary mouse monoclonal antibodies (mAbs) (anti-N mAb [6H3: GeneTex] or anti-NSP8 mAb [5A10: GeneTex]) followed by a secondary antibody (goat anti-mouse IgG conjugated with Alexa Fluor 488). The cells were mounted in a mounting medium containing 4′,6-diamidino-2-phenylindole (DAPI: Vector Laboratories). Fluorescence images were acquired by a fluorescence microscope. Blue- and green-fluorescence images were merged in ImageJ software4 cells/well in a 96-well plate. The cells were immediately treated with various concentrations of remdesivir. The cells were also treated with 0.2% DMSO as a negative control because 10-μM remdesivir contains 0.2% DMSO. At 24 h post-treatment, the luminescence signal was detected as described above. Cell viability was measured by WST-1 assay following manufacture’s protocol (Roche). The IC50 and CC50 were calculated using a four-parameter logistic regression model from the GraphPad Prism 5 software (GraphPad Software Inc.).The CHO-K1 cells electroporated with 5 μg of the replicon RNA were seeded as 1.5 × 10Supplementary Information


Strikingly, 100 ATP hydrolysis events are needed per successfully refolded ribozyme, suggesting that each round of unfolding requires ten ATP molecules, since 90% of substrate unfolding cycles only lead back to the kinetically favored misfolded state. This near-Sisyphean effort reveals a potentially conserved model for RNA reorganization by RNA chaperones.DEAD-box proteins are nonprocessive RNA helicases that can function as RNA chaperones by coupling ATP binding and hydrolysis to structural reorganization of RNA. Here, Jarmoskaite  Answers have been difficult to ascertain because of the substrate promiscuity and transient interactions characteristic of RNA helicases. Addressing these questions promises to provide insights into the many crucial metabolic processes such as splicing, RNA quality control pathways, and translation regulation mechanisms that rely on RNA helicase-driven remodeling.The three-dimensional structure of a folded RNA is crucial to its processing, localization, and function in diverse cellular environments. As a polymer capable of forming numerous hydrogen bonds, stacking, and ionic interactions, the folding pathways of RNA are far from linear. Even more than proteins, with only four building blocks, RNAs can sample large numbers of thermodynamically exceedingly stable, yet often nonfunctional states that may have limited biological utility. As a countermeasure, energy-dependent molecular chaperones have evolved throughout biology to remodel their molecular substrates into functional conformations. These molecular chaperones couple ATP hydrolysis to physical interactions with their misfolded targets that facilitate the desired structural reorganization into the functional state. Despite their prominent role in cellular homeostasis, details remain largely unknown about how much fuel is required to power productive substrate rearrangement and what the nature of those rearrangements is, Tetrahymena thermophila (The largest class of RNA helicases are DEAD-box proteins, named after a conserved amino acid sequence D-E-A-D. In general, these proteins couple the energy from ATP binding and hydrolysis to conformational changes that lead to nonprocessive unwinding and translocation of double-stranded RNA , 2. Somermophila , 6. In ormophila , 6, 7, 8rmophila . Yet deset al. (In their new work, Jarmoskaite et al.  implemenT. thermophila ribozyme, much ATP ends up being consumed (To answer this question, the authors further investigated what portion of the ATP hydrolysis activity is required for productive ribozyme remodeling. By controlling for background ATP hydrolysis that does not contribute to remodeling, they estimate that ∼100 molecules of ATP are hydrolyzed during productive refolding of a single misfolded ribozyme. This suggests that the majority of the ATP hydrolysis activity is spent on futile interactions with the ribozyme that do not lead to structural reorganization. Further investigation of the ATP consumption rate reveals that, while it does not vary much with either protein or ATP concentration, it does depend on ribozyme structure stability. The authors find that increasing the stability of the misfolded ribozyme leads to an increase in ATP utilization and, conversely, adding destabilizing mutations leads to a decrease. This complements previous findings showing that CYT-19’s ATPase activity is activated by less compact, and presumably more accessible, ribozyme tertiary structures . Togetheconsumed . In thisin vivo remains to be determined as it is unclear how the ribozyme’s native and misfolded states partition in the cellular environment. Future experiments will need to resolve the in vivo population of the misfolded state and how that is coupled to transcription rate and concentration of CYT19 as well as other RNA chaperones that may be able to resolve it into the native state.The emerging mechanism of CYT-19 substrate unfolding is reminiscent of the Greek myth of Sisyphus, in which the deceitful king is eternally condemned by Zeus to the futile task of rolling a boulder up a hill only to have it fall just short of reaching the top. Fortunately, unlike Sisyphus, CYT-19 finally succeeds in resolving the misfolded ribozyme after an average of approximately nine futile cycles . WhetherIt would be reasonable to assume that other DEAD-box and similar DEAH-box proteins with RNA chaperone activities also display ATP usage in steps that are both productive and nonproductive in RNA reorganization. Delineating ATP usage as done for CYT-19 in this work could help inform a unifying picture of how these molecular chaperones reorganize their targets, thereby mechanistically underpinning the multitude of RNA processing pathways in which they are needed. During RNA splicing, for example, insights like this would be highly valuable in understanding the RNA secondary structure rearrangements during both the early assembly and late catalytic stages, which are largely driven by RNA helicases with chaperone activities . Future The authors declare that they have no conflicts of interest with the contents of this article.


Saccharomyces cerevisiae model. We examine how these molecular interactions impact protein localization, cargo import, function and complex formation. We show that reversing the charge of key residues in importin‐α (Arg44) or Cse1 (Asp220) results in loss of function of the respective proteins and impairs complex formation both in vitro and in vivo. To extend these results, we show that basic residues in the Nup2 N‐terminus are required for both Nup2 interaction with importin‐α and Nup2 function. These results provide a more comprehensive mechanistic model of how Cse1, RanGTP and Nup2 function in concert to mediate cNLS‐cargo release in the nucleus.The importin α/β transport machinery mediates the nuclear import of cargo proteins that bear a classical nuclear localization sequence (cNLS). These cargo proteins are linked to the major nuclear protein import factor, importin‐β, by the importin‐α adapter, after which cargo/carrier complexes enter the nucleus through nuclear pores. In the nucleus, cargo is released by the action of RanGTP and the nuclear pore protein Nup2, after which the importins are recycled to the cytoplasm for further transport cycles. The nuclear export of importin‐α is mediated by Cse1/CAS. Here, we exploit structures of functionally important complexes to identify residues that are critical for these interactions and provide insight into how cycles of protein import and recycling of importin‐α occur in vivo using a  Directional transport of cargoes between the nucleus and cytoplasm is mediated by receptors that bind cargo in one compartment and release cargo into a destination compartment. Cargoes that contain a cNLS are recognized by importin‐α in the cytoplasm. Release factors including the importin‐α export receptor, Cse1, and a nuclear pore complex protein, Nup2, ensure efficient cargo delivery into the nucleus. Interactions defined by previous structural studies are required for productive interactions between importin‐α, Cse1, and Nup2 to occur in vivo. Cargo proteins that contain a classical NLS (cNLS) are bound to the major nuclear protein import factor, importin‐β, by the importin‐α adapter, after which cargo/carrier complexes enter the nucleus via nuclear pore complexes (NPCs).GTPase activating protein (RanGAP) in the cytoplasm and the Ran guanine nucleotide exchange factor (RanGEF) in the nucleus.In nuclear transport pathways, cargo release is coordinated by the small GTPase, Ran  have been implicated in the release of cNLS‐cargo: the export factor for importin‐α, Cse1, and the nucleoporin, Nup2.CSE1 and NUP2 cause synthetic growth defects when paired with mutations in the importin‐α gene, SRP1, that alter autoinhibitory function, pointing to their involvement in nuclear protein delivery in vivo.The effect of RanGTP coupled with the autoinhibitory domain is sufficient to modulate the affinity of cNLS‐cargo for importin‐α in vitro; however, the spontaneous dissociation of the cNLS‐cargo/importin‐α/importin‐β complex occurs too slowly to account for the rapid release of protein cargo required in vivo.Crystal structures of the Cse1/RanGTP/importin‐α complex and of the Nup2/importin‐α complexThe crystal structure of the N‐terminus of Nup2 bound to ΔIBB importin‐αIn this study, we test the Cse1/RanGTP/importin‐α and Nup2/importin‐α structural models in vivo by changing residues essential for complex formation and assaying their effects on protein localization, cargo import, function and complex formation. We find that reversing the charge of Arg44 in importin‐α or Asp220 in Cse1, residues that crystal structures indicate are critical for interaction between the IBB domain of importin‐α and the N‐terminal arch of Cse1,22.1Both the crystal structureThe changes introduced into the N‐terminus of importin‐α at amino acid Arg44 lie within the IBB region and could potentially disrupt the interaction with importin‐β, preventing these importin‐α variants from entering the nucleus. As Cse1 interacts with importin‐α after they both reach the nucleus, this potential mislocalization could preclude interaction between importin‐α and Cse1. To verify that the importin‐α amino acid substitutions created do not affect the localization of these variants, each importin‐α variant was tagged at the C‐terminus with GFP and the resulting fusion proteins were visualized in wild‐type yeast cells Figure . Wild‐tySRP1, or the gene for Cse1, CSE1, and containing wild‐type SRP1 or CSE1 URA3 maintenance plasmids were transformed with plasmids encoding wild‐type importin‐α or Cse1, variant importin‐α or Cse1, or vector alone and were plated on control plates or plates containing 5‐fluoroorotic acid (5‐FOA) to select against the URA3‐containing maintenance plasmid.SRP1 and CSE1 are both essential,ΔSRP1 cells expressing R44A or R44K importin‐α grow like wild‐type cells; however, cells expressing the charge reversal variants, R44E or R44D importin‐α, as the sole copy of importin‐α are unable to grow. Similarly, ΔCSE1 cells expressing D220A or D220N Cse1 grow like wild‐type cells, but cells expressing D220R and D220K Cse1 are inviable  forms in the nuclear basket region of NPCs and appears to also involve the nuclear basket protein NUP50.Taken together, structural, biochemical, and studies performed in cells and now in vivo provide insight into mechanisms that ensure efficient import of cargo proteins that contain a classical NLS into the nucleus. Future studies will focus on establishing whether cNLS‐cargo delivery in the nucleus occurs in a sequential or a concerted manner, advancing in a step‐wise progression or proceeding with collaborative, cooperative mechanism. This work also provides the groundwork for exploring the many additional nuclear transport pathways for both import and export that are mediated by other members of the importin‐β family of proteins. In many cases, the signals recognized by these receptors have not yet been defined so the detailed mechanistic understanding of the most well characterized pathway will provide a basis for future studies of these many routes by which cargoes enter and exit the nucleus, including understanding how these pathways are regulated. Such studies are critically important as both nuclear import and export pathways are emerging as promising pathways to target in chemotherapy.44.1S. cerevisiae strains and plasmids used in this study are summarized in Table SRP1 gene encodes importin‐α, CSE1 encodes Cse1, and NUP2 encodes Nup2.All chemicals were obtained from US Biological or Sigma unless noted otherwise. All media were prepared and all DNA manipulations were performed according to standard procedures.4.2GFP‐fusion proteins were localized in live cells using direct fluorescence microscopy to visualize the fluorescently‐tagged proteins in living cells. The GFP signal was visualized using a GFP‐optimized filter (Chroma Technology) on an Olympus BX60 epifluorescence microscope equipped with a Photometrics Quantix digital camera. Cells expressing GFP‐tagged proteins from their own promoters were grown overnight in selective media, diluted in fresh media, and grown for 3 hours prior to localization studies.4.3Protein samples  were resolved on Criterion 4% ‐20% gradient gels (Bio‐Rad), transferred to nitrocellulose membranes (Bio‐Rad) and Myc‐tagged proteins were detected with anti‐Myc monoclonal antibody 9B11 , GFP‐tagged proteins were detected with anti‐GFP rabbit polyclonal antibody , and GST fusion proteins were detected with anti‐GST monoclonal antibody .4.4ΔSRP1 (ACY324), ΔCSE1 (ACY1237) or ΔNUP2/ΔNUP133 (ACY1552) cells containing a wild‐type SRP1, CSE1, or NUP2 URA3 plasmid. Single transformants were grown to saturation in liquid culture, serially diluted (1:10) in dH2O, and spotted on control ura‐ leu‐ glucose plates or on selective leu‐ glucose plates containing 5‐fluoroorotic acid (5‐FOA) . Plates were incubated at 18°C, 25°C, 30°C or 37°C as indicated for 3 to 5 days. To clarify the results of the Nup2 functional assay, cells were picked from the 25°C 5‐FOA plate, grown to saturation at 25°C, serially diluted, spotted on glucose plates lacking leucine and incubated at 30°C.The in vivo function of each of the importin‐α, Nup2, or Cse1 variants was tested using a plasmid shuffle technique.4.5Escherichia coli cells expressing wild‐type Cse1‐GST (pAC1271) were resuspended in PBS supplemented with 0.5 mM phenylmethylsulfonyl fluoride, PLAC , and 2 mM β‐mercaptoethanol and lysed by sonication. Lysates were cleared by centrifugation and incubated with 200 μL of prepared glutathione‐Sepharose beads . Beads were collected and washed three times with PBS then incubated with yeast lysate. To prepare yeast lysates that contain myc‐tagged variants of importin‐α, wild‐type cells (ACY192) containing plasmids encoding wild‐type or variant importin‐α‐myc  were collected and washed twice in dH2O and once in PBSMT . Glass bead lysis was conducted in PBSMT supplemented with protease inhibitors (phenylmethylsulfonyl fluoride and PLAC). Lysates were cleared by centrifugation and total protein concentration was assessed by Bradford assay. Two milligrams of total yeast lysate and 50 μL of Cse1‐GST bead slurry were incubated overnight with agitation. The unbound fraction was removed and the bound fraction was washed twice with PBSMT and twice with PBS. Bound and unbound samples were loaded on a 10% denaturing SDS‐PAGE gel. The proteins were transferred to a nitrocellulose membrane and probed with either primary α‐GST  or primary α‐myc  and secondary α‐mouse HRP‐conjugated antibodies to detect the Cse1‐GST or importin‐α‐myc proteins.For the Cse1/importin‐α binding assay, Cse1‐GST beads were incubated with yeast lysates expressing importin‐α‐myc variants. To prepare Cse1‐GST beads, For the Nup2/importin‐α binding assay, c‐myc beads were incubated with lysates from cells expressing both Nup2‐GFP and importin‐α‐myc. Lysates from wild‐type cells (ACY192) containing plasmids encoding wild‐type or variant Nup2‐GFP  and wild‐type importin‐α‐myc (pAC2326) were prepared as described above. One milligram of yeast lysate was incubated overnight with 20 μL prepared c‐myc beads (Santa Cruz Biotechnology) in a total volume of 500 μL PBSMT. Fractions were prepared as above, loaded on a 10% denaturing SDS‐PAGE gel, transferred to a nitrocellulose membrane, and probed with either primary α‐GFP (rabbit) and secondary α‐rabbit HRP‐conjugated antibodies or primary α‐myc  and secondary α‐mouse HRP‐conjugated antibodies to detect the Nup2‐GFP or importin‐α‐myc proteins.4.6LEU2 plasmids as well as a URA3 importin‐α maintenance plasmid were transformed with the following high copy 2μ TRP1 plasmids encoding; vector (control), Cse1, Cse1 variants , Nup2, or importin‐β and selected on Ura‐Leu‐Trp‐ minimal media. Cells were grown overnight at 25°C to saturation in Leu‐Trp‐ minimal media, cell concentrations were normalized by OD600, and cells were serially diluted and spotted onto control Ura‐Leu‐Trp‐ minimal media (to maintain the wild‐type copy of importin‐α) or Leu‐Trp— minimal media containing 5‐FOA. Growth was examined at 25, 30 and 37°C. Data are presented for the nonpermissive temperature.To assess suppression of the loss of viability of cells expressing nonfunctional importin‐α variants as the sole copy of importin‐α, cells containing importin‐α variants as The authors declare no conflict of interest.


Massive transcriptome sequencing through the RNAseq technology has enabled quantitative transcriptome-wide investigation of co-/post-transcriptional mechanisms such as alternative splicing and RNA editing. The latter is abundant in human transcriptomes in which million adenosines are deaminated into inosines by the ADAR enzymes. RNA editing modulates the innate immune response and its deregulation has been associated with different human diseases including autoimmune and inflammatory pathologies, neurodegenerative and psychiatric disorders, and tumors. Accurate profiling of RNA editing using deep transcriptome data is still a challenge, and the results depend strongly on processing and alignment steps taken. Accurate calling of the inosinome repertoire, however, is required to reliably quantify RNA editing and, in turn, investigate its biological and functional role across multiple samples. Using real RNAseq data, we demonstrate the impact of different bioinformatics steps on RNA editing detection and describe the main metrics to quantify its level of activity. Eukaryotic organisms exhibit quite complex and dynamic transcriptomes whose regulation is essential for all cellular processes and for maintaining the homeostatic state . The comAlu repetitive elements that are widespread in human genes (accounting for around 10% of the human genome)  resides in  genome) . Transcr genome) .Accumulating evidence indicates that A-to-I RNA editing in mammals modulates the innate immune response  and its An important property of RNA editing is that its levels vary across different tissues and cell types. Both the edited and unedited versions of transcripts co-exist in the same tissue or cell and the ratio between the unedited and edited variants is regulated by a variety of factors depending on tissue type or developmental stage. Consequently, quantifying RNA editing, detecting levels of edited variants or measuring the overall editing activity, are crucial for investigating its functional involvement and biological role.A variety of bioinformatics programs and workflows have been released to profile RNA editing in deep transcriptome datasets . Althougphs000424. Raw data were initially inspected using FASTQC and reads were trimmed using FASTP. Then, high quality reads were aligned onto the human genome (hg19 assembly from UCSC) using STAR v.2.5.2b (SRR607967) were aligned to the human genome (hg19 and hg38 primary assemblies) using BWA v.0.7.17 . The robustness of the overall editing metric over the number of RNA editing positions was tested selecting randomly growing numbers of positions from the REDIportal collection and calculating the overall editing per each sampling and “body site.” Then, we measured the Pearson correlation between the overall editing calculated per each group of positions and the same metric detected using the whole database collection, by means of a custom script (pearsonr function from scipy python module).The overall RNA editing level Alu editing index (AEI) was calculated using the methodology by Recoding index was also calculated using a custom python script working on REDItools tables. We considered as recoding sites all 1585 editing positions in REDIportal that are marked as non-synonymous in all three gene annotations available in the database . U-test. Recoding sites were collected per each artery tibial and cerebellum sample from REDItools tables. The comparison was carried out by a custom python script taking into account sites covered by at least 10 RNAseq reads in at least 50% of the samples per group. p-values were corrected for multiple testing using the Benjamini–Hochberg method.Differential RNA editing at REDIportal recoding sites was identified using the non-parametric Mann–Whitney (MW) https://github.com/BioinfoUNIBA/QEdit.Software, command lines, and scripts used in this work are available at the following GitHub repository Profiling RNA editing in whole transcriptome data is yet a challenging task, due to sequencing errors, read-mapping errors, genome-encoded polymorphisms (SNPs), somatic mutations, and spontaneous RNA chemical changes. SNPs and somatic mutations may be partly filtered out using genomic reads from matched whole genome sequencing (WGS) or whole exome sequencing (WXS) experiments, as well as tables of known SNPs from public databases. Alignment and sequencing errors may be partly removed using stringent filters of read and base quality. All of these aforementioned issues require careful design and tuning of computational pipelines to detect RNA editing candidates, as each step or procedure or software can affect the yield and quality of predictions.SRR607967). Raw reads were initially inspected using FASTQC and their low quality regions were removed by means of FASTP. Two datasets were generated, the first containing original raw reads and the second including trimmed reads. Both datasets were aligned onto the hg19 and hg38 reference chromosomes of the human genome using three different aligners, BWA designed for unspliced reads and STAR and HISAT2 optimized for handling spliced reads. Resulting multi-alignments were processed with REDItools in order to provide the distribution of single RNA variants according to a common basic filtering scheme. Known SNPs from the WGS of the same individual (run accession SRR2165704) were removed. In all tested cases, we achieved quite similar distributions, in which A-to-G and T-to-C changes (putative editing events on the direct or reverse strand) are over-represented, suggesting enrichment in true RNA editing events  did not make an appreciable difference , but thede novo or “known”. The former aims to identify all potential RNA editing events of a sample or the hyper-edited regions only without relying on previously known sets of editing positions, while the latter focuses on a restricted number of known changes from literature or well-established databases.Once trimming and alignment steps have been performed, the final list of RNA editing candidates strongly depends on the methodology used to call them. In general, two types of approaches can be pursued, de novo RNA editing events in deep transcriptome data have been released to date. They all suffer from some level of false positives, and each tool requires fine tuning of a variety of parameters that can strongly affect the quality of results and, thus, sensitivity and specificity of predictions  while JACUSA predicted 91,955 putative editing sites  . In thisAlu elements are not present and the number of expected RNA editing candidates is much lower compared to humans  using 3,490,661 unmapped reads by STAR. The alignment onto the transformed human genome yielded 19,377 reads enriched in A-to-G clusters, corresponding to 124,546 RNA editing changes. Of these, only 3586 were present in the filtered list of candidates by REDItools. Consequently, more than 120,000 A-to-G RNA editing events, missed by REDItools in the previous analysis, have been de novo identified in hyper-edited regions. So, events falling in hyper-edited reads should not be excluded a priori since they may represent a considerable fraction of sites. Large scale investigations based on TCGA samples have proven that the number of unique editing sites identified in hyper-edited regions follows the same trend as the AEI index calculated excluding hyper-edited reads  with the entire REDIportal database. Using the RNA editing sites from DARNED  and RADAR , we obtained a correlation with REDIportal of 95%  and 99% , respectively.To evaluate the effect of the number of RNA editing positions on the robustness of the overall editing metric, we randomly selected growing numbers of positions from the REDIportal collection and calculated the overall editing Alu elements, defined as the AEI. As mentioned above, the vast majority of editing activity takes place within Alu elements, with almost every adenosine in the ADAR-targeted Alu repeats being edited to some extent . For example, one may calculate the weighted average over all known recoding sites, known as the recoding editing index (REI) . This meCTSS), FLNA (Q/R) is an important regulator of vascular contraction and blood pressure. Our data and a previous study  affects the protein’s susceptibility to proteolytic cleavage, thus providing a means for a cell to modulate its multiple activity through A-to-I RNA editing  for the above GTEx RNAseq experiments . Our resus study  indicatediseases . Indeed,diseases . Proteoldiseases . Interes editing .IGFBP7 and FLNA transcripts account for more than 90% of all edited Gs, and for the high value of the REI as compared to other tissues. In case one is interested in the distribution, a common practice is to look at graphical visualizations of editing levels through all sites of interest, using, for example, a heatmap plot , requiring ultra-high coverage for reliable detection and quantification. A given position could appear edited in some samples but unedited in others (because of limited coverage), a fact that is often ignored in the statistical testing. Sometimes, when the number of samples is sufficiently high, missing editing levels could be imputed using methods based on the principal component analysis , chainedFinally, the large number of editing sites requires an aggressive multiple-testing correction, and severely limits the statistical power. This leads to an underestimate of the number of differentially edited sites.U-test followed by Benjamin–Hochberg multiple test corrections. For example, such an approach was used to identify many recoding sites differentially edited in cancer compared with normal samples  should be avoided. Indeed, accumulating evidence from large scale projects indicates that RNA editing levels seem to follow a beta distribution rather than a normal distribution  and extrinsic  factors affect the de novo detection that is far from being exhaustive. Averaging over millions of known sites from public databases can help but it requires estimated RNA editing levels that are dependent on a prefixed coverage cut-off that, in turn, drastically reduces the number of usable sites and leads to unreliable, often irreproducible, measures. The weighted average (or an index) over millions of known sites from public database, named here as the overall editing, is a much better solution. However, using this approach one has to rely on a specific set of sites from a given database, a set that might be continuously being modified. In contrast, the AEI is calculated over all tens of millions of genomic adenosines located within Alu sequences and accounts for the editing activity in low covered regions, while avoiding the need to quantify the editing level per-site. An index similar to AEI can be determined for recoding events. However, as the number of recoding sites is much lower, and the current set is known to be very noisy, the REI, while informative in some cases, should be used with care.The accurate detection of A-to-I editing is indispensable to systematically quantify RNA editing and facilitate comparative investigations across multiple samples. Similarly, A-to-I quantification metrics should be carefully selected. Indeed, measuring RNA editing activity across samples counting ad hoc models may be probably required to better perform this task.Identification of differential RNA editing is an important task. Although many studies have been employing various parametric and non-parametric approaches, further investigations are required. Given the non-normal distribution of RNA editing levels, and the strong  effect of variable coverage, phs000424.Publicly available datasets were analyzed in this study. This data can be found here: dbGAP accession CL and DS performed main bioinformatics analyses. SR carried out AEI computations. EE and GP supervised the work. AG and EP conceived the study and designed the analyses. EP drafted the manuscript. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Metastasis is the most devastating stage of cancer progression and often shows a preference for specific organs.To reveal the mechanisms underlying organ-specific metastasis, we systematically analyzed gene expression profiles for three common metastasis sites across all available primary origins. A rank-based method was used to detect differentially expressed genes between metastatic tumor tissues and corresponding control tissues. For each metastasis site, the common differentially expressed genes across all primary origins were identified as organ-specific metastasis genes.Pathways enriched by these genes reveal an interplay between the molecular characteristics of the cancer cells and those of the target organ. Specifically, the neuroactive ligand-receptor interaction pathway and HIF-1 signaling pathway were found to have prominent roles in adapting to the target organ environment in brain and liver metastases, respectively. Finally, the identified organ-specific metastasis genes and pathways were validated using a primary breast tumor dataset. Survival and cluster analysis showed that organ-specific metastasis genes and pathways tended to be expressed uniquely by a subgroup of patients having metastasis to the target organ, and were associated with the clinical outcome.Elucidating the genes and pathways underlying organ-specific metastasis may help to identify drug targets and develop treatment strategies to benefit patients. Metastasis is a fatal step in cancer progression and is the main cause of cancer-related deaths . Tumor mGene expression profiles are widely used to explore organ-specific metastasis. Several groups have developed microarray-based diagnostic tools to determine a tumor’s site of origin , 8 or toIn this study, we investigate organ-specific metastasis by examining gene expression signatures across different tumor types that metastasize to the same organ . For each metastasis site , at least two types of primary site were included in the integrated dataset. Then, a rank-based method was used to detect differentially expressed genes (DEGs) between metastatic tumor tissues and the corresponding control tissues. We focused on common DEGs and enriched pathways across all tissues of origin, and investigated these organ-specific metastasis genes and enriched pathways in breast primary tumors. Cluster analysis and survival analysis were used to test whether the organ-specific metastasis genes and pathways were expressed uniquely by a subgroup of patients with metastasis to the target organ, and whether they were associated with clinical outcomes. In conclusion, we present here an analysis to identify signatures that are specific to the common target organ rather than to diverse primary tumor tissue types. The delineation of the roles of these signatures in the interplay between cancer cells and the target organ will lead to a better understanding of organ-specific metastasis and its susceptibilities to treatment.We searched all public databases for transcriptional profiles with clinical data of primary and metastasis site. Nine metastasis microarray gene expression datasets, covering three metastasis sites  , were coAll of nine metastasis microarray gene expression datasets were collected from the Affymetrix platform with relatively consistent quality control. For the two datasets performed on the customized GPL10379 platform (Rosetta/Merck Human RSTA Custom Affymetrix 2.0 microarray), only samples with high quality control (> 50% tumor cell content) were included in the datasets. Additional, among the five datasets metastasized from breast cancer, only one dataset GSE43837 was specific for the HER2+ subtype, this dataset was not excluded from this study in order to enhance the statistical power in DEG detection.To make full use of the information available from multiple datasets, data from different datasets were integrated using the R package virtualArray . The virGenes that were detected by all platforms Tables  and 2 weRankComp method is based on the relative ordering information of gene expression within each sample. As the relative ordering of gene expression is overall stable for particular types of normal human tissues across common platforms , reversai and j such that We further estimated effect size for the identified DEGs. To estimate the effect size in unpaired data, expressions of each DEG were assumed to follow normal distributions with different variances in condition linskaya , 23:1\don = ni + nj and q = nj/n, the denominator σ could be rewritten as:Let This effect size could, therefore, be linked to the Welch t statistic as:A medium effect size threshold of 0.5 was used to further screen the DEGs that were identified by the RankComp method.In addition, Chi-square test was used to test whether there’s a prominent bias towards available clinical characteristics (such as age and stage) between metastasis tumor and control samples for each dataset in DEG detection.To identify organ-specific metastasis genes, we compared samples grouped according to the metastasis sites. As illustrated in Fig. As individual genes often act in concert and may be responsible for multiple effects, organ-specific genes should be considered in a global context. Separate enrichment analysis was performed for up- and down-regulated organ-specific metastasis genes using known biological pathways , which wbb. Another 1979 DEGs were identified between 34 metastatic brain samples originating from lung cancer and the same brain control samples, denoted as Llb. Between these two lists, 1612 DEGs overlapped, accounting for 87% of Lbb  and 81% of Llb . There was a particularly large overlap for brain metastases from breast cancer and lung cancer  biology processes enriched for up-regulated brain-specific metastasis genes. Table S3. Gene Ontology (GO) biology processes enriched for down-regulated brain-specific metastasis genes. Table S4. Gene Ontology (GO) biology processes enriched for up-regulated liver-specific metastasis genes. Table S5. Gene Ontology (GO) biology processes enriched for down-regulated liver-specific metastasis genes. Table S6. Gene Ontology (GO) biology processes enriched for up-regulated lung-specific metastasis genes. Table S7. Gene Ontology (GO) biology processes enriched for down-regulated lung-specific metastasis genes.


The long non-coding RNA Malat1 has been implicated in several human cancers, while the mechanism of action is not completely understood. As RNAs in cells function together with RNA-binding proteins (RBPs), the composition of their RBP complex can shed light on their functionality. We here performed quantitative interactomics of 14 non-overlapping fragments covering the full length of Malat1 to identify possible nuclear interacting proteins. Overall, we identified 35 candidates including 14 already known binders, which are able to interact with Malat1 in the nucleus. Furthermore, the use of fragments along the full-length RNA allowed us to reveal two hotspots for protein binding, one in the 5′-region and one in the 3′-region of Malat1. Our results provide confirmation on previous RNA-protein interaction studies and suggest new candidates for functional investigations. Mass spectrometry-based proteomics allows identifying thousands of proteins in a single experiment and has been instrumental for screens to identify unknown interaction partners in affinity purification experiments. The first quantitative RNA-protein interaction screens were conducted with in vitro transcribed RNA structures and fragments of mRNA ,2. LaterMetastasis-associated lung adenocarcinoma transcript 1  or also known as noncoding nuclear enriched abundant transcript 2 (neat2 or NEAT2) was originally discovered as a marker to predict metastasis and survival in early-stage non-small cell lung cancer . MALAT1 The long noncoding RNA has been involved in splicing, transcriptional, and posttranscriptional regulation . The intWe decided to measure the RBP interactome for the nuclear 6983 nt murine Malat1 by dividing the lncRNA into 14 fragments of 500 bases each (the last one being 483 nt). Each fragment was amplified by PCR using primers that contained the sequence for the T7 promoter at the 5′-end and the S1 aptamer sequence at the 3′-end . All 14 The RNA pulldown for each Malat1 fragment and the non-related control RNA was performed twice in parallel to allow incorporation of a label switch during the reductive dimethylation. Unbound proteins were removed by washing and the bound protein fraction eluted with LDS sample buffer. Each pulldown was subjected to tryptic digest and reductive dimethyl labeling prior to mass spectrometry analysis on a high-resolution mass spectrometer.The data was analyzed with MaxQuant  and for At this enrichment threshold, we were able to identify enriched proteins at each fragment of Malat1. In agreement with the use of nuclear extracts, 33 of them have been described to localize to the nucleus and 2 were assigned to the mitochondrion. Furthermore, 29 proteins are known to bind to RNA and 20 of them have an annotated RNA recognition domain. Overall, we identified 35 different proteins  and the The binding pattern of the individual proteins ranged from very specific (only enriched with one fragment) to ubiquitous . The number of enriched proteins varied between only 2 enriched interactors for fragment 13 to 20 interactors for fragment 4 and fragment 6. We performed unsupervised clustering of these enriched proteins to investigate whether there is an overall dependency for co-binding and indeed identified 3 major clusters . In the The second cluster contains all interactors whose binding is distributed across all studied fragments. In addition, here, with Hnrnpab, Hnrnph1, Hnrnph2, Hnrph3, Hnrnpm, Hnrnpc, and Hnrnpq , and the possible group member Raly, the hnRNPs form a large group of interactors. In this group, we also find the splicing factor Srsf6 and the splicing enzyme Ppig, the transcriptional regulators Chd9 and Myef2, the polypyrimidine-tract binding proteins Ptbp1 and Ptbp3, the RNA stability regulator Elavl1 , the core DBIRD complex member Zfp326 and the uncharacterized kelch domain-containing protein Klhdc4. Two other proteins, Dlat and Pgam5 are reported mitochondrial proteins and while they are detected to be enriched at two different fragments of Malat1, they might not interact with the lncRNA in the nucleus.In the third cluster, we obtained only 3 proteins . This cluster contains all proteins with broad binding pattern along Malat1, which consequently were enriched on at least 9 of the 14 fragments. These three proteins, one multi-specific RNA helicase and the Adar1/Adarb1 editosome, are nuclear localized and are likely to bind to a large number of cellular RNAs. We might have enriched for these less specific binding proteins through the comparison to an unrelated RNA control sequence that originated from a randomly chosen region of a vector.At least some lncRNAs have been shown to exert their scaffold function by smaller domains as was shown with HOTAIR previously . We thusTo note, our analysis shows the highest correlation exclusively with adjacent fragments and anti-correlation is only seen when the fragments are not consecutive. This strongly suggests that the detected protein interaction pattern is not random and shows that Malat1 has regions with similar binding behavior, suggesting possible functional domains.We performed a quantitative RNA-protein interaction screen using an in vitro RNA pulldown approach for Malat1 with nuclear extract from the murine NSC-34 neuroblastoma cell line. By enrichment analysis, we identified 35 proteins to be able to interact with nuclear Malat1.A similar quantitative RNA pulldown approach has been performed previously with a fragment of human MALAT1 applying stable isotope labeling of amino acids in cell culture (SILAC) as the quantitative strategy. In contrast to this previous experiment that mapped interactors only at a proposed physiological relevant fragment (nt 6918–8441) of human MALAT1, we here mapped interactors to the complete sequence of mouse Malat1 using 14 fragments of 500 nt each. Additionally, we here used nuclear extract instead of whole cell lysate as the lncRNA Malat1 is localized in the nucleus. While the quantitative proteomic strategies are likely interchangeable, the use of shorter fragments, a cell line originating from a different tissue, a nuclear extract and the study of murine Malat1 are sufficient differences to justify slightly diverse results. Comparing our 35 proteins to the human set of 127 interactors, we found overlap for 10 proteins , nearly one third of our candidates. Notably, other candidates from the human MALAT1 experiment are cytoplasmic and it is not clear how relevant they are for the function of MALAT1 given its nuclear localization.Especially noteworthy is that according to conservation analysis the 3′-end of human MALAT1 would be similar to the 5′-end of murine Malat1 . Here agIn addition to RNA pulldown approaches, oligonucleotide capture experiments have been conducted to obtain RBP interactomes for the complete full-length lncRNA: HyPR-MS for human MALAT1  and ChIRIn our screen, we found candidates that were already functionally associated to either mouse or human MALAT1. This includes the interaction with SRSF2, which has a described functional role in the alternative splicing regulation of MALAT1 ,29. FurtThere are some not yet known interactors in our screen that might be interesting candidates for future functional studies. Among the candidates unique to our study are several proteins described to be involved in different cancer types in humans: for example, NOLC1 in multidrug resistant non-small cell lung cancer  , DEK in One advantage of the RNA pulldown approach is the ability to dissect a long RNA into smaller fragments to identify putative functional domains. We here used 14 fragments of 500 nt length to scan changes in the RBP interactome of Malat1. We identified two hotspots for interaction: fragment 3–6 (nt 1001–3000) and fragment 10–11 (nt 4501–5500). Most of our enriched interaction partners are found at these two regions. This suggests that murine Malat1 might have two distinct domains that provide interaction interfaces for proteins or protein complexes that should be tested functionally in the future.Total RNA was isolated from murine NSC-34 cells using the PrepEase RNA Spin Kit  followed by cDNA preparation with the RevertAid First Strand cDNA Synthesis Kit  according to the manufacturer’s instruction. Malat1 fragments were amplified using Pfu Ultra II Fusion HS DNA polymerase  with specific amplification primers  and clonInserts were validated by Sanger sequencing . The fragment sequences were fused to the T7 promoter sequence at the 5′-end and the minimal S1 aptamer sequence  at the 3In vitro transcription was performed for 4 h at 37 °C using 200 units of T7 RNA polymerase  in the supplied transcription buffer containing 13 mM NTPs and 1 µg DNA template. Successful transcription was monitored by agarose gel electrophoresis. RNA was purified by phenol-chloroform extraction. To this end, the transcription reaction was combined with nuclease free water to a final volume of 250 µL. Then, 400 µL Phenol/Chloroform 5:1 pH 4.3–4.7  was added and the mixture was vortexed. After centrifugation for 3 min at 10,000 rpm , the upper phase was transferred into a new reaction tube and 1 mL pure ethanol  containing 15 µg/mL GlycoBlue  was added and mixed. The sample was incubated for 10 min at −80 °C and centrifuged afterwards for 1 h at 4 °C and 14,000 rpm . The RNA pellet was dried for 5 min at room temperature and resuspended in nuclease free water. RNA concentration was determined by measuring the extinction at 260 nm with a Nanodrop 2000 spectral photometer .2, and 70% humidity. Nuclear extracts were prepared basically as described previously [2, 10 mM KCl) for 10 min on ice and collected by centrifugation. Cells were resuspended in two volumes of cold buffer A containing 0.1% IGEPAL CA-630 , 0.5 mM DTT, and a protease inhibitor mix  and sheared with 50 strokes (type B pestle) in a dounce homogenizer. After centrifugation for 15 min at 3900 rpm  at 4 °C, pelleted nuclei were resuspended in two volumes of buffer C . Subsequently the suspension was incubated for 1 h on a rotating wheel at 4 °C and centrifuged at 14,500 rpm  for 1 h at 4 °C to obtain the nuclear extract as supernatant. The protein concentration was established by Bradford  using an UV/Vis photometer .NSC-34 cells were grown in DMEM media  containing 10% FBS  and 100 U/mL penicillin and 100 μg/mL streptomycin  in a humidified incubator at 37 °C, 5% COeviously . In shor2, 1 µM Pepstatin A, 1 µg/mL Leupeptin, and 1 mM PMSF) at 4 °C for 30 min on a rotation wheel. After two washing steps with RNA binding buffer to remove excess unbound RNA, the prepared beads were incubated with 400 µg nuclear extract and 20 µg yeast tRNA  as competitor in RNA wash buffer  for 30 min and 4 °C on a rotation wheel. Unbound proteins were removed by three washing steps with 200 µL RNA washing buffer and bound proteins eluted by heating beads in 1× LDS Buffer  supplemented with 100 mM DTT for 10 min at 70 °C and 1400 rpm in a thermomixer .For each pulldown, 25 µg S1-tagged RNA was immobilized on 50 µL paramagnetic streptavidin beads  in RNA binding buffer  leading to a 28 Da mass-tag (light labeled fraction) or with formaldehyde-d2  and NaBH3CN resulting in a 32 Da mass-tag (heavy labeled fraction). Heavy labeled peptides from pulldowns with Malat1 fragments were mixed with light-labeled peptides from the control RNA pulldown (forward experiment) and vice versa (reverse experiment).Protein samples were separated on a 4%–12% NuPAGE Novex Bis-Tris precast gel  for 8 min at 180 V in 1× MES buffer . After protein fixation and coomassie blue staining for protein detection, gels (one slice per sample) were mince and destained in 50% EtOH/25 mM ammonium bicarbonate (ABC) followed by dehydration in 100% acetonitrile (ACN). Afterwards a reduction reaction was performed by incubating samples for 1 h in 10 mM DTT, 50 mM ABC at 56 °C followed by an alkylation reaction in alkylation buffer  in 50 mM ABC) for 45 min at room temperature in the dark. Gel slices were washed with 50 mM TEAB buffer pH 8.0 and dehydrated again with ACN. Dried gel pieces were subsequently incubated over night with trypsin solution  in 50mM TEAB per sample) at 37 °C. Tryptic peptides were extracted twice with 30% ACN and three times with pure ACN. The mixture with the extracted peptides was concentrated in a speed-vac  to a final volume of ca. 100 µL. Dimethyl labeling was performed as described ,41. PeptPeptides were desalted on StageTips  and sepaContaminants, reverse database hits, protein groups only identified by site, and protein groups with less than 2 peptides (at least one of them classified as unique) were removed by filtering from the proteinGroups file. Missing values were imputed by shifting a compressed normal distribution obtained from the LFQ intensity values to the limit of quantitation. The two-dimensional interactions plots were generated from the filtered MaxQuant proteinGroups output file using in-house R scripts . The qua


Nocardia species remains a challenge due to the complexities of taxonomy and insufficient discriminatory power of traditional techniques. We report the development of a molecular technique that utilizes real-time PCR-based high-resolution melting (HRM) analysis for differentiation of the most common Nocardia species. Based on a novel fusA-tuf intergenic region sequence, Nocardia farcinica, Nocardia cyriacigeorgica and Nocardia beijingensis were clearly distinguished from one another by HRM analysis. The limit of detection of the HRM assay for purified Nocardia spp. DNA was at least 10 fg. No false positives were observed for specificity testing of 20 non-target clinical samples. In comparison to established matrix-assisted laser desorption/ionization-time of flight MS, the HRM assay improved the identification of N. beijingensis. Additionally, all the products of PCR were verified by direct sequencing. In conclusion, the developed molecular assay allows simultaneous detection and differentiation of N. farcinica, N. cyriacigeorgica and N. beijingensis with high sensitivity and specificity.Accurate identification of  Nocardia spp., which are ubiquitous in the environment, are long-neglected opportunistic pathogens, with the majority of infections occurring in immunocompromised and immunodeficient patients [Nocardia farcinica and Nocardia cyriacigeorgica are the most common pathogens causing nocardiosis in various parts of the world [Nocardia beijingensis is frequently isolated in China, Thailand and Japan; however, infections have also been observed outside of Asia in recent years [Nocardia infections has increased [Nocardia.patients . Nocardhe world . Nocardnt years . The maint years . These dnt years . In manynt years . With thncreased . Hence, Nocardia spp. has been based on microscopic morphology and biochemical reactions, which remain challenging [hsp65, gyrB and rpoB have been evaluation for the identification of Nocardia [Nocardia species might still require additional molecular tests for accurate identification [Traditionally, the identification of llenging . Sequencocardia . Howeverocardia . Identiffication .Tm) depends on amplicon length, the GC/AT ratio and nucleotide sequence. Different melting curves can be formed according to fluorescent intensity and the Tm value. The HRM assay has already been used successfully to identify many important viruses, fungi and bacteria [N. farcinica, N. cyriacigeorgica and N. beijingensis at the species level using a novel fusA-tuf intergenic region (IR) sequence.High-resolution melting (HRM) analysis is a method of genetic analysis that can detect single-nucleotide polymorphisms . HRM assbacteria . In the N. farcinica IFM 10152, N. cyriacigeorgica DSM 44484 and N. beijingensis DSM 44636 used in this study were obtained from the DSMZ . In addition to the reference strains, 85 clinical isolates were collected from 15 provinces in China between 2013 and 2018. Isolates were identified by DNA sequencing of 16S rRNA genes, as described elsewhere [blastn program, with species-level calls made by ≥99 % identity.Reference strains lsewhere . BrieflyfusA and tuf genes, encoding elongation factors G and Tu, respectively, are highly conserved genes among bacteria [fusA, the first 56 bp of tuf and the IR between them – designated as fusA-tuf IR – was selected for further analysis.The bacteria . The locfusA-tuf IR locus for three reference strains  were obtained from the published genomes in the National Center for Biotechnology Information databases and subjected to a multiple-sequence alignment using SeqMan in the DNAStar software (fusA-tuf IR using CmSuite software (v 8.0). The PCR primers amplified a 344, 321 and 303 bp fragments for N. farcinica, N. cyriacigeorgica and N. beijingensis, respectively.The sequences of the software . A primeThe genomic DNA of reference strains and clinical isolates was extracted using a QIAamp DNA minikit (Qiagen), in accordance with the manufacturer’s instructions. The DNA samples were quantified using a spectrophotometer  and were kept at −20 °C until further use.−1) and 10.4 µl ddH2O. The amplification protocol consisted of a pre-denaturation at 95 °C for 10 min; followed by 35 cycles of denaturation at 95 °C for 15 s, annealing at 63 °C for 30 s and extension at 72 °C for 30 s. Next, HRM analysis was initiated by raising the temperature to 95 °C for 15 s and decreasing it to 60 °C for 1 min. Then, the melting curves were generated by ramping from 60 to 95 °C at increments of 0.025 °C s−1. All of the amplicons were tested in triplicate to detect technical errors. The HRM data were analysed using QuantStudio real-time PCR software v1.3. The experiments were performed in triplicate.HRM analysis was performed and validated using an ABI QuantStudio 6 Flex system (Thermo Fisher Scientific). Each reaction was set up in a 30 µl volume containing 15 µl 2×TaqMan PCR master mix (Thermo Fisher Scientific), 0.9 µl primer F (10 μΜ), 0.9 μl primer R (10 µM), 0.3 µl ROX reference dye II (100×), 1.5 µl EvaGreen 20× in water (Biotium), 1 µl DNA sample  using sterile ddH2O as follows: 10 ng µl−1, 100 pg µl−1, 10 pg µl−1, 1 pg µl−1, 100 fg μl−1 and 10 fg μl−1. HRM reactions were performed with these serial dilutions being used as templates. Sterile ddH2O served as a blank control. The experiments were performed in triplicate. Results indicate that the limit of detection for N. farcinica, N. cyriacigeorgica and N. beijingensis was at least 10 fg . The assay’s specificity was also assessed by testing 20 non-Nocardia pathogens. These non-target samples included the following: Corynebacterium diphtheriae, Corynebacterium striatum, Corynebacterium simulans, Mycoplasma pneumoniae, Skermania piniformis, Arcanobacterium pyogenes, Staphylococcus aureus, Streptococcus pneumoniae, Stenotrophomonas maltophilia, Staphylococcus haemolyticus, Legionella pneumophila, Klebsiella pneumoniae, Haemophilus influenzae, Neisseria meningitidis, Pseudomonas aeruginosa, a cryptococcal meningitis isolate, Actinobaculum suis, Candida albicans, a Pneumocystis jirovecii pneumonia isolate and Trichosporon. Except for Skermania piniformis, which produced a melting curve at 83.4 °C, the pathogens showed no melting curves.Using the HRM assay, 10 ng genomic DNA of 85 clinical isolates were identified as 58 strains of Nocardia species in clinical isolates, a total of 85 isolates were tested in comparison with the established MALDI-TOF MS method, using a Microflex LT mass spectrometer . The software used for the data acquisition was FlexControl 3.0 . The parameters used were as follows: mass spectra within 2–20 kDa; ion source 1, 20 kV; ion source 2, 18.5 kV; lens, 6.0 kV; and linear detector, 2560 V. Escherichia coli ATCC 8739 was used for mass calibration.To further evaluate the performance of the assay for identification of Samples were prepared as described elsewhere . BrieflyNocardia isolates to the species level. Among the five remaining strains, two N. cyriacigeorgica were assigned to N. farcinica/N. cyriacigeorgica, while three N. beijingensis were identified to the genus level. With the application of HRM, 100 % (85) of Nocardia isolates were accurately identified. HRM showed a higher resolution to differentiate N. beijingensis isolates, indicating our method as a valid alternative to the MALDI-TOF MS assay.The results showed that MALDI-TOF MS identified 80 out of 85 (94.1 %) of In order to further confirm the reliability and specificity of the HRM assay, nucleotide sequence analysis was performed. After HRM analysis, the PCR products of the 85 clinical isolates used in this study were purified with a QIAquick PCR purification kit (Qiagen), according to the manufacturer’s instructions. Sequencing was performed with the same primers as for HRM using an ABI PRISM 7500 sequence detection system (Applied Biosystems), according to the standard protocol of the supplier. All the sequences of the PCR products were identical to the reference samples, confirming the results of the HRM analysis.Nocardia by targeting the fusA-tuf IR locus. This novel locus used in our study, which combines two adjacent protein-encoding genes, took advantage of the non-homologous region between them; thus, giving more discriminatory power. Based on the fusA-tuf IR locus sequence, N. farcinica, N. cyriacigeorgica and N. beijingensis were clearly and reliably distinguished from one another by unique HRM graphs.To our knowledge, this HRM assay is the first assay to specifically identify the main species of 2 concentration strongly influences the melting behaviour of dsDNA, it should be optimized carefully [Although the HRM assay showed high discriminatory power, some aspects may affect this technology. First, proper primer design is essential for the development of the HRM assay. Improvement of the specificity of the primers and avoidance of the formation of primer dimers is necessary . Second,arefully .Nocardia spp. in human clinical samples, such as sputum. The application of the HRM assay to clinical specimens would make the technique accessible to more laboratories, but we were unable to perform this analysis. As nocardiosis is neglected in the clinic, it was difficult to obtain clinical samples and the informed consents of patients.One limitation in this study was that the designed assay was not evaluated for detection of Nocardia species causing nocardiosis can be identified using a single primer pair, in a one-step closed-tube system. (ii) Although reverse transcription-PCR holds great utility, it requires fluorescently labelled probes, which results in a substantial increase in cost. In contrast, the HRM assay uses common and widely available reagents and equipment, along with inexpensive unlabelled oligonucleotides. (iii) After the HRM analysis, samples can be discriminated into groups based on the unique melting profiles and limiting the cost of sequencing to only some representative samples of each group.However, despite this limitation, our study has major advantages. (i) The three N. farcinica, N. cyriacigeorgica and N. beijingensis based on the fusA-tuf IR locus was successfully developed and evaluated. This method represents a rapid, simple and accurate technique to identify Nocardia spp. of clinical interest, and could be used as a potential screening tool for Nocardia strains in basic and clinical laboratories.In conclusion, an HRM method for simultaneous detection of Click here for additional data file.


Whereas cis-encoded sRNAs interact only with their single completely complementary mRNA target transcribed from the opposite DNA strand, trans-encoded sRNAs are only partially complementary to their numerous mRNA targets, resulting in huge regulatory networks. In addition to sRNAs, uncharged tRNAs can interact with mRNAs in T-box attenuation mechanisms. For a number of sRNA-mRNA interactions, the stability of sRNAs or translatability of mRNAs, RNA chaperones are required. In Gram-negative bacteria, the well-studied abundant RNA-chaperone Hfq fulfils this role, and recently another chaperone, ProQ, has been discovered and analyzed in this respect. By contrast, evidence for RNA chaperones or their role in Gram-positive bacteria is still scarce, but CsrA might be such a candidate. Other RNA-protein interactions involve tmRNA/SmpB, 6S RNA/RNA polymerase, the dual-function aconitase and protein-bound transcriptional terminators and antiterminators. Furthermore, small proteins, often missed in genome annotations and long ignored as potential regulators, can interact with individual regulatory proteins, large protein complexes, RNA or the membrane. Here, we review recent advances on biological role and regulatory principles of the currently known sRNA-mRNA interactions, sRNA-protein interactions and small protein-protein interactions in the Gram-positive model organism Bacillus subtilis. We do not discuss RNases, ribosomal proteins, RNA helicases or riboswitches.In bacterial cells we find a variety of interacting macromolecules, among them RNAs and proteins. Not only small regulatory RNAs (sRNAs), but also small proteins have been increasingly recognized as regulators of bacterial gene expression. An average bacterial genome encodes between 200 and 300 sRNAs, but an unknown number of small proteins. sRNAs can be  E. coli by two groups  that interact with their target mRNAs and tRNAs that interact with the 5′ UTR of mRNAs in the so-called T-box riboswitches.cis- and trans-encoded sRNAs. The first sRNAs discovered 35–40 years ago on plasmids and transposons are cis-encoded, i.e., transcribed convergently to their RNA targets from the complementary DNA strand and able to form complete duplexes with them . cis-encoded sRNAs in B. subtilis.The largest group of currently known B. subtilis (txpA/RatA (bsrG/SR4 (bsrE/SR5 (yonT/SR6 (Type I toxin-antitoxin (TA) systems are composed of two elements, a hydrophobic toxin and an RNA antitoxin that neutralizes toxin action by interacting with the toxin mRNA to affect its stability and/or translation. Out of 14 predicted type I TA systems of subtilis , four haxpA/RatA , bsrG/SRbsrG/SR4 , bsrE/SRbsrE/SR5 , and yonyonT/SR6 . The majtxpA/RatA module is encoded on the chromosomal skin element. In ratA knockout strains, the toxin TxpA (59 aa) causes cell lysis on agar plates after 5 days. The 220 nt long RatA overlaps the 3′ end of txpA mRNA by ∼120 nt. The interaction between both RNAs promotes the degradation of txpA mRNA by RNase III, which is required for the viability of B. subtilis and makes RNase III an essential enzyme in this bacterium .The acterium . The secbsrG/SR4 module is located on the chromosomal SPβ prophage. In sr4 knockout strains, the hydrophobic toxin BsrG (38 aa) causes cell lysis on agar plates after overnight incubation at 37°C  is a much weaker toxin than BsrG (bsrE mRNA (256 nt) interact by 114 complementary bp at their 3′ ends, and this interaction causes degradation of the toxin mRNA by RNase III at all three bsrE stop codon positions . The main three differences between both pathways are: (i) the presence of only one U-turn motif in loop L3 of bsrG mRNA but two U-turn motifs, one in L4 of bsrE RNA and another in terminator loop L4 in SR5 which are engaged in the initial contact, (ii) two stem-loops of SR5, SL2 and SL4, are fundamental for formation of a stable duplex with bsrE mRNA, whereas only one stem-loop and a single stranded region of SR4 sufficed and (iii) SR5 binding did not trigger a structural change around the toxin mRNA RBS, whereas SR4 binding did. Therefore, the antitoxin SR5 is monofunctional: It promotes toxin mRNA degradation but does not inhibit bsrE translation directly. Interestingly, both bsrE mRNA and SR5 respond to different stress factors: Whereas bsrG mRNA is only heat-shock sensitive due to its refolding at 48–55°C which results in rapid degradation by RNases Y and J1  and YoyJ (83 aa). The 3′ end of the 100 nt long SR6 interacts with the 3′ end of yonT mRNA to promote its degradation by RNase III. On the other hand, the 5′ end of SR6 binds to the 5′ end yoyJ mRNA, but neither affects the amount nor half-life of yoyJ RNA. Instead, it seems to inhibit yoyJ translation  treatment led to a decrease of both yonT mRNA and SR6.Similarly to bsrH/anti-bsrH (skin prophage. bsrH mRNA (285 nt) and anti-bsrH (200 nt) can interact at their 3′ ends and RNase J1 is involved in cleavage of the complex. But so far, it has not been shown that BsrH (29 aa) acts as a toxin in B. subtilis and that anti-bsrH neutralizes toxin action.Another potential type I TA system is nti-bsrH , b locatcis-encoded sRNAs (antisense RNAs) have been reported: S25, gdpPas/S1559, S1326, S1136-1134 and S1290. S25 (≈1350 nt) is expressed under control of extracytoplasmic sigma factors σX and σM and regulates an autolysin encoded by yabE  was reported to decrease the amount of rpsD mRNA and, consequently, the amount of the small ribosomal subunit ∼1.5 fold under ethanol-stress  has been discovered using a computational search for sRNAs in intergenic regions of the B. subtilis genome and later confirmed by Northern blotting  results in inhibition of ahrC translation initiation by a novel mechanism: induction of structural changes 20 to 40 nt downstream of the RBS, although SR1 binds ∼100 nt downstream of the ahrC RBS , a functional homolog of E. coli RyhB, was discovered (sdhCAB (citB (aconitase) and lutABC (oxidases important for growth on lactate as sole C source). Subsequent studies revealed that FsrA is a global regulator with a broad variety of targets, among them gltAB encoding the iron-sulfur containing enzyme glutamate synthase, dctP (dicarboxylate transporter), resA and qcrA. By predicting complementary regions with RNAhybrid and M-fold the authors suggest that a C-rich single-stranded region of FsrA base-pairs with the RBS of its target mRNAs  inhibit lutABC to allow the direction of iron to higher-priority target proteins. Both target mRNA and protein levels were affected about two-fold by FsrA and FbpB with FbpB having a stronger effect on RNA levels. Whereas FsrA might directly inhibit translation, FbpB might facilitate FsrA/lutABC RNA pairing or recruit the RNA degradation machinery  is the only base-pairing sRNA conserved between B. subtilis and S. aureus. RoxS transcription is activated by nitric oxide depending on the two-component system ResDE , sucCD mRNA (succinyl-CoA synthase) and yflS  suffices. RoxS itself is also subject of RNase Y cleavage at nt + 20 yielding truncated RoxS(Y) which can efficiently regulate ppnKB and is required for translation inhibition of sucCD operon RNA. In contrast to ppnKB and sucCD, yflS is positively regulated: RoxS activates yflS translation by disrupting a structure at the 5′ UTR that impedes ribosome recruitment. In addition, RoxS stabilizes yflS mRNA by preventing 5′-3′ exonucleolytic degradation by RNase J1  of mRNA and protein levels were observed in ΔrnaC strains. RNAC increases the cell-to-cell variation of the AbrB levels, which leads to growth rate heterogeneity of cells within one population during exponential phase. This heterogeneity is physiologically relevant, since slowly growing bacterial cells are less susceptible to antibiotics and environmental stress . T-box leader RNAs are composed of stems I, II and III and a pseudoknot (Stem IIA/B) present upstream of the competing terminator and antiterminator helices . Stem I B. subtilis glyQS (5 M–1 s–1 comparable to that of cis-encoded antisense RNAs (see above).Downstream gene expression depends on the ratio between charged and uncharged tRNAs : Both tRis glyQS  demonstrB. subtilis T-box riboswitch-tRNA complex  and the protein SmpB (ssrB). Together the complex mimics the structure of tRNAAla, is bound by EF-Tu-GTP and can be loaded onto stalled ribosomes. The globular domain of SmpB acts as a substitute of the anticodon-loop and occupies the decoding site, while the C-terminal tail of SmpB probes the vacant mRNA channel of the 30S SU (rev. in B. subtilis (A)GKTNSFNQNVALAA. Finally, the ribosome is released and the truncated and tagged protein degraded by Clp proteases that recognize the ALAA-motif  to regulate its activity , pRNA:6S RNA-dissociation (koff) and refolding (kconf) determines whether or not pRNAs dissociate or refold the 6S-1 RNA.The ≈200 nt long 6S RNA is ubiquitous in bacteria, and l bulges . In 2000activity . It interomoters . Crystalform DNA  allowingform DNA . 6S RNA  E. coli . Both B.ynthesis . 6S-1 isynthesis  whereas ucidated : As soonFuture investigations will reveal the function of 6S-2 and show which mechanisms mediate promoter-specific transcription regulation by both 6S RNAs.B. subtilis TRAP (trp RNA binding attenuation protein) and various antitermination proteins.Examples for transcription attenuation  and another IRE between feuA and feuB (iron uptake). In addition, it binds at a stem-loop in the 3′ UTR of gerE mRNA encoding a transcription factor in sporulation, which might stabilize the RNA to ensure proper timing of spore coat formation  of all three kingdoms of life to convert citrate into isocitrate. For this activity, it needs a saturated iron-sulfur (FeS) cluster. Under iron limitation, the FeS cluster disassembles causing enzyme inactivation and, therefore, TCA shutdown. The inactive enzyme adopts an alternative conformation to bind at iron-response elements (IREs) located in the 5′ or 3′ UTR of mRNAs to repress translation or RNA degradation, respectively. The first bacterial aconitase discovered to bind RNA was lis CitB : Monomerormation . Howevernslation . Surprisn solved . Under F 1 and 2 . Direct  1 and 2 . Protein as well , althougB. subtilis small proteins that interact with larger proteins . It interacts with GapA, one of the two glyceraldehyde-3P-dehydrogenases (GAPDHs) in B. subtilis. SR1P stabilizes gapA mRNA by preventing its rapid degradation under gluconeogenesis  previously known as S1136 (sr7 gene is located in the intergenic region between tyrS and rpsD and controlled by a σB-dependent promoter. SR7P is synthesized under five different stress conditions from the σB-dependent SR7 as well as constitutively from a tyrS mRNA processing product. SR7P interacts with enolase present in the degradosome. This interaction in turn improves enolase binding to RNase Y. The SR7P-Eno-RNase Y interaction is not bridged by RNA. The activity of RNase Y is significantly higher in the tri-component SR7P/Eno/RNase Y complex than in the Eno/RNase Y complex alone. This modulating effect of SR7P was demonstrated both in vivo and in vitro. In addition, SR7P impacts cell survival under selective stress conditions suggesting that it might play a specific role in stress response.as S1136  is encodas S1136 . The sr7B. subtilis, major cavities in our knowledge still exist. Only for four of 108 trans-encoded sRNAs and for 8 small proteins, interaction partners have been identified and biological functions elucidated. Furthermore, a global approach is required to ascertain if CsrA plays a similar role in B. subtilis as Hfq and ProQ in Gram-negatives. The function of the putative RNA chaperones FbpA, FbpB and FbpC has to be unraveled. Targets of the multitude of sRNAs have to be identified and their – perhaps sometimes novel – mechanisms of action elucidated. A considerable increase in the number of dual-function sRNAs can be anticipated. sRNAs might be found that bind enzymes or act directly on the genome like some eukaryotic siRNAs. Structures of more antiterminator/protein complexes and CitB have to be solved and the function of 6S-2 RNA uncovered. The further study of small proteins which is still in its infancy, will allow insights into regulatory networks comprising not only small proteins modulating large proteins or the membrane but also those binding small ligands.Although a variety of intermolecular interactions have been investigated in SB wrote the sections on RNA-RNA interactions, 6S RNA, terminators/antiterminators, CitB., and designed The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.