Total hip replacement is one of the most widely performed surgeries. It is stated as the most efficient method of treating end-stage osteoarthritis of the hip joint. What is more, it significantly improves the quality of patients’ lives, relieves them from pain and restores decreased range of motion, provided that is conducted properly. Aim of this article is to indicate which constituents of prosthetic placement can be easily measured on postoperative radiographs and point out how to interpret obtained results. Multiple mechanical factors, such as center of rotation, femoral offset, acetabular offset, acetabular inclination, acetabular anteversion and leg length discrepancy can be measured on postoperative radiographs. To provide a successful surgery and to acquire both radiological and clinical satisfying results, proper prosthetic placement is crucial. Malpositioning of each element, in varying degrees may lead to dislocation or reoperation. Total hip replacement (THR) is one of the most widely performed surgeries. There were 98.649 hip replacement procedures performed in 2019 in the United Kingdom [In everyday clinical practice, radiographs are the most widely used tool for imaging. It is the most widespread and commonplace method. Classical radiography is associated with a lower dose of radiation in comparison to computed tomography. Its advantage over other imaging methods results also from the lowest price. Computed tomography can be used for postoperative assessment as well. It is certainly more accurate than radiographs, but it has its limitations, such as radiation dose and limited availability . There iIn connection with this, in the majority of cases, radiographs are perfectly adequate. What is more, the application of this imaging technique is not associated with the artifacts caused by metal implants, in contrast to the use computed tomography or magnetic resonance.Aim of this article is to indicate which constituents can be measured on postoperative radiographs and point out how to interpret obtained results.Multiple mechanical and biomechanical factors, such as center of rotation (COR), femoral offset, acetabular offset, acetabular inclination (AI), acetabular anteversion and leg length discrepancy can be measured on radiographs. Each one of them, in varying degrees, can affect the outcome of the surgery.The literature was reviewed. Articles related to the subject, published in the years 1978–2021 were researched. Therefore publications including keywords such as total hip replacement, center of rotation, femoral offset, acetabular offset, cup anteversion, cup inclination and leg length discrepancy were searched in the PubMed database. Research was focused on English language papers, available abstracts, studies performed on people and articles. Inclusion of the articles was determined on the basis of titles, then abstracts, eventually entire articles. As terms of exclusion non-English language articles, papers ahead of print and only titles or abstract available were chosen. All studies presenting procedures conducted on animals were also excluded. If there occurred any signs of unreliability or relation to the topic were insignificant, the articles were eliminated during further evaluation. Afterwards, every selected article was verified another time. Any duplicates or obsolete information were removed. Research and error risk assessments were performed by one author. All information was selected and verified individually. Analysis and synthesis of studies were prepared independently.All methods of measurements apply to radiographs taken in the supine position at a source-to-film distance of 100–115 cm with the X-ray beam directed to the midpoint of the pubic symphysis and perpendicular to the patient.The described radiographic technique is a non-weight-bearing view. It is connected with the lesser radiation dose, allows to obtain a better quality radiographs, but it does not take into account the functional anatomy in contrast to weight-bearing view.One of the goals of THR is to reconstruct the COR. As the study shows, restoring COR is an extremely important factor affecting operation result. It should be restored within 5 mm medial and 3 mm superior to the normal side. If optimal reconstruction is unattainable, the ability to control hip offset meticulously is limited. Shifting COR over 1 cm superiorly or medially causes early radiological signs of loosening. Malposition of COR may lead to abnormal gait, abductors insufficiency, increased risk of impingement and dislocation . Proper Ranawat’s method is an old but still applicable method used for the definition of the COR . To deteFemoral offset is stated as a distance from the COR of the femoral head to a line bisecting the long axis of the femur  Fig. 2)2). RestoIn addition, femoral offset restored within 5 mm to the normal side, decreases both linear and volumetric polyethylene wear . FailingHip offset is not only femoral offset. There is another component part-acetabular offset, which should not be omitted because its value varies from person to person. Acetabular offset is the distance from COR to the medial wall of the quadrilateral plate , 12. It Acetabular inclination is also known as an abduction angle. Radiographic AI is measured on anteroposterior radiographs. It is an angle between transischial line and a line conducted through the cup margins  Fig. 4)4). AI afComputed tomography facilitates accurate determination of acetabular cup position, especially anteversion, but it is not widely used in clinical practice because of its high cost, limited availability and additional radiation exposure , 18. BecLiaw’s anteversion is stated as:α is an angle between the principal axis of the ellipse and the vector connecting the endpoints of the main and the minor axes. In plan, ellipse is located on the margin of a cup 6).Fig. 6To provide a successful surgery and to acquire both radiological and clinical satisfying results, all of the mechanics and biomechanics must be restored, if it is not, surgery can aggravate the patient’s complaints instead of removing them and the results of performed THR can be unsatisfactory. This may also result in dislocation or the need for reoperation.


Liquid–liquid phase separation (LLPS) has assumed a prominent role in biological cell systems, where it underpins the formation of subcellular compartments necessary for cell function. We investigated the underlying mechanism of LLPS in virus infected cells, where virus inclusion bodies are formed by an RNA-binding phosphoprotein (NS2) of Bluetongue virus to serve as sites for subviral particle assembly and virus maturation. We show that NS2 undergoes LLPS that is dependent on protein phosphorylation and RNA-binding and that LLPS occurrence is accompanied by a change in protein secondary structure. Site-directed mutagenesis identified two critical arginine residues in NS2 responsible for specific RNA binding and thus for NS2–RNA complex driven LLPS. Reverse genetics identified the same residues as essential for VIB assembly in infected cells and virus viability. Our findings suggest that a specific arginine–RNA interaction in the context of a phosphorylated state drives LLPS in this, and possibly other, virus infections. In the past, many efforts have been put into understanding the mechanism of liquid–liquid phase separation (LLPS) in artificial biomimetic systems, but recently, there have been many studies on how LLPS may influence cellular activities. An increased interest in LLPS in natural system has revealed that LLPS are fundamental to most cellular processes and complex sets of phase transitions may underpin the formation of several subcellular compartments. LLPS may form small macromolecular condensates or more often form large subcellular compartments known as membrane less organelles (MLOs) .Many viruses also develop their own, membrane-less compartments in the infected cells, where genome replication and virus assembly occur, which are referred to as ‘virus factories’, ‘viroplasm’ or viral inclusion bodies (VIBs) (reviewed in ) 5–11)..5–11). SReoviridae family, where significant details of VIB assembly and disassembly are known . Therefore, here we first studied these three factors individually 2. For the BS8 (BSR cells stably expressing NS2) cells, the media was additionally supplemented with the selection marker puromycin. For BTV infection, BSR or BS8 cell lines were used as previously described (Spodoptera frugiperda (Sf9), were maintained in Insect-XPRESS™ Medium and grown at 28°C.BSR cells (BHK-21 subclone) were maintained in Dulbecco's modified Eagle's medium (DMEM) supplemented with 5% fetal bovine serum (FBS) and grown at 35°C in 5% COescribed . For BTVescribed . Insect Sf9 insect cells with recombinant baculoviruses at an MOI of 5, with cells harvested at 70 hours post-infection. Plasmid pET15b was used for non-phosphorylated NS2 expression in bacterial cell systems as previously described (NS2 mutant plasmids were generated via the QuikChange mutagenesis method (Agilent Technologies) and were inserted into the baculovirus transfer vector pAcYM1-S·tag for recombinant protein expression in insect cells. Phosphorylated NS2 proteins were expressed following the infection of escribed . NS2 proescribed .350nm using the DS-11 nanodrop instrument (DeNovix). The threshold used was determined by observing multiple samples under the microscope and monitoring the OD350nm. Solid phase separations appeared as irregular shaped liquid droplets , while liquid–liquid phase separated samples had circular droplets . We adopted a figure between these ranges as the cut-off to ensure clear separation between true LLPS and aggregation. Any conditions that generate solid phase separation or resulted in precipitation in the samples were excluded in the phase boundary plots. The NS2–RNA (Segment 10) complexes were produced via the heating and snap cooling method. Briefly, a calculated amount of RNA was heated to 70°C prior to mixing with the NS2 protein sample on ice, where the complex was allowed to settle for at least 15 minutes.Recombinant protein was purified and concentrated to a stock concentration of 16 mg/ml. Tubes were prepared with increasing concentrations of proteins, ranging from 25 to 100 μM. Each protein concentration was then incubated in 20 mM Tris–HCl, pH 7.4 containing increasing salt concentrations: 25, 50, 100, 150 and 200 mM. The samples were centrifuged for 10 min at 14 000 RPM to remove any solid phase separated species which may have formed. For a direct measurement of phase separation in each reaction, the absorbance readings of a 200 μl reaction volume (0.5 ml tubes as cuvette) were measured at ODts as in  and had For bright-field microscopy visualization of phase separation, 10μl aliquots of NS2–RNA complexes were added to each well of a KOVA™ Glasstic™ Slide (Fischer Scientific) and were incubated at 37°C in a humid chamber for either 40–50 or 120 min, prior to analysis using an inverted microscope . For visualization of NS2–RNA complexes via confocal microscopy  the samples were stained for NS2 using an in-house guinea pig anti-NS2 primary antibody and an anti-guinea pig Alexa 488 coupled secondary antibody (Thermo Fisher Scientific). To visualize colocalization of RNA with NS2 in phase separated droplets, RNA was stained with GelRed® (Fisher Scientific). For VIB visualization in fixed cells, BSR cells grown on coverslips were infected with BTV (mutant or wild-type) at an MOI of 0.75 or 2, before fixing with 4% paraformaldehyde solution (Sigma-Aldrich/Merck) at 4h or 8h post-infection. Infected cells were then permeabilized with 0.5% Triton X-100 (Sigma/Merck) and blocked with 1% bovine serum albumin (BSA) (Sigma/Merck). Intracellular NS2 staining was carried out as previously stated and Hoechst 33342 (Thermo Fisher Scientific) was used for nuclei staining. For the 1,6-hexane diols  treatment experiments, BSR cells infected with BTV at an MOI of 2 were treated with 3.5% 1,6-hexane diols or polyethylene glycol 200 at 8h post-infection. After 8 min of treatment, cells were fixed and stained for NS2 prior to confocal microcopy analysis. For the 1,6HD removal assay, fresh media was exchanged after 8 min of treatment and the cells were then fixed after 22 min of the media exchange, stained for NS2 and analyzed via confocal microcopy. For live cells imaging, briefly, BSR cells were seeded in μ-Slide I Luer  at a confluency of 40–50% and infected with green fluorescent BTV DISC strain at an MOI of 1–2 . Live or2/dmol for representation (The far-UV circular dichroism (CD) spectra of purified wtNS2 and the NS2 mutants were recorded in CD buffer  at room temperature (20°C). For the RNA binding experiment, NS2 protein solutions (5.4 μM) at 4°C were aliquoted into four separate tubes at a volume of 150 μl prior to the CD reading. RNA segment 10 (S10) at 12 mg/ml was heated to 70°C before 0.1–2.0 μl of RNA solution was added to each NS2 tube (on ice), corresponding to a final RNA concentration of 0, 0.02, 0.26 and 0.46 μM RNA, respectively. The CD spectra of each tube containing RNA-NS2 complex was recorded along with that of RNA only samples of corresponding concentration. The spectra were collected in the 260–195 nm range with a 0.5 mm rectangular cell path length at 20°C, using a Chirascan & Chirascan Plus spectrometer  attached to a Peltier unit . Each CD and UV spectra (for concentration measurements) were smoothed  and analysed using Origin V6 and APL Prodata Viewer v4.2.15 software. The spectra of the RNA–NS2 complexes with the varying RNA concentrations were normalized using a biosimilar chirality method at a single wavelength (208 nm) in mdeg . The ploentation .in vitro kinase assay was performed on substrate protein NS2 (6X-His tag) using the kinase enzyme, Casein kinase 2 (CK2α) (GST tag); both were expressed separately in E. coli cells and purified using nickel or glutathione sepharose beads respectively. The kinase reaction was performed in a 50 μl reaction-mixture volume using 1X Tris reaction buffer  as described previously ) and incubated for 15 min to allow the complex to stabilize. The RNA–NS2 complexes formed were run onto a 0.8% agarose gel in TBE (Tris–borate–EDTA) supplemented with ethidium bromide at a concentration of 0.2 μg/ml and analysed using a gel imaging system G: BOX (Syngene). For competition assays, a 0.8 μM concentration of each wtNS2 or mutant protein, was added to 1 μg of heated S10 RNA in the presence of a 100-fold molar excess of tRNA. The resulting NS2–RNA complexes formed were analysed on 0.8% agarose gel in TBE  and assessed by optical density (OD) at 350 nm, weak phase separation was evident only at low salt concentrations (∼50 mM NaCl)  Figure . The add) Figure . However) Figure . Thus, cThe secondary structure of proteins, particularly IDRs play decisive mechanistic roles in phase separation ,32,33. TProtein-RNA condensate formation has been shown to be regulated by protein phosphorylation ,35. NS2 32P]-NS2 produced. While there was no phosphorylation in negative control S249A + S259A mutant NS2 protein  at increasing concentrations (from 0.1 to 10 μM) with a constant concentration of NS2 (2 μM) to vary the phosphorylation state, validating each reaction by observing intensity of [ protein , which fm Figure . Insteadn Figure  than then Figure . These d6A + R7A, K160A + K162A and K281A + K283A were equivalent to the wild-type NS2 (wtNS2), in both size and stability , (aa153–166 and (aa274–286) . These ry Figure . We compy Figure  associaty Figure . Thus, aKD, of 0.91 (±0.08) μM demonstrating strong binding of RNA by wtNS2  μM while mutants K160A + K162A and K281A + K283A mutants retained RNA binding with a KD of ∼1.2 (±0.1) μM and 1.32 (±0.09) μM respectively  was incubated with increasing amounts (from 0.2μM to 4μM) of each of the three mutant proteins alongside the recombinant wtNS2 as a control. Protein–RNA complexes were then analysed using an established electrophoretic mobility shift assay (EMSA) ,31. Prev2 Figure . In conty Figure –D. Moreoy Figure . These d350nm. While nucleoprotein complexes for the mutants K160A + K162A and K281A + K283A showed a minor decrease in phase separation when compared to wtNS2, there was a significant loss of condensate formation for the R6A + R7A mutant, consistent with a role for this region in phase separation  was incubated with S10 RNA for 1 h, and the resulting condensates were analysed at ODn Figure . Since Ln Figure . Phase cn Figure  or in thn Figure  failed tn Figure . Howevern Figure . FurtherS Figure . Mutants0 Figure , G.To confirm that the observed droplets contained NS2–RNA complexes, we stained them with a fluorescent anti-NS2 antibody and visualized via confocal microscopy. When purified phosphorylated protein (∼4 mg/ml) was incubated with S10 RNA (1.0 mg/ml) for 20 min, two populations of NS2 (green) were visualized, one forming large droplets, indicating phase separation, while the other population appeared as a scattering of very small granules Figure  whose nu6 and R7 mutations, virus recovery was performed using a novel complementary cell line of BS8 cells expressing an incorporated wtNS2 protein. Both mutant and wt viruses were recovered at the comparable level and phenotype ..6 and R7e Figure , E. ThesOver the last two decades many studies have focused on virus inclusion bodies which are essential for the accumulation of virus components, genome packaging and assembly of progeny virions ,42. InfeAn outstanding question has been how a single protein is able to capture all the virion components and produce an environment conducive to virus assembly. NS2 is disordered and a three-dimension structure for the entire molecule is lacking. A structure has been solved for an N-terminal fragment aa 8–160) which is consistent with a role in RNA binding and oligomerisation but fails to explain any other ascribed NS2 function . An incr0 which iUsing an electrophoretic mobility shift assay (EMSA) specific RNA binding was mapped to Arg 6 and 7 with no apparent role for lysines in the central and C-terminal domains . The gua2+ sensing  or multiple virus replication cycle was therefore avoided. Broadly, our findings are consistent with other reports, suggesting that LLPS in different viruses, even in unrelated virus families, primarily depends on three factors: an IDR, phosphorylation and RNA binding . The NS2 predicted model is available from the corresponding author or the direct download link (BTV_NS2.pdb).gkac904_Supplemental_FilesClick here for additional data file.


Recent evidence indicates that the HIV-1 Integrase (IN) binds the viral genomic RNA (gRNA), playing a critical role in the morphogenesis of the viral particle and in the stability of the gRNA once in the host cell. By combining biophysical, molecular biology, and biochemical approaches, we found that the 18-residues flexible C-terminal tail of IN acts as a sensor of the peculiar apical structure of the trans-activation response element RNA (TAR), interacting with its hexaloop. We show that the binding of the whole IN C-terminal domain modifies TAR structure, exposing critical nucleotides. These modifications favour the subsequent binding of the HIV transcriptional trans-activator Tat to TAR, finally displacing IN from TAR. Based on these results, we propose that IN assists the binding of Tat to TAR RNA. This working model provides a mechanistic sketch accounting for the emerging role of IN in the early stages of proviral transcription and could help in the design of anti-HIV-1 therapeutics against this new target of the viral infectious cycle. Protein–nucleic acid interactions can occur through different types of protein binding domains and are responsible for a variety of essential molecular and cellular mechanisms and their regulation. This binding diversity has been well described by recent RNA interactome screenings revealing that the term RNA-Binding Domain (RBD) is no longer synonymous of a well-structured domain, but it also includes intrinsically disordered regions (IDR) endowed with non-canonical RNA-binding properties ,2,3. Mor2+ cofactors and folds similarly to nucleotidyl-transferases and nucleases -1-propanesulfonate), and 1 tablet of Protease Inhibitor Cocktail . The preparation was sonicated for 120 s on ice, then the resulting lysate was subjected to centrifugation at 11,000× g for 1h. The recovered supernatant was then applied to a HisTrapTM Fast Flow Crude column  and purified by an AKTA pure system . The sample was first abundantly washed with a lysis buffer containing 100 mM imidazole and 2M NaCl, then the protein was eluted using a gradient up to 500 mM imidazole in 10 column volumes. A second step of purification was carried out using a Superdex 75 10/300 GL column  with an isocratic elution carried out with a storing buffer . Mammalian FlagIN-FL (IN-FLm) was expressed in Baby Hamster Kidney suspension cells  using a vaccinia virus expression system as previously described [Construction of plasmid pET15b encoding N-terminal 6XHis tagged IN-CTD (aa 220-270) and IN-CTD-ΔCT (aa 220-288) were previously reported . The IN-escribed . IN-CT pescribed ,66. Thisescribed .32P-ATP  for 1 h at 37 °C then purified on denaturing 10% (w/v) polyacrylamide gel (29:1) as previously described [2, 2 mM DTT) for 3 min at 95 °C followed by 40 min of slow controlled cooling to room temperature and finally placed on ice.We produced several RNAs as listed in escribed . Before 2, 0.2 µg BSA, and 8% (v/v) PEG8000. The samples were incubated at 37 °C for 30 min before being resolved by native 6% polyacrylamide (19:1) gel electrophoresis in a 0.5x TAE (Tris acetate EDTA) buffer. Results were analysed by phosphorimaging using ImageQuant software. IN-FLm was incubated for 2 h at 4 °C with the RNA substrate. For the dose-dependent competition assay shown in D. Then, we added increasing concentrations of Tat.The electrophoretic mobility shift assays (EMSA) were performed as described in . SamplesEnzymatic treatments were performed in 10 µL of the reaction mix containing 0.5 pmol of 5′ radiolabeled RNA, 0.2° µg of yeast tRNA, 1 × Structure buffer , and 0.01 U of RNase T1 . Incubation was done at 37 °C for 5 min. Reactions were stopped by the addition of 40 µL of a quenching buffer composed of 10 mM HEPES pH 7.5, 1 mM EDTA, and 3% SDS. Partial alkaline hydrolysis was performed as follows; 10 µL of reaction mix containing 0.5 pmol of RNA, 1 µg of yeast tRNA, and 1× Alkaline Hydrolysis buffer were incubated at 95 °C for 12 min then quenched with 2× denaturing loading buffer and placed on ice. For RNA/protein complexes, 0.5 pmol RNA were previously incubated with a 36 µM protein  at 37 °C for 30 min then treated with RNase T1. After quenching, samples were phenol extracted and ethanol precipitated. After recovering from precipitation, all samples were run on a 15% sequencing polyacrylamide gel in 0.5 × TBE. (Tris Borate EDTA). Results were analysed by phosphorimaging.2, 2 mM β-mercaptoethanol and 5% glycerol) adjusting the final NaCl concentration to 150 mM. Samples were complemented or not with 600 pmol of 3′ end–biotinylated TAR  on ice and proteins were eluted with an SDS loading buffer and analysed on a polyacrylamide 16% (37.5:1) SDS-PAGE. For the Histidine pulldown assay, magnetic streptavidin resin was replaced with HisPur™ Ni-NTA Magnetic Beads  and protein elution was done with a 0.5 M Imidazole containing buffer.The pulldown assays were conducted as described before . Brieflyated TAR  in a fin2, 1 µM ZnSO4, and 0.5 M BSA. For the BLItz platform, we used Streptavidin (SA) Biosensors , and for the Octet RED96e, we used the Streptavidin (SAX) Biosensors  that were hydrated for 10 min in an assay buffer then plunged in a solution containing 1 µM 3′ end–biotinylated TAR RNA in an assay buffer with a 1 × RNAase inhibitor  and 0.1 µM BSA for the RNA loading step. A wash was performed after RNA loading. In the BLItz experiment, the association of the protein to RNA was monitored in real-time for 200 s; the hydrated biosensor tip was placed in a 1.5 mL black assay tube containing 200 µL of the protein solution in an assay buffer as indicated. Afterward, a dissociation step or a second association with Tat was performed for 200 s. Biosensors were discarded after each measurement. All kinetic assays shown in For the Bio-Layer Interferometry (BLI) analysis, we used the BLItz platform (FortéBio). For the high throughput experiments such as shown in E. coli or in mammalian cells), binds TAR RNA  as an RNA binding protein in the very first stages of proviral transcription. In particular, we have shown that full length IN, despite its intrinsic instability and independently of the post-transcriptional modifications , int


Human papillomaviruses (HPV) are a group of small non-enveloped DNA viruses whose infection causes benign tumors or cancers. HPV16 and HPV18, the two most common high-risk HPVs, are responsible for ~70% of all HPV-related cervical cancers and head and neck cancers. The expression of the HPV genome is highly dependent on cell differentiation and is strictly regulated at the transcriptional and post-transcriptional levels. Both HPV early and late transcripts differentially expressed in the infected cells are intron-containing bicistronic or polycistronic RNAs bearing more than one open reading frame (ORF), because of usage of alternative viral promoters and two alternative viral RNA polyadenylation signals. Papillomaviruses proficiently engage alternative RNA splicing to express individual ORFs from the bicistronic or polycistronic RNA transcripts. In this review, we discuss the genome structures and the updated transcription maps of HPV16 and HPV18, and the latest research advances in understanding RNA cis-elements, intron branch point sequences, and RNA-binding proteins in the regulation of viral RNA processing. Moreover, we briefly discuss the epigenetic modifications, including DNA methylation and possible APOBEC-mediated genome editing in HPV infections and carcinogenesis. Human papillomaviruses (HPV) are a group of small, non-enveloped DNA viruses, 52–55 nm in diameter. To date, the group consists of more than 450 genotypes (types) ,2,3, in HPV infection is cell-type-specific. HPV infects keratinocytes on the basal lamina through micro-wounding induced by scratching or sexual intercourse. Initial attachment of HPV virions to the host keratinocytes takes place by the interaction of viral capsid proteins with host cell receptors . The virE) and viral late (pAL), into three distinct regions: an upstream regulatory region (URR), an early (E), and a late (L) gene regions  signals, viral early  in papillomaviral RNA transcripts, only the first ORF located at the transcript’s 5′ end is efficiently translatable , is an old, outdated version from the Human Papillomaviruses Compendium-1996 composed by Carl Baker and Charles Calef . This old version was then updated in 2006 [As a leading causative agent for cervical cancer, HPV16 genome transcription and viral oncogene E6 and E7 expression were the major focus of HPV studies in the past ~40 years. The HPV16 transcription map was initially generated by multiple laboratories based on viral RNAs extracted from cervical cancer tissues, HPV DNA-transfected cells, and HPV-infected tumor cell lines containing both extrachromosomal and integrated HPV16 genomes ,42,43. T in 2006  to incluIn general, HPV16 genome expression is driven mainly by two major promoters. The early promoter P97 upstream of the E6 ORF contains a TATA box (a eukaryotic core promoter motif) 35-nt upstream of its transcription start site and is used for transcription of nearly all viral early gene transcripts, while the late promoter P670 within the E7 ORF is a TATA-less promoter responsible for the expression of HPV late genes. The HPV16 E2 protein regulates the promoter P97 activity through binding to the cis-elements in the URR region ,46,47. HE at nt 4215 is mainly used for polyadenylation of viral early RNA transcripts, and the pAL at nt 7321 is responsible for the polyadenylation of viral late RNA transcripts. However, the late RNA transcript E1^E4 uses the pAE for its polyadenylation. The cleavage sites for RNA polyadenylation of HPV16 early transcripts have been mapped mainly to nt 4232 [The HPV16 genome contains two canonical pA signals (AAUAAA) to guide host RNA polyadenylation machinery to cleave the viral RNA transcripts for the addition of a poly A tail and termination of viral RNA transcription. The pA nt 4232  and of l nt 4232 .E signal and the intron 2 splicing.HPV16 uses all three coding frames to translate viral proteins. Viral E6, E2, E8^E2, and L1 are expressed from the frame 2 and viral E7, E1, E5, and L2 from the frame 1. Interestingly, the first 5 aa of E1^E4 are translated from the frame 1 and are identical to the first 5 aa of E1 protein, but the rest of the E1^E4 protein is translated from the late RNA exon 2 in the frame 3 generated by viral RNA splicing . The traThe most recent updated HPV18 transcription map  is basedE at nt 4235 with a predominant cleavage site at nt 4270 [HPV18 early transcripts originated from P55 or P102 are polyadenylated at the pA nt 4270 . Similar nt 4270 ,57 and iE signal at nt 4235 for production of viral late protein E1^E4  at nt 385 to serve as a branch site [AC, where one heptamer has a branch site adenosine (underlined) at nt 384 and the other at nt 388 [In addition to its suboptimal nature of splice sites, HPV16 E6E7 intron 1 has a BPS heptamer AACAAnch site  during tt nt 388 , for E6*t nt 388  and latet nt 388 . It is pt nt 388  or the it nt 388 , thus alt nt 388 ,87  by interaction with various RNA splicing factors ,80,81,90E for the viral E1^E4 expression, thus decreasing HPV16 L1 expression [Although the AC-rich ESE binding SRSF3 and AG-rich ESE interacting with SRSF1, hnRNP G, and Tra2B are important for the usage of the 3358 3′ ss splicing of the HPV16 early transcripts to express viral E6 and E7 ,91,93, tpression . Inhibitpression  . As shown in The 3′ end processing of pre-mRNAs transcribed by RNA polymerase II is essential for RNA stability, export, and translation. RNA polyadenylation initiated by cleavage of the nascent transcript is coupled with the addition of non-templated 150–200 adenylate residues to an RNA transcript to form a poly-A tail. Four core cis-elements in the nascent transcripts, including a UGAU cis-element for a downstream pA signal (PAS) definition, a highly conserved AAUAAA hexamer representing PAS, a cleavage site generally positioned 10–30 nt downstream of PAS, and a G/U- or U-rich element  that is ~10–30 nt further downstream of the cleavage site, are recognized by cellular polyadenylation machinery to initiate the process of RNA cleavage and polyadenylation ,100. NumE and pAL, for their gene expression. As shown in E at nt 4215 for cleavage and polyadenylation at nt 4232 for all viral early and late E1^E4 RNA transcripts and a pAL at nt 7321 for cleavage and polyadenylation at nt 7345 for viral late L1 and L2 RNAs. In parallel, HPV18 utilizes a pAE at nt 4235 for cleavage and polyadenylation at the nt 4270 cleavage site and a pAL at nt 7278 for cleavage and polyadenylation at the nt 7299/7307 site. Recognition of individual viral PAS sites is intimately coupled to viral promoter activation and alternative RNA splicing and thus linked to keratinocyte differentiation, with the pAE being recognized in both undifferentiated and differentiated keratinocytes while the pAL is recognized only in highly differentiated cells. Although not yet fully understood, additional RNA cis-elements upstream and downstream of the pAE are involved in the regulation of viral pAE recognition in HPV16 and HPV31 [E [E [E activity  and RBPs. The cis-elements controlling the activity of the viral late pAL were initially discovered in BPV-1 late transcripts. These studies revealed a 53-nt BPV-1 inhibitory cis-element at the late 3′ UTR, which contains a consensus cryptic 5′ ss and inhibits late RNA polyadenylation at the late pAL. Through binding U1 snRNAs, this cryptic 5′ ss-containing cis-element inhibits late RNA polyadenylation by a direct interaction of U1-70K with poly(A) polymerase [Viral late pAlymerase ,112. Thelymerase   downstream of the late cleavage sites [Analyses of the 80-nt sequences 5′ to the HPV18 pAs region . Similarge sites . Collectge sites ,118,119.The human genome encodes ~1500 RBPs ,123,124.One group of the common RBPs consists of abundant multifunctional hnRNPs ,126. MulHost RNA splicing factor SR proteins, including SRSF1(ASF/SF2), SRSF2 (SC35), SRSF3 (SRp20), and SRSF10 (SRp38/SRrp40), are also upregulated in HPV-positive cervical precancer lesions and cancer tissues ,138,139 To investigate which viral protein(s) in HPV16 and HPV18 infections causes the increased expression of host RBPs, HPV16 E2, which binds DNA and regulates viral and host gene transcription, has been demonstrated to enhance the expression of SRSF3 (SRp20), SRSF1 (ASF/SF2), SRSF2 (SC35) and SRSF4 (SRp75) and SR proteins kinase 1 (SRPK1) ,145,146.In addition to their DNA-binding activities, purified HPV16 E2 and E6 are two viral RNA-binding proteins which suppress in vitro RNA splicing via the E2 N-terminal half and hinge regions and the E6 N-terminal and central portions, respectively . Both E2E site, which is reversible by high levels of HPV16 E1 protein or by the polyadenylation factor CPSF30. Both the N-terminal and hinge regions of HPV16 E2 contribute to this inhibition and cause a read-through at the pAE into the late region, leading to induction of the expression of L1 and L2 mRNAs with increased L1 RNA 3632^5639 splicing [HPV16 E2 inhibits polyadenylation in vitro by preventing the assembly of the CPSF complex at the pAsplicing .DNA methylation, catalyzed by the enzymatic activity of DNA methyltransferases (DNMTs), occurs predominantly on cytosines preceding guanine nucleotides (CpG dinucleotides) and maintains CpG methylation patterns in the cells following DNA replication . MethylaAll papillomavirus genomes contain hundreds of CpG dinucleotides, as shown in As well as DNA methylation on both host and viral genomes, host RNA-editing by deamination enzymes also plays an important role in genome modifications by introducing point mutations into RNA or single-stranded DNA (ssDNA). Adenosine-to-inosine (A-to-I) editing on double-stranded RNA (dsRNA) is catalyzed by dsRNA-specific adenosine deaminase ADAR1 or ADAR2 ,180 of wThe increased expression of A3s in HR-HPV cancers has been considered as a source of oncogenic drivers to introduce point mutations in the host genome ,198,199 As stated above, A3s utilize ssRNA and sometimes ssDNA as a substrate for genome editing. The papillomavirus genome is a double-stranded DNA (dsDNA) and should not normally be editable by A3s ,211. It https://www.who.int/reproductivehealth/call-to-action-elimination-cervical-cancer/en/, accessed on 1 March 2022). For HPV16, these advances include the characterization of a promoter P14 possible for E1 transcription [It has been sixteen years since our past review  on papilcription  and a pocription . Additiocription . The advcription , subsequcription ,60,216, cription . The discription  opens upcription  led to tcription . Conceivcription .The link between alternative RNA splicing in cancer development and progression has been one of the focal areas of research in the papillomavirus field since the 1990s. Alternative RNA splicing of viral polycistronic pre-mRNAs is essential for the expression of individual viral proteins from properly spliced RNA isoforms. Multiple RBPs, especially SR proteins and hnRNPs, have been identified as being involved in the regulation of HPV gene expression by binding to cis-elements in HPV pre-mRNAs. Among the host splicing factors facilitating the regulation of HPV16 and HPV18 RNA splicing, SRSF3 (SRp20) is perhaps the most remarkable host splicing factor, which not only regulates the early-to-late switch of HPV16 and HPV18 productive infection, but also prevents keratinocyte differentiation ,79. HighE site on the intron 2 retained late transcripts. Whether viral E2 inhibits recognition of the pAE site on the late transcripts by host polyadenylation machinery and promotes the read-through into the late region during HPV late infection [While recognizing our past achievements, we remain puzzled by our lack of knowledge of how viral E1, E2, E5, and L2 are produced in the context of the HPV16 or HPV18 genomes, although their functions have been studied extensively by artificial overexpression of each protein from a mammalian expression vector with a foreign promoter. In the context of the HPV16 or HPV18 genomes during HPV infection, each of these genes is expressed along with other genes from a shared early or late viral promoter. Thus, the corresponding ORF of each gene is in the middle or last part of a polycistronic RNA. Considering that RNA 5′ cap-mediated ribosome scanning is from the RNA 5′ to 3′ direction and HPV RNA transcripts lack a ribosomal internal entry site, RNA splicing of the polycistronic RNAs removing or disrupting the upstream ORF(s) has been a plausible mechanism for the expression of E1, E2, E5, and L2 . Howevernfection  remains Protein translation is the last step in gene expression, involving 5′ cap-mediated ribosome binding to a translation initiation codon AUG of the ORF. The canonical ribosome footprint is ~20–30 nt long from RNA ,224,225.Non-coding RNAs  have revolutionized our understanding of the complexity of the transcriptome involving every part of modern biology ,227,228.


Even though metastasis, a hallmark of cancer, is responsible for up to 90% of cancer-related mortality, it is still the least understood aspect of cancer pathogenesis . By commA central question, yet unaddressed, is the underlying intrinsic characteristics that allow disseminated tumor cells to eventually form overtly metastatic disease. The discovery of metastatic gene signatures, for instance, has undoubtedly added to our understanding of the pathophysiology of metastasis . HoweverAnother crucial question is how current treatment affects all the genes and pathways involved in metastasis. Many current patients’ treatment modality is ineffective due to fatal metastasis, despite significant advances and new discoveries in tumor metastasis biology. Importantly, the advancement of new technologies and the development of better tumor models have aided in the discovery of novel gene signatures that predict metastatic spread to specific body sites. Future genetic signature-based clinical studies should be designed with the goal of classifying cancer patients who are likely to develop distant metastases at the earliest possible stage. This will determine which patients will benefit from therapeutic targeting of metastatic-related genes and pathways while avoiding unnecessary treatment, resulting in more cost-effective metastatic treatment that will reduce morbidity and mortality associated with this systemic disease.Interestingly, earlier research on metastasis has shown cancer stem cells (CSCs) to be a driving force in promoting tumor growth, immune evasion, co-selection of metastatic microenvironments and recurrence in distant organs through their unique biological properties . HoweverA growing body of evidence also indicates potential role of CSC-derived extracellular vesicles in mediating metastasis, stemness, and remodeling of the tumor immune microenvironment . UnderstAnother intriguing aspect of metastasis research that is gaining momentum is the metabolic regulatory crosstalk between CSC and TME in the maintenance of metastasis at distant organs ,10. HoweThe articles in this Special Issue are intended to serve as a benchmark for potential mechanistic insights and perspectives on the metastatic process, as well as a reflection on future avenues for developing a framework for potential clinical and translational strategies for cancer metastasis therapy research. We sincerely hope that the articles in this Special Issue will be of interest to the cancers audience, which has dedicated itself to eradicating a threat that is, indeed, awaiting its abolition.


Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction for those structured RNAs, which is as fundamentally and practically important similar to protein structure prediction. One major factor in the recent advancement of protein structure prediction is the highly accurate prediction of distance-based contact maps of proteins.Neff > 50). The initial use of the predicted contact map as distance-based restraints confirmed its usefulness in 3D structure prediction.Here, we showed that by integrated deep learning with physics-inferred secondary structures, co-evolutionary information and multiple sequence-alignment sampling, we can achieve RNA contact-map prediction at a level of accuracy similar to that in protein contact-map prediction. More importantly, highly accurate prediction for top L long-range contacts can be assured for those RNAs with a high effective number of homologous sequences (https://sparks-lab.org/server/spot-rna-2d/ and as a standalone program at https://github.com/jaswindersingh2/SPOT-RNA-2D.SPOT-RNA-2D is available as a web server at Bioinformatics online. Although these structures can be obtained by X-ray crystallography, nuclear magnetic resonance (NMR) or cryogenic electron microscopy, the low throughput and costly nature of these experimental techniques and the challenges associated with RNA structure determinations has led to fewer than 3% deposited structures in protein data bank  with knoRecently, computational prediction of RNA 3D structures has been improved by restraining distance-based contacts from direct coupling analysis analysis (DCA)  X-ray structures from PDB on 3 October 2020. These RNA structures were split into individual chains using a As the 80% sequence identity cut-off may not be strict enough, therefore, we further used the BLAST-N  and 10 for TS2 to make this test set as strict as possible for benchmarking. Tertiary structure predictions were evaluated on the TS2 set as it represents the most challenging set of targets for SPOT-RNA-2D.Furthermore, we made VL and TS2 non-redundant even at the remote-homolog level from the other datasets  and within themselves . To achieve non-redundancy at the remote-homolog level, we build a covariance model for RNAs in VL and TS2. The covariance was built by first searching the query RNA  against NCBI’s database for homologs using BLAST-N. Then the multiple sequence alignment (MSA) of homologs along with consensus secondary structure (CSS) from 3D structural files was used to build the covariance model using the INFERNAL  tool. FiThe final X-ray dataset consists of 286, 30, 63 and 30 RNA chains for TR, VL, TS1 and TS2, respectively. The distribution of the datasets, such as the number of RNA chains in each set, median and maximum sequence lengths, type of base-pairs, the average number of distance-based contacts, is shown in https://rfam.xfam.org/ website), to analyze the distribution and overlap of datasets in terms of Rfam families. As shown in These X-ray datasets were mapped to Rfam families , we prepared an additional test set (TS3) using NMR structures. To prepare TS3, we downloaded all the NMR structures (707) from the PDB (Neff) for all the RNAs were obtained using GREMLIN tool, where Neff is defined as the sum of weights after down-weighting each sequence by the number of neighbours above a pairwise sequence similarity cutoff of 0.8.To obtain the distance-based contact labels, we defined two nucleotides in contact if the distance between the nearest-heavy atoms of these two nucleotides is <8 Å as done previously (The input features for this work follow our previous base-pair predictor [SPOT-RNA2 (L × L from the single-sequence-based method RNAfold (Single-sequence-based features include one-hot encoding of an input RNA sequence of size L × L. PSSM and DCA features were extracted from RNAcmap (Evolutionary-profile-based features include position-specific scoring matrix (PSSM) of size All 1D features . A single ResNet block consists of two pre-activated convolutional layers with a kernel size of 3 × 3 and NF filters. The input to convolutional layers was also normalized by using layer normalization  output layer with a single node and a sigmoid activation function was used. It predicts the upper triangular base-pair probability matrix of size L × L (as shown in A) and filters (NF). NA was varied from 1 to 20 and NF from 32 to 96 with a step increase of 1 and 8, respectively. Based on the performance of the validation set, we choose the four best models for the ensemble. Model architecture parameters for these four models are shown in We trained many models based on the architecture shown in All the models were implemented using Google’s TensorFlow framework (Version-1.15) and trained using Nvidia GTX TITAN X graphics processing unit (GPU). For training, an Adam optimizer  with a lrna_denovo application of the Rosetta molecular modelling suite. Inputs were generated from the FARFAR2 (https://github.com/DasLab/rna_benchmark). In all cases, secondary structure assignments [from RNAfold  and SPOT-RNA-2D-Single (0.50). For other methods, we used a threshold value that optimizes the F1 score on the test set TS1.Similar to RNA secondary structure prediction, distance-based contact-map prediction is a binary classification problem. Thus, we used precision [i and j are the of sequence positions of any two nucleotides in a sequence. This definition of long-range contacts [In addition, for an RNA of length L, we used a mean precision of top https://github.com/debbiemarkslab/plmc), GREMLIN (https://github.com/sokrypton/GREMLIN_CPP), mfDCA and plmDCA [from pydca  to obtain predictions.We compared SPOT-RNA-2D with PLMC (https://www.tbi.univie.ac.at/RNA/), SPOT-RNA (https://github.com/jaswindersingh2/SPOT-RNA), SPOT-RNA2 (https://github.com/jaswindersingh2/SPOT-RNA2) and LinearPartition (https://github.com/LinearFold/LinearPartition) and run these predictors locally with default parameters.For comparison with RNA secondary structure prediction methods, we downloaded RNAfold  improves the F1-score by more than 6%, 4%, 13% and 13% for VL, TS1, TS2 and TS-rfam, respectively. For the evolution-based information, adding 1D PSSM and 2D DCA from PLMC  in TS1 than in VL (median Neff=2) and TS2 (median Neff=9).To further extract the evolution information, we performed MSA sampling during training that was found effective for protein structure prediction by Alphafold . We chosFinally, we used ensemble learning by performing the average ensemble of the outputs of the best four models from many models trained using the generalized architecture shown in Two methods SPOT-RNA-2D and SPOT-RNA-2D-Single were developed with and without evolution information, respectively. They were compared to four existing direct coupling analysis (DCA) predictors  on two high-resolution (<3.5 Å) non-redundant test sets (TS1 and TS2) derived from PDB X-ray structures and one non-redundant test set (TS3) derived from PDB NMR structures. Another test set TS-rfam prepared from TS1 and TS2 by extracting 69 RNAs without any Rfam family overlap with training set (TR). Test sets TS1 and TS2 contain 12 RNA-Puzzles RNAs with results also shown separately. To further compare with the recently developed deep learning-based RNAContact  for all the predictors. This is mainly due to the relatively low The large improvement of SPOT-RNA-2D over DCA methods can be further illustrated by the precision–recall curve  on combiAnother way to measure the method performance in contact prediction is the fraction of true positions in top L, L/2, L/5 and L/10 predictions (Precision). The most important contacts are those contacts with large differences in sequence positions . Neff-value. The mean precisions for SPOT-RNA-2D and DCA predictors  2’-dG-II riboswitch (boswitch  (Chain AVarkud satellite ribozyme (Neff=1) by RNAContact, SPOT-RNA-2D-Single and SPOT-RNA-2D, respectively. For this RNA, SPOT-RNA-2D-Single is the most accurate within top L long-range contacts with a precision of 0.83, followed by SPOT-RNA-2D (0.82) and RNAContact (0.42), respectively. This example illustrates SPOT-RNA-2D can essentially recover SPOT-RNA-2D-Single for low Neff RNAs. RNAContact misses most structural features, whereas SPOT-RNA-2D captures most contact features.Neff (Neff=3) RNA hatchet ribozyme .One nice feature of SPOT-RNA-2D is that it can recover the performance of the single-sequence-based method SPOT-RNA-2D-Single when few homologous sequences are available. This is supported by the dependence of the performance of SPOT-RNA-2D and SPOT-RNA-2D-Single on the number of effective homologous sequences . Thus, SNeff RNAs.One limitation of SPOT-RNA-2D is due to its usage of RNAcmap to derive evolution information. RNAcmap uses BLAST-N to make the first round of sequence-based search for homologs, followed by INFERNAL to perform the second round of sequence-profile and secondary-structure-based search. This method is computationally prohibitive for RNA sequences >1000 bases. As a result, our model was trained with a maximum sequence length of 418. For example, RNAcamp can take 5–6 h on 40 CPU threads for 418 bases RNA sequence, which will be the same for all the alignment-based predictors. After that, SPOT-RNA-2D only takes 92.68 s for making the prediction of all RNAs in the test set TS1. For time-sensitive calculations, SPOT-RNA-2D-Single can be used, which takes only 43.76 s for full test set TS1 prediction from single-sequence and performs better than the RNAContact and DCA predictors, and comparable to SPOT-RNA-2D on low One disappointing result is that even with perfect secondary-structure base-pair assignment and tertiary contact accuracy, the average RMSD is still 8.1 Å for targets in TS2 (6.5 Å after excluding complex structures). This highlights a potential limitation of the current modelling framework. One likely reason is the lack of backbone restraints, which has been proven important as secondary structure restraints in protein structure prediction. Our recent result shows that backbone angles can be predicted with reasonable accuracy (To place the accuracy for RNA contact prediction in context, we compare it to the accuracy of protein contact prediction. The average number of RNA contacts is about 3 L , comparebtac421_Supplementary_DataClick here for additional data file.


Precise control of gene expression is central for every organism. Much research has been done to uncover the network of regulatory pathways controlling the RNA life cycle and how RNA levels change rapidly to modulate the amount of proteins made by the cells. In principle, every point in the life cycle of an mRNA from its transcription, modification, splicing, processing, trafficking, translation, and decay is monitored by one or more RNA surveillance mechanisms. Together, these regulatory mechanisms maintain normal cellular activity and provide timely responses to adapt to changing environments such as during starvation, differentiation, or infections. This special section presents a collection of recent work in prokaryotic and eukaryotic translational control and the role of RNA elements and modifications in regulation of translation.Wang and Amoyel describe recent advances that demonstrate the outsized role of mRNA translational regulation in stem cell proliferation and differentiation. They survey how bulk and mRNA selective translation events dictate stem cell fate and describe translation regulatory mechanisms that affect stem cell specification across many types of stem cells. This is critical as most multicellular life originates from a single totipotent cell that divides and differentiates, giving rise to an entire organism. Next, Jungers and Djuranovic review the literature describing how RBPs and miRNAs act synergistically or antagonistically to regulate mRNAs via 3′UTR sequences. We’ve known that interactions between trans-acting regulatory factors on individual mRNAs create an elaborate landscape of effects that regulate genes post-transcriptionally. They close by highlighting the implications of these regulatory mechanisms on human disease, particularly cancers. Lastly, many viral, bacterial, and some eukaryotic RNAs contain sequence elements that shift ribosomes to alternative reading frames during translation. Riegger and Caliskan discuss the current knowledge regarding programmed ribosomal frameshifting. They summarize the role(s) of RNA secondary structures and highlight how trans-acting RNA modulators can dynamically adjust the timing and efficiency of frameshifting events. Ribosome frameshifting provides new targets for disrupting viral translation strategies and presents opportunities to impact infections and immune responses.The contributions to this special section fell into three distinct groups. The first of these groups include three reviews examining the contributions of translational control to stem cells, a second review that helps us better understand the translational impact of RNA binding proteins (RBPs) and microRNAs, and a third review examining ribosome frameshifting. In the first paper, Belinite et al. successfully purified stable 30S small ribosome subunits from S. aureus and solved their structures complexed with RNA by cryo-electron microscopy in the presence and absence of spermidine. Remarkably, spermidine stabilizes helix 44 to form the active conformation. This study provides important insights into how ribosome structure could be regulated during translation. Next, we’ve known that certain archaeal transcripts, including tRNA, rRNA, and sRNA, undergo post-transcriptional maturation, often involving a circular RNA intermediate. However, the mechanism underpinning selective circularization occurs was unknown. Liu et al. demonstrated that ATP-dependent RNA ligase (Rnl) generates circular C/D Box sRNAs and validated the structural features required for circularization. Interestingly, their work suggests that, either directly or indirectly via C/D Box sRNA circularization, Rnl also contributes to rRNA processing near the helix 98 region of 23S rRNA. Finally, the anti-Shine-Dalgarno (anti-SD) sequence is a highly conserved rRNA sequence that hybridizes with the Shine-Dalgarno (SD) sequence upstream of the start codon to promote translation initiation. However, species in the phylum Bacteroidota have very few mRNAs with consensus SD sequences. McNutt et al. used comprehensive mutation analysis of anti-SD sequence in E.coli and F. johnsoniae to gain new insights into the mechanism of SD-independent translation. Together, these three works leverage studies investigating ribosome structure, small RNA processing, and the impact interactions of ribosomal and mRNA sequences to expand our knowledge of prokaryotic translational regulation.In the second group of papers, three groups use prokaryotic systems to ask fundamental questions about translational control. High-resolution structures of active ribosomes are critical for understanding translational regulation. Douka et al. describe Ribo-seq approaches tailored for human and Drosophila melanogaster cell lines. They illustrate that even subtle changes in experimental conditions can alter overall quality of ribosome footprinting. Furthermore, the authors show the varying impact of antibiotic pretreatment on different cell types. Overall, this study highlights key attributes that require careful optimization in each new system to obtain high-quality ribosome profiling data. Second, using budding yeast mutants and ribosome profiling Stanciu et al. show that eIF3’s (eukaryotic initiation factor 3) contributions were often mediated by sequences in 5′ untranslated regions. eIF3 is a key initiation factor that functions throughout translation initiation including during mRNA recruitment and has been linked to the selective translation of specific classes of mRNAs. Third, IF4B binds and recruits mRNA to preinitiation complexes. Liu et al. disrupted yeast eukaryotic initiation factor 4B (eIF4B) and 40S binding motifs to evaluate how these factors impact adaptation and survival in diverse cellular environments. The authors show that N-terminal domain deletions of eIF4B leads to decreased translation rates, especially on mRNAs with long and structured 5′ UTRs. This work suggests that the NTD of eIF4B affects the translation of a subset of mRNAs, thereby impacting cellular stress responses. Finally, RNA modifications such as N6 methyl-adenosine (m6A) influence post-transcriptional gene regulation. IGF2BP2 (insulin-like growth factor 2 mRNA-binding protein 2) is an RNA binding protein and m6A reader known to regulate the localization, translation, and stability of mRNAs. In their work, Han et al. demonstrate that IGF2BP2 regulates the translation of ATG12 (autophagy-related 12) mRNA indirectly through the MALAT1 lncRNA.The final group of research articles detail new findings that describe the contributions of RNA modifications and sequences to translational control. Overall, these articles highlight the impact of RNA-based regulatory events during translation. These articles also shed light on the interplay of protein factors and mRNA elements that fine tune the dynamics of translation. These aspects will be relevant to understanding the complexity of the gene expression networks and their impact on cellular response in cell proliferation, cell fate and immune regulation.


Highly mutated enzymes were more tolerant to most of the inhibitors, but more sensitive to high concentrations of NaCl. The results presented could help select the optimal enzyme for novel LAMP-based diagnostic tests.Reverse transcriptases (RTs) are a family of enzymes synthesizing DNA with RNA as a template, and are used in almost all studies related to RNA. M-MuLV RT is widely used in diagnostics methods, including in reverse-transcription loop-mediated isothermal amplification (RT-LAMP). The performance of various reverse transcriptases in RT-LAMP remains poorly studied. Here, we report the first direct comparison of various M-MuLV RTs in RT-LAMP. The enzymes studied contained different numbers of mutations or an additional Sto7d protein. Several parameters were assessed: optimal reaction temperature, enzyme concentration, reverse transcription time, a minimal amount of RNA template, and tolerance to inhibitors. Mutations increased the optimal temperature of the reverse transcription up to 5–10 °C. All of the RTs were suitable for RT-LAMP with RNA templates in the range of 101–106 copies per reaction. Highly mutated enzymes were 1.5–3-fold more tolerant to whole blood, blood plasma, and guanidinium, but they were two-fold more sensitive to high concentrations of NaCl. The comparison of different RTs presented here could be helpful for selecting the optimal enzyme when developing novel LAMP-based diagnostic tests.Reverse transcriptases (RTs) are a family of enzymes synthesizing DNA using RNA as a template and serving as indispensable tools in studies related to RNA. M-MuLV RT and its analogs are the most commonly used RTs. RTs are widely applied in various diagnostics methods, including reverse-transcription loop-mediated isothermal amplification (RT-LAMP). However, the performance of different RTs in LAMP remains relatively unknown. Here, we report on the first direct comparison of various M-MuLV RTs in RT-LAMP, including enzymes with a different number of mutations and fusions with Sto7d. Several parameters were assessed, namely: optimal reaction temperature, enzyme concentration, reverse transcription time, a minimal amount of RNA template, and tolerance to inhibitors. Mutations increased the optimal reaction temperature from 55 °C to 60–65 °C. All of the RTs were suitable for RT-LAMP with RNA templates in the range of 10 Reverse transcriptases (RTs) are a family of enzymes utilizing RNA as a template to synthesize DNA and participating in various processes such as retrovirus propagation and telomere end elongation ,2. To daMycobacterium tuberculosis, and SARS-CoV-2 -dTTP (4 Bq/pmol), 50 mM Tris-HCI (pH 8.3), 6 mM MgCl3 copies per 20 µL), 1x ddPCR master-mix , 300 nM oligonucleotide primers and probes: E_Sarb_F/R/P for SARS-CoV-2 [The concentrations of DNA and RNA in the obtained standards were refined by a digital PCR using the QX200™ Droplet Digital™ PCR System  according to the manufacturer’s instructions. The reactions were carried out in a total volume of 20 µL containing the DNA under examination 2SO4, 50 mM KCl, 0.1% Tween-20, 8 mM MgSO4), 1.25 mM each dNTP, 0.4 µM each external primer (F3/B3), 0.8 µM loop primers (LF/BF), 1.6 µM internal primers (FIP/BIP) (Geobacillus sp. 777 [The reaction mixture for LAMP (20 µL) contained 1× reaction buffer for Bst-polymerase  , DNA or  sp. 777 , and 1 µSulfolobus tokodaii. The features of the enzymes are listed in In the present work, the enzymes to be studied were selected according to their popularity and improvements by various approaches, including different mutations and fusion with DNA-binding proteins. We compared in RT-LAMP several M-MuLV RT variants: two of the most popular commercial enzymes, Superscript III and Superscript IV; RNAse H- M-MuLV RT ; RT mut , fusions of RNAse H-M-MuLV RT and RT mut with DNA-binding protein Sto7d from To evaluate the performance of various RTs, we used a previously designed LAMP assay for SARS-CoV-2 RNA detection . Briefly3 copies per reaction. Each experiment was conducted in three independent replicates, with each replicate including a no-template control and a no-reverse transcriptase control. The results of the enzyme titration are presented in Initially, we titrated RTs in RT-LAMP to assess the range of RT concentrations that would provide a sufficient RT-LAMP efficacy. This parameter could be crucial as excess enzymes might lead to a spurious non-specific amplification  and enzyme shortage might cause a slow and inefficient reaction . All of the enzymes were titrated at a range of 0.4–20 U/reaction. The template concentration for RT-LAMP, SARS-CoV-2 RNA fragment, and MS2 genomic RNA was 10All of the RTs performed the reverse transcription step of RT-LAMP when added in the reaction in a similar range of 0.4–20 U/reaction. However, the efficacy of RTs was different. Thus, the reaction rate of the native M-MuLV RT was lower than that of the other enzymes in both model RT-LAMP assays, as manifested by the higher Tt values. The efficacy of RT-LAMP with Superscript III and Superscript IV was the same in the whole range of 0.4–20 U/reaction. Non-specific amplification products appeared in RT-LAMP for MS2 and SARS-CoV-2 with homemade enzymes at a concentration of more than 20 U per reaction, with the single exception being RT . In otheThus, commercial enzymes were suitable for RT-LAMP in a range of 0.4–20 U/reaction, while the homemade enzymes were 0.4–20 U/reaction. In the latter experiments, the final concentration of all of the enzymes was 10 U/reaction.3 copies per reaction; the final concentration of all of the enzymes was 10 U/reaction. Each experiment was conducted in three independent replicates, with each replicate including a no-template control and a no-reverse transcriptase control. The results are presented in In the next step of comparison, we defined the optimal temperature for the reverse transcription step in the RT-LAMP for each enzyme. Various RTs have different temperature optimums, depending on the exact alteration composition. A suboptimal reaction temperature could result in a lower reaction speed as the enzyme might lose optimal conformation or be thermally denatured. The template secondary structure at a low reaction temperature could stall the enzyme progression. The temperature of reverse transcription in RT-LAMP varied in the range of 37–65 °C. The template concentration for RT-LAMP, SARS-CoV-2 RNA fragment, and MS2 genomic RNA was 10annealing at a range of 60–62 °C. The optimal temperature was 60 °C for RT mut and RT-Sto mut, and 55 °C for RT and RT-Sto. As shown before, the optimal temperature for chimeric enzymes was similar to the cognate RTs, despite the presence of Sto7d. Thus, commercial enzymes enriched with mutations demonstrated a higher thermostability than the chimeric enzymes or enzymes with L139P, D200N, and T330P mutations. In further experiments, the reverse transcription temperature was 55 °C for RT and RT-Sto and 60 °C for all of the other enzymes.Commercial enzymes, Superscript III and Superscript IV, were the most active at the temperature range of 60–65 °C. These enzymes could work at a higher temperature. The annealing temperature of the LAMP primers used was a limiting factor preventing testing at higher temperatures because all of the LAMP primers were designed to have T3 copies per reaction. Each experiment was conducted in three independent replicates, with each replicate including a no-template control and a no-reverse transcriptase control. The results of the experiments are presented in The optimal time for reverse transcription could be crucial for diagnostic tests because they often require a high throughput capacity for mass-scale testing. Thus, machine time could be a limiting factor, particularly in pandemic situations or testing in crowded places. Normally, an excessive number of RTs are used to catalyze reverse transcription in RT-PCR or RT-LAMP. However, an excess of enzymes can lead to false-positive test results. In the samples with a low abundance of target RNA, an insufficient reverse transcription time could result in false-negative test results. The fast enzyme could speed up the analysis and increase the throughput capacity of the diagnostic laboratories. To evaluate the optimal time of the reverse transcription, we changed this parameter from 1 to 20 min. The template concentration for RT-LAMP, SARS-CoV-2 RNA fragment, and MS2 genomic RNA was 10As expected, increasing the reverse transcription time led to a lower Tt and faster LAMP results. Thus, prolonged reverse transcription decreased the Tt values for 5–10 min, depending on the enzyme. Further prolongation of the reverse transcription reduced Tt values to less than 1 min. However, in that case, the overall time of RT-LAMP remained the same. Tt values of less than 1 min were caused by Gss-polymerase activity during the reverse transcription step. Thus, the LAMP amplicons synthesized during the reverse transcription step created a detectable fluorescent signal at the beginning of the LAMP step. RT-Sto and RT-Sto mut demonstrated a higher rate of reverse transcription with RT-LAMP for MS2 but not with RT-LAMP for SARS-CoV-2. An additional DNA-binding domain facilitatedthe enzyme performance on the template with a complex secondary structure. In most cases, RT-LAMP with RT showed the highest Tt, indicating the lowest reaction speed with this enzyme. For further experiments, we chose 10 min for reverse transcription as a reasonable compromise for reducing the overall amplification time, including both reverse transcription and LAMP steps.6–101 copies per reaction. Each experiment was conducted in three independent replicates, with each replicate having a no-template control and a no-reverse transcriptase control. The results of titration are presented in Analytical sensitivity, defined as the lowest detectable analyte concentration, is one of the crucial parameters for diagnostics tests. Insufficient sensitivity could result in false-negative results. Thus, in the case of viral testing, patients would not receive the appropriate therapy and would continue to spread the virus. To evaluate the ability of RTs to use various amounts of RNA as templates, we titrated the SARS-CoV-2 RNA fragment and MS2 genomic RNA in the range of 10Most of the RTs demonstrated a similar efficacy for various concentrations of a template. Six orders of magnitude of RNA concentration generated a positive amplification signal. Notably, the efficiency of RT-LAMP with SARS-CoV-2 was higher than with MS2. The latter could be caused by the complex secondary structure of the MS2 genomic RNA, as mentioned above for the experiment with the time for the reverse transcription. Among all of the enzymes, only RT performed RT-LAMP slower, similarly to previous experiments.3 copies per reaction. Each experiment was conducted in three independent replicates, with each replicate including a no-template control and a no reverse transcriptase control. The calculated IC 50 values for each inhibitor are presented in Various substances originating from samples or RNA purification procedures could inhibit the catalysis of RTs. Human whole blood and blood plasma, urea, humic acids, ethanol, NaCl, and guanidinium are among these inhibitors. In most cases, laboratory purification protocols allow DNA/RNA samples to be obtained free from traces of inhibitors. However, such methods could be unavailable in the case of point-of-care testing, which is the domain of LAMP, and other approaches for isothermal amplification. To assess the ability of different RTs to perform RT-LAMP in the presence of inhibitors, we titrated several inhibiting substances in RT-LAMP reactions: human whole blood, blood plasma, urea, ethanol, NaCl, salts of guanidinium, formamide, and NaCl. The template concentration for RT-LAMP, SARS-CoV-2 RNA fragment, and MS2 genomic RNA was 10As anticipated, the presence of mutations and an additional DNA binding domain increased tolerance to inhibitors of the respective altered RTs. However, the scale of the positive effect varied depending on the inhibitor. Thus, Sto7d or triple mutations allowed M-MuLV to work at a six-fold higher concentration of NaCl. However, M-MuLV RT alterations broadened the working concentration range by only 1.5–3-fold for whole blood, blood plasma, and guanidinium. All of the enzymes were equally sensitive to the presence of ethanol and urea. Thus, mutations and Sto7d mitigate the effect of inhibitors related to enzyme-template binding while being ineffective against other substances. Highly mutated enzymes were superior to most of the other inhibitors. The single exception was NaCl, when RT mut and RT-Sto mut outperformed commercial RTs. The inhibitory effect of guanidinium isothiocyanate was higher than that of guanidinium chloride.Reverse transcriptases allowing RNA to be analyzed without the constant risk of degradation are one of the cornerstones of modern biology. With numerous RTs being characterized and marketed ,6,7,8, MIn the present work, we focused on comparing popular highly mutated commercial M-MuLV variants and homemade enzymes harboring only three mutations or an additional DNA-binding domain. The intention was to determine the effect of multiple alterations in M-MuLV RT on its actual performance in RT-LAMP. Currently, most vendors provide altered M-MuLV RT with several patented mutations. The number of mutations increases over time, and the latest versions of M-MuLV can harbor up to dozens of altered amino acids. Thus, Superscript II (1988) was a truncated M-MuLV RT without RNAse H domain , SuperscHere, we made a preliminary comparison of RTs with various amounts of mutations or with an additional DNA-binding domain in RT-LAMP. As a control, we chose the truncated M-MuLV RT without the RNAse H domain, which is essentially the first version of Superscript II. RT mut, M-MuLV RT with three mutations  claimed by Fermentas in 2008 ,53, servAll of the enzymes were synthesized cDNA in concentrations of 0.4–20 U/reaction. However, 0.4 U/reaction of RT and RT mut worked visibly slower, and at more than 20 U/reaction, non-specific products accumulated during the reverse transcription step. An insufficient amount of RT leads to slower amplification and potential false-negative results, especially when the concentration of the target RNA is low ,55. In tAs expected, the optimal temperature for RTs was the same as previously reported. A slightly higher optimal temperature, 55 °C, was observed only for RT and RT-Sto. RNAse H-deficient M-MuLV RT was reported to have an optimum temperature of 42–50 °C ,58. The RTs with three mutations demonstrated the same thermal optimum, 60 °C, and were less thermostable than highly mutated superscripts. Thus, multiple mutations were proven to increase the thermal stability of M-MuLV RT in practical applications. The optimum temperature for Bst-like polymerases was in the range of 60–65 °C. Therefore, a working temperature of 60 °C was sufficient for RTs working with Bst-polymerase in a single enzymatic reaction.The overall time for analysis could be a critical parameter in a situation when many samples should be tested in a limited time. In this sense, rapid amplification attained with fast enzymes is beneficial and saves valuable machine time. Among all of the studied enzymes, M-MuLV RT H- was the slowest, while its chimeric cognate with Sto7d demonstrated a speed at the level of commercial RTs. The difference in reaction speed between other enzymes was much lower, regardless of the mutation number and presence of Sto7d. Fused enzymes demonstrated a higher reaction rate only with RT-LAMP for MS2. These observations could be a result of the intact catalytic rate and enhanced affinity to a template. Most reported mutations in M-MuLV RT affect its interaction with a template, but not the catalytic properties ,17,18. TAll chimeric and mutant enzymes showed a similar ability to produce cDNA using a minuscule quantity of RNA template. Only M-MuLV RT H- was unable to convert a low concentration of MS2 phage genomic RNA. This finding agrees with the previous observation of the reaction rate, as the enhanced affinity of altered M-MuLV RTs allowed them to utilize smaller quantities of a complex template. Interestingly, highly mutated enzymes were not superior to the triple mutant or chimeric RTs. Presumably, a slight increase in affinity to a template was enough to increase the overall sensitivity of reverse transcription. Further alterations to RTs had no positive effect on the ability to work with low template concentrations.All RTs demonstrated similar values or a minor difference in optimal concentration, reaction rate, and temperature, as well as the ability to utilize a small amount of template. However, highly mutated RTs were superior in their tolerance to various inhibitors. One possible explanation for this could be the higher affinity to a template masked in other experiments. Only when enzymes were tested close to their limits did the stronger binding allow for mitigating the effect of the inhibitor substances. The exception was NaCl, which acts as a screen for charged surfaces and prevents the interaction of proteins and nucleic acids ,60. SomeSeveral limitations of the present study need to be mentioned. The available group of commercial RTs is broad and cannot be covered in a single study. Other RTs, such as WarmStart and Maxima RT, could demonstrate a different efficacy in RT-LAMP, depending on the actual composition of mutations. Here, we only presented a short glimpse of the problem, and further extensive studies are needed to evaluate the performance of numerous RTs in practical applications. Another matter of concern is the choice of model systems. The efficacy of different enzymes could vary depending on the GC content, secondary structures, and primer concentrations ,62,63. HTo sum up, a set of mutant M-MuLV RTs was compared in RT-LAMP, defining the enzyme efficiency in two model systems. Several parameters were evaluated: optimal temperature, enzyme concentration, time for reverse transcription, ability to utilize small amounts of RNA templates, and tolerance to inhibitors. As expected, mutated RTs demonstrated a higher efficacy than intact M-MuLV RT.To the best of our knowledge, we report here on the first direct comparison in RT-LAMP of various reverse transcriptases originating from M-MuLV RT. The enzymes studied included highly mutated commercial RTs, triple mutant M-MuLV RT, fusion RTs with Sto7d, and H-M-MuLV RT. Several parameters were assessed, such as the optimal temperature, enzyme concentration, time of reverse transcription, ability to utilize small amounts of RNA templates, and tolerance to inhibitors. Unlike fused Sto7d, mutations were proven to increase the optimal temperature of the reverse transcription step in RT-LAMP. For either Sto7d, three or more mutations had a similar positive effect on the efficacy of RTs, decreasing the time for reverse transcription and enhancing the ability to utilize low concentrations of RNA templates. Highly mutated commercial enzymes proved to be superior to triple mutants and chimeric RTs in terms of tolerance to human whole blood, blood plasma, and guanidinium. However, they were found to be more sensitive to high concentrations of NaCl. The comparison of various RTs presented here is expected to be helpful for selecting the optimal enzyme to develop novel diagnostic tests based on LAMP.


Glioblastoma is a complex and aggressive primary brain tumour that is rapidly fatal. Timely and accurate diagnosis is therefore crucial. Here, we explore the newly emerging field of epitranscriptomics to understand the modifications that occur on RNA molecules in the healthy and diseased brain, focusing on glioblastoma. RNA modifications are modulated by various regulators and are diverse, specific, reversible, and involved in many aspects of brain tumour biology. Epitranscriptomic biomarkers may therefore be ideal candidates for clinical diagnostic workflows. This review summarises the current understanding of epitranscriptomics and its clinical relevance in brain cancer diagnostics.6A). The m6A mark, similar to many other RNA modifications, is strictly regulated by so-called ‘writer’, ‘reader’, and ‘eraser’ protein species. The abundance of genes coding for the expression of these regulator proteins and m6A levels shows great potential as diagnostic and predictive tools across several cancer fields. This review explores our current understanding of RNA modifications in glioma biology and the potential of epitranscriptomics to develop new diagnostic and predictive classification tools that can stratify these highly complex and heterogeneous brain tumours.RNA modifications are diverse, dynamic, and reversible transcript alterations rapidly gaining attention due to their newly defined RNA regulatory roles in cellular pathways and pathogenic mechanisms. The exciting emerging field of ‘epitranscriptomics’ is predominantly centred on studying the most abundant mRNA modification, N6-methyladenine (m I. I6A mRN6A methylation [6A binding sites, which led to the identification of specific tumour-suppressive RRACH binding miRNAs that facilitate FTO-dependent, transcript-specific demethylation [Indeed, GSCs and differentiated GBM cells have distinct patterns of mhylation . Howeverhylation . Taken thylation .6A ‘eraser’, ALKBH5, has also been linked to GSC self-renewal and tumourigenesis [The other migenesis  through igenesis  and is oigenesis . ALKBH5 igenesis .6A methylation and increased GSC tumourigenicity [6A modification are essential for GSC maintenance and dedifferentiation, with the preservation of a stem-like phenotype mediated by SOX2 mRNA stabilisation via the recruitment of ELAV1 to m6A-modified SOX2 [Contrary to reports demonstrating positive associations between reduced mgenicity ,87,88,89genicity ,91,92,936A-modified RNA species analysed, as well as the intra- and intertumoural heterogeneity of GBM. Future experimental work must be context- and compartment-dependent to explain these inconsistencies and further our understanding of m6A RNA methylation and the interplay among regulator proteins in GSCs [Discrepancies between different studies might be explained by the diversity of cell types and m in GSCs .6A RNA methylation levels and m6A regulator expression reported in GBM [6A levels and low survival, and a specific inhibitor of METTL3 slowed tumour progression in a medulloblastoma mouse model [6A RNA methylation explored above [6A RNA methylation levels relative to normal brain tissue, with decreased METTL3 and increased FTO levels in glioma tissues offered as a causative link [6A levels and reduced cell proliferation and migration in vitro, perhaps related to a disruption in HSP90 chaperone activity and enhanced apoptosis [6A writer, METTL4, was shown to promote glioma cell proliferation and migration [6A-dependent depletion of MYC levels [6A ‘eraser’, ALKBH5, have been tested and show efficient inhibition of GBM cell proliferation [ALKBH5 was reported as a significant negative prognostic factor for GBM patients [ALKBH5 expression levels in GBM tissue are inconsistent [RNA modifications play increasingly important roles in the tumourigenesis and development of GBM; several ‘writers’, ‘erasers’, and even ‘readers’ of RNA modifications are proposed as potential diagnostic biomarkers and novel targets for treatment . Yet, thd in GBM ,19. In pse model . In lineed above , Li et aive link . Here, Mpoptosis . Other spoptosis ,96. Meanigration ,97. ReceC levels . MoreoveC levels . PromisiC levels . While sC levels ,101. Simferation  enhancedferation , and impferation . While Apatients ,106, repnsistent ,107.6A ‘reader’ and member of the YTH protein family, YTHDF1. Relative to normal brain tissue, YTHDF1 has the highest expression in GBM, and is stabilised by musashi RNA-binding protein 1 (MSI1). GBM cell proliferation and migration are enhanced by the MSI1 stabilisation of YTHDF1, and high expression of these genes in GBM is associated with reduced survival [MYC (YTHDF2-MYC) and targeting IGFBP3. Interestingly, YTHDF2 expressing GSC have an increased sensitivity to cell killing by linsitinib, an IGF1/IGF1R inhibitor. As NSC cell viability is unaffected by linsitinib treatment, this drug is proposed as a potentially specific anti-GBM treatment [Another approach to sensitise glioma cells to treatment was explored by modulating msurvival . Notablysurvival ,109. Increatment .6A can also have a role in treatment response. Unfortunately, there are several inconsistencies in the literature regarding the expression levels of the regulators and global m6A levels, and more in-depth studies are required to decipher the intricate web of RNA modifications in glioblastoma.Epitranscriptomic regulators and RNA modifications are extensively involved in essential functions of the healthy brain and play various roles in brain disease. This is well shown in glioblastoma, where m6A methylation, other forms of RNA methylation also play important roles in GBM biology. For instance, the downregulation of m6Am writer, PCIF1, promotes glioma cell proliferation and tumour growth in mice [5C methylated mature miRNAs are linked to poor GBM patient outcomes through a mechanism where this modification inhibits miRNA gene silencing [5C methylation was found to have a significant role in GBM formation through a mechanism involving ‘eraser’ TET1, while TET2 and TET3 downregulation is linked to GBM tumourigenesis [Beyond m in mice . m5C metilencing . hm5C meigenesis ,114,115.igenesis ; ADAR2 wigenesis ,118. Uprigenesis ,120. Likigenesis .The field of epitranscriptomics is rapidly expanding and is anticipated to impact clinical diagnostics. The search for rapid, simple, and reliable methods to explore and detect new disease classification and therapeutic targets are essential steps for precision medicine. Currently, several RNA methylation detection methods exist, with diverse sensitivity, sample input requirements, and data analytical processes. Epitranscriptomics platforms may be antibody-based or employ mass spectrometry, polymerase chain reaction (PCR), next-generation sequencing (NGS), or nanopore direct RNA sequencing .6A or m5C enzyme-linked immunoassay (ELISA). ELISA kits are useful for assessing global m6A/m5C methylation transcriptome-wide but are unable to distinguish m6A from m6Am or detect a methylation mark at single-nucleotide or even at gene resolution. The detection of modifications at a single RNA species level can be measured by Dot-blot, another antibody-based method. This is also a relatively inexpensive method, with 100 ng to 1 μg required starting material, and can be used to detect RNA modifications in diverse RNA species [Probably, the simplest, most accessible, and affordable methods are antibody-based, i.e., m species ,124.Mass-spectrometry-based methods, i.e., liquid chromatography–coupled tandem mass spectrometry (LC–MS/MS), are more sensitive than antibody-based approaches. LC–MS/MS allows the absolute quantification of modified nucleosides, and, when coupled with nucleic acid isotope labelling (NAIL–MS), can assess the temporal dynamics of RNA modifications . A drawb6A-reverse transcription (RT)-quantitative PCR, single-base-elongation- and ligation-based PCR amplification method (SELECT), and primer-extension-based method [PCR-based approaches are locus-specific RNA modification detection methods based on the premise that the queried modification impedes reverse transcription. These methods have high sensitivity and specificity; thus, they can be used for various RNA species. Some of the most well-known PCR-based approaches are the md method ,127.6A-seq or methylated RNA immunoprecipitation sequencing (MeRIP-seq) [6A individual-nucleotide resolution crosslinking and immunoprecipitation sequencing (miCLIP-seq), m6A-label-seq, m6A-sensitive RNA-endoribonuclease-facilitated sequencing (m6A-REF-seq), MazF RNase-assisted sequencing (MAZTER-seq), and deamination adjacent to RNA modification targets (DART-seq) [6A-selective allyl chemical labelling and sequencing (m6A-SAC-seq) [6A-SAC-seq requires very little starting material (2 ng of poly A+ RNA) but is a particularly laborious process.NGS-based detection methods are diverse and frequently used for whole transcriptome profiling at single-nucleotide resolution. Some approaches require antibody coupling, such as mRIP-seq) . Other sART-seq) ,130,131.SAC-seq) ,134,135.6A, inosine, or pseudouridine [One of the most promising epitranscriptomic approaches is Oxford Nanopore Technologies (ONT) sequencing, which can detect modifications in naïve RNA, such as mouridine ,137. Comouridine . Algoritouridine ,140,141.6A mark, other detection and data analytical methods facilitate the exploration of m5C, m7G, inosine, pseudouridine, and many other modifications on RNA. Although RNA modification detection methodology and bioinformatics tools are advancing rapidly, these techniques will require further adaptation before their implementation in diagnostic clinical service laboratories where there are real sample quantity and time constraints for analytical processes and data interpretation.Beyond the m6A levels are reported as potential diagnostic markers in the peripheral blood of small-cell lung (NSCLC) and breast and gastric cancer patients relative to healthy and/or benign cancer controls [6A levels correlated to tumour-staging parameters were depleted following surgery, and accompanying upregulations in ‘writer’ and/or downregulations in ‘eraser’ genes were observed [6A levels in rheumatoid arthritis (RA) patients’ and ischemic stroke patients’ blood were elevated relative to healthy controls, along with decreased ‘eraser’ ALKBH5 and FTO levels and increased YTHDF2 ‘reader’ levels in RA samples [6A levels in the peripheral blood of individuals with Type 2 diabetes mellitus (T2DM) are lower than in controls, with concomitantly high m6A ‘eraser’ FTO gene expression [6A methylation status and gene expression of m6A regulators as diagnostic tools across a range of pathologies are presented in The potential use of epitranscriptomic regulators as biomarkers has been explored across multiple cancer types; however, only a handful of studies have attempted to measure RNA modifications in body fluids directly. Of note, high global mcontrols ,143,144.observed ,143,144. samples ,146. In pression . Similarpression ,149,150.6A regulators with significant prognostic value in a diverse range of cancer types are presented in In recent epitranscriptomics studies exploring new diagnostic classification markers, investigations often stem from in silico analyses of public sequencing datasets  to identify the predictive and prognostic significance of the diverse regulators of RNA modifications. For instance, in lung adenocarcinoma, high HNRNPC and IGF2BP3 levels correlate with reduced overall survival ,157, andIDH1/2 mutations, 1p/19q codeletion, and MGMT promoter methylation, has improved the accuracy of diagnostics, prognostication, and prediction of treatment response for glioma patients, there has been no tangible improvement in clinical management and patient outcomes. Epitranscriptomics of known glioma sub-entities may yield further advances in tumour stratification and help guide treatment selection for precision medicine, as was shown in preliminary studies of colon cancer [Like other cancer types, the value of epitranscriptomics as a tool for brain cancer diagnostics is still in its early days. The WHO classification system for primary brain tumours has integrated molecular diagnostics with traditional histopathology . While tn cancer 6A RNA methylation regulators in public primary brain cancer sequencing datasets, and these key findings are summarised in Several studies explored m6A ‘readers’ include YTHDF2 overexpression, induced through the EGFR/SRC/ERK pathway to promote tumourigenesis, invasiveness, and cell proliferation independently associated with poor prognosis [6A ‘reader’ eIF3 was performed in independent glioma cohorts as several eIF3 subunits are localised to chromosomes 1p and 19q [6A regulators IGF2BP2, IGF2BP3, HNRNPC, and YTHDF2 and m5C-related long noncoding RNAs CIRBP-AS1, GDNF-AS1, LINC00265, and ZBTB20-AS4 have prognostic significance in LGG [Key findings in glioma patients in relation to mrognosis . Similarrognosis . A syste and 19q ,200, reg and 19q . All 13  and 19q . Among o and 19q . m6A rege in LGG ,204.6A ‘writer’ METTL3 was positively associated with a higher malignant grade and poorer prognosis of IDH-wildtype but not IDH-mutant gliomas [6A regulator expression when comparing glioma cohorts comprising different IDH mutational and 1p/19q codeletion states. While these cohorts represent different glioma sub-entities with distinct molecular pathophysiology, the average survival times are vastly different, particularly between oligodendroglioma  and GBM , and thus divergent epitranscriptomic regulator expression patterns may relate to the very different biology of these distinct pathological entities [6A regulators, and a risk signature  was significantly associated with prognosis, the immune microenvironment and treatment efficacy [ALKBH5, IGF2BP2, IGF2BP3, HNRNPA2B1, YTHDF1, YTHDF2, RBM15, and WTAP) was shown to be prognostic for glioma patients, and the expression of IGF2BP2 and IGF2BP3 was linked to tumour occurrence, development, and progression [ALKBH5, which is upregulated in glioma and associated with immune signalling, and inflammatory and metabolic pathways genes [ related to m6A regulators, all with high expression in high-grade glioma, and further correlation to IDH status and transcriptome subtype [6A regulators function in the tumour microenvironment, tumour cell stemness, and infiltration have also been established [6A RNA methylation regulators and clinicopathological features and treatment sensitivity of gliomas are outlined in Expression of the m gliomas . Anotherentities . In thisefficacy . A similgression . A recenys genes . Another subtype . Links bablished . Likewisablished . Further6A regulator expression levels among the different brain tumour entities and non-tumour brain tissue, including ALKBH5, WTAP, METTL14, and YTHDC2 [6A RNA methylation regulating gene expression levels may help to explain the opposing reports of high and low global m6A in brain tumour tissue relative to ‘normal’ brains [6A regulator genes, which are summarised in 6A ‘erasers’ FTO, which is frequently observed to be downregulated in GBM relative to other tumours and control tissue [6A levels.Again, there are several conflicting reports of md YTHDC2 ,107,213.’ brains . There il tissue ,101. If 6A, m5C, and other RNA methylation profiles have helped to identify epitranscriptomic regulators and their involvement in cancer biology. While the direct assessments of RNA modifications in diagnostic laboratory settings are technologically challenging, there is a wealth of evidence supporting the significant relationships between epitranscriptomic regulator gene expressions and clinicopathological parameters in cancer, including their use as diagnostic, prognostic and/or predictive biomarkers. With distinct roles defined in stem cell biology and neural development along with high levels detected in brain tissue, RNA modifications are, theoretically, highly relevant to multiple aspects of gliomagenesis and biology. Indeed, multiple studies have reported epitranscriptome regulator signatures that are highly predictive of glioma pathology and patient outcomes.Epitranscriptome profiling studies, particularly in cancer research, are becoming more common. Studies investigating global m6A methylation levels in highly annotated cohorts of discrete tumour types as well as biofluids are warranted.Further technological advances are needed to adapt epitranscriptomic approaches for clinical diagnostics and will likely uncover entirely novel targets for further stratifying tumour molecular phenotypes, directing treatment(s) and guiding patient management. In the interim, the detection of regulator genes and global m