Membrane transporters including glucose transporters (GLUTs) are involved in cellular energy supplies, cell metabolism and other vital biological activities. They have also been implicated in cancer proliferation and metastasis, thus they represent an important target in combatting cancer. However, membrane transporters are very difficult to study due to their multispan transmembrane properties. The new computational tool, AlphaFold2, offers highly accurate predictions of three-dimensional protein structures. The glutamine, threonine and tyrosine (QTY) code provides a systematic method of rendering hydrophobic sequences into hydrophilic ones. Here, we present computational studies of native integral membrane GLUTs with 12 transmembrane helical segments determined by X-ray crystallography and CryoEM, comparing the AlphaFold2-predicted native structure to their water-soluble QTY variants predicted by AlphaFold2. In the native structures of the transmembrane helices, there are hydrophobic amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F). Applying the QTY code, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids, glutamine (Q), threonine (T) and tyrosine (Y) rendering them water-soluble. We present the superposed structures of native GLUTs and their water-soluble QTY variants. The superposed structures show remarkable similar residue mean square distance values between 0.47 and 3.6 Å (most about 1–2 Å) despite >44% transmembrane amino acid differences. We also show the differences of hydrophobicity patches between the native membrane transporters and their QTY variants. We explain the rationale why the membrane protein QTY variants become water-soluble. Our study provides insight into the differences between the hydrophobic helices and hydrophilic helices, and offers confirmation of the QTY method for studying multispan transmembrane proteins and other aggregated proteins through their water-soluble variants. For detailed transporter protein sequence information, the predicted 2D structures and hydrophobicity change, please see Supplementary The native sequences for GLUT1 through GLUT14 and their QTY-variant sequences were aligned using the same methods previously described  and are also available at Uniprot website https://www.uniprot.org. Each UniProt ID from the dataset was extended with ID, entry name, description, and FASTA sequence. The data was taken from UniProt using a custom Python code. The FASTA sequences were converted into their soluble versions using the QTY method (https://pss.sjtu.edu.cn/), followed by Protter 2D diagram plotting (http://wlab.ethz.ch/protter/start/). These steps were optimised via Python libraries for web applications such as requests and splinter.Structure predictions of the QTY variants were performed using the AlphaFold2  and GLUT3  were obtained from the protein data bank (PDB), https://www.rcsb.org. AlphaFold2 predictions of 12 native GLUTs and their QTY variants were carried out using the AlphaFold2 program at https://github.com/sokrypton/ColabFold. All 14 native GLUT sequences are obtained from Uniprot https://www.uniprot.org. The X-ray crystal structures and predicted structures were aligned with PyMOL.The published X-ray crystal structures of native GLUT1  and Y (tyrosine) form three water hydrogen bonds, one donor from H (hydrogen) and two acceptors from O (oxygen).We aligned the native GLUTs with their QTY variants. Despite significant QTY replacement of hydrophobic residues overall ~17–26%), especially in the transmembrane domains (~44–50%) in the GLUTs, the isoelectric-focusing point pI and molecular weight remain rather similar 6%, espec. This isversus Q, I,V versus T and F versus Y , threonine (T) and tyrosine (Y) to replace four hydrophobic amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F), since their electron density maps share remarkable structure similarities between L isoelectrical focusing points (pIs) are identical for the native and QTY variants for GLUT4, GLUT5 and GLUT13 despite significant QTY sequence replacement. The pIs in the QTY variants have little changes, and in some examples, there are no change. This is because that three amino acids Q, T, Y do not bear any charges at neutral pH. Thus, introductions of these amino acids Q, T, Y do not significantly alter the pI. This is important since altered pI may result the non-specific interactions.The isoelectric-focusing points (pI) vary, some in the acidic and some in the basic range. For example, native GLUT1, GLUT2, GLUT6, GLUT7, GLUT9, GLUT10, GLUT11, GLUT12 and GLUT14 have basic pIs > 8.0. On the other hand, GLUT5 and GLUT13 have acidic pIs < 6.0. Others including GLUT3, GLUT4, GLUT8 have near neutral pIs, ~ 6.84–7.5 . NotablyFurthermore, although there are between ~43.6 and ~50% transmembrane QTY replacements, the molecular weights of the native and QTY variants differ by only a few hundreds of Daltons. This is because the addition of −OH groups from QTY amino acids increased their molecular weights .6THA)   and GLUT3 (4ZW9), and the (ii) AlphaFold2 predicted water-soluble QTY variants (cyan). As seen from The native structures and water-soluble QTY variants superposed very well, the RMSD is GLUT1 and GLUT1versus GLUT1QTY (1.545 Å), GLUT2 versus GLUT2QTY (3.058 Å), GLUT3 versus GLUT3QTY (1.025 Å), GLUT4 versus GLUT4QTY (0.764 Å), GLUT5 versus GLUT5QTY (0.712 Å), GLUT6 versus GLUT6QTY (1.910 Å), GLUT7 versus GLUT7QTY (0.470 Å), GLUT8 versus GLUT8QTY (1.169 Å), GLUT9 versus GLUT9QTY (0.593 Å), GLUT10 versus GLUT10QTY (1.186 Å), GLUT11 versus GLUT11QTY (1.502 Å), GLUT12 versus GLUT12QTY (3.590 Å), GLUT13 versus GLUT13QTY (0.881 Å) and GLUT14 versus GLUT14QTY (2.720 Å)  are not yet available, we used AlphaFold2 predictions. The residue mean-square distances (RMSD in Å) for the superposed structures are also shown in The native GLUTs are highly hydrophobic, especially in the 12TM helical domains, thus they are intrinsically water-insoluble and require detergents to solubilise them after removing them from the lipid bilayer membranes. Without the appropriate detergents, they immediately aggregate, precipitate and lose their biological functions.The 12TM domains are directly embedded in the hydrophobic lipid bilayer so the hydrophobic side chains of amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F) directly interact with the lipid molecules excluding water. Thus, they display highly hydrophobic patches on the 12TM domain .et al., et al., et al., et al., et al., et al., et al., On the other hand, after systematic QTY replacement of hydrophobic amino acids L, I, V, F, with hydrophilic amino acids Q, T, Y, these hydrophobic patches become largely reduced  and 6. TNature has already evolved three distinct types of alpha-helices: (1) the hydrophilic alpha-helix such as found in haemoglobin and many other water-soluble enzymes and circulating proteins such as growth factors, cytokines and antibodies; (2) the hydrophobic alpha-helix such as in transporters and other integral transmembrane proteins found in G protein-coupled receptors, ion channels, photosynthesis systems and (3) amphiphilic alpha-helices with both hydrophilic and hydrophobic amino acid residues. These three types of alpha-helices have nearly identical molecular structures, irrespective of the hydrophobicity and hydrophilicity . The number will continuously increase over time.DeepMind AlphaFold’s team has already deposited ~ 1 million open protein structures in the EBI  and GLUT3 (PDB: 4ZW9) revealing remarkable similarity.This is a quite important approach that has potential vital impact on further studies glucose transporters saving significant amount of time and finances in analysis of function of this group of proteins.It would be important ifthe authors will address some questions:It was not clear how the solubility of the potential hydrophilic glucose transporters will change their functions. It would be good to model their interactions with glucose. It was not clear if the cancer related overexpression/upregulation glucose transporters were related to their incorporation into membranes or changes in their transport of glucose. Highly possible that this info has been published in some papers and that must be verified and analysed in silico.Plausibly that these channels by which glucose is transported though the membrane were changed, but this issue was not analysed.What is importance of the isoelectric focusing point of glucose transporters?Is it different in the cancer cells? Hope this is known from previous publications.Additional commentsIt would be much easier for a reader to have a table where GLUT 1-14 would be listed and in this table related cancers, availability of a structure, and references would be indicated.This part of introduction is rather difficult to read and recapitulate the most important facts.It was not clear if these glucose transporters were mutated in cancerous cell and were not able to be incorporated into membranes or just did not function. That would be good to know.It would be good to see in the figure where is the glucose channel located within the proteins obtained by X-ray.It would be important to see the same areas in silico mutants.It would be recommended to replace throughout the entire MS the word "superimpose" to "superpose"Superposed - Place (something) on or above something else, esp. so that they coincide. Used mostly in scientific or mathematical contexts. To place one geometric figure on top of another in such a way that all common parts coincide.Superimpose-To place an object over another object, usually in such a way that both will be visible. Typically related to 2D images, one does not worry if they are not coinciding.Figure 3. It would be good if the authors will check the orientations GLUT1 and GLUT3 in this figure. It seems that views (front and side) are swapped for GLUT3 compared to the view of GLU1.The last panel of GLUT3  seems to be rotated in plane and in a different orientation with respect to GLUT1 as well. It is strongly recommended to indicate where are the N - and C-termini and the angles between different views. Nothing is shown in green. Please check the legend. Comments to Author: The authors have demonstrated that 14 glucose transporters’ structures and their water-soluble counterparts were solved using AlphaFold2 and their QTY tool. They have demonstrated that the native and water-soluble variants are structurally similar and the water-soluble variants are indeed hydrophilic from the decreased hydrophobic patches. I would like to request the following revisions:1. The abbreviation QTY is not defined anywhere in the abstract or in the introduction. Please state and combine these later sentences as soon as the "QTY tool" is mentioned: "In the native structures of the transmembrane helices, there are hydrophobic amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F). Applying the QTY code, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids glutamine (Q), threonine (T) and tyrosine (Y) rendering them water- soluble."2. The introduction should also mention RosettaFold - AlphaFold2 is not the only available tool that predicts structure from sequence. Was there a reason why AlphaFold2 was used over RosettaFold?3. Which "conventional computing programs" were used for the previous QTY application?4. There should be relevant references for the sentence "The difficulties in obtaining structures of transmembrane species experimentally are well-known" and for the sentence "In addition to targeting the glucose uptake activity of cancer cells, these water-soluble glucose transporters can prospectively find many additional applications, such as ultrasensitive glucose sensing devices, as water-soluble antigens to generate therapeutic monoclonal antibodies."5. Please state the implications of the pIs changing. It’s also unclear whether the stated pIs in the sentence are for native or water-soluble variants.6. Were the pLDDT scores high for these structures? How confident should we be in using these structures?7. The time it takes in predicting the structure is stated in minutes-weeks but when using what kind of computing resources? Please clarify.8. Please state the advantages and disadvantages for using the QTY method and anything that users should be aware of when applying this for other systems and using outputs from this method.9. The SI should include all of the structures of the native and water-soluble variants of these glucose transporters (in PDB format) for researchers who can use them for MD simulations. Also, at what pH, ionic concentration, and temperature are these structures expected to be in? That would be useful information for researchers setting up the simulations. Comments to Author: Reviewer #2: The authors have demonstrated that 14 glucose transporters’ structures and their water-soluble counterparts were solved using AlphaFold2 and their QTY tool. They have demonstrated that the native and water-soluble variants are structurally similar and the water-soluble variants are indeed hydrophilic from the decreased hydrophobic patches. I would like to request the following revisions:1. The abbreviation QTY is not defined anywhere in the abstract or in the introduction. Please state and combine these later sentences as soon as the "QTY tool" is mentioned: "In the native structures of the transmembrane helices, there are hydrophobic amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F). Applying the QTY code, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids glutamine (Q), threonine (T) and tyrosine (Y) rendering them water- soluble."2. The introduction should also mention RosettaFold - AlphaFold2 is not the only available tool that predicts structure from sequence. Was there a reason why AlphaFold2 was used over RosettaFold?3. Which "conventional computing programs" were used for the previous QTY application?4. There should be relevant references for the sentence "The difficulties in obtaining structures of transmembrane species experimentally are well-known" and for the sentence "In addition to targeting the glucose uptake activity of cancer cells, these water-soluble glucose transporters can prospectively find many additional applications, such as ultrasensitive glucose sensing devices, as water-soluble antigens to generate therapeutic monoclonal antibodies."5. Please state the implications of the pIs changing. It’s also unclear whether the stated pIs in the sentence are for native or water-soluble variants.6. Were the pLDDT scores high for these structures? How confident should we be in using these structures?7. The time it takes in predicting the structure is stated in minutes-weeks but when using what kind of computing resources? Please clarify.8. Please state the advantages and disadvantages for using the QTY method and anything that users should be aware of when applying this for other systems and using outputs from this method.9. The SI should include all of the structures of the native and water-soluble variants of these glucose transporters (in PDB format) for researchers who can use them for MD simulations. Also, at what pH, ionic concentration, and temperature are these structures expected to be in? That would be useful information for researchers setting up the simulations.Reviewer #3: This is an interesting paper reporting analysis of mutated in silico hydrophobic sequences into hydrophilic ones in transmembrane helical segments using the QTY tool in analysis of glucose transporters that are implicated in various cancers. The authors have used the AlphaFold2 predictions for potential water-soluble QTY variants of these membrane transporters by mutating the transmembrane helices making them hydrophilic. The predicted structures of mutated glucose transporters GLUT2, and GLUT4 to GLUT14 were compared with known structures of GLUT1 (PDB: 6THA) and GLUT3 (PDB: 4ZW9) revealing remarkable similarity.This is a quite important approach that has potential vital impact on further studies glucose transporters saving significant amount of time and finances in analysis of function of this group of proteins.It would be important ifthe authors will address some questions:It was not clear how the solubility of the potential hydrophilic glucose transporters will change their functions. It would be good to model their interactions with glucose. It was not clear if the cancer related overexpression/upregulation glucose transporters were related to their incorporation into membranes or changes in their transport of glucose. Highly possible that this info has been published in some papers and that must be verified and analysed in silico.Plausibly that these channels by which glucose is transported though the membrane were changed, but this issue was not analysed.What is importance of the isoelectric focusing point of glucose transporters?Is it different in the cancer cells? Hope this is known from previous publications.Additional commentsIt would be much easier for a reader to have a table where GLUT 1-14 would be listed and in this table related cancers, availability of a structure, and references would be indicated.This part of introduction is rather difficult to read and recapitulate the most important facts.It was not clear if these glucose transporters were mutated in cancerous cell and were not able to be incorporated into membranes or just did not function. That would be good to know.It would be good to see in the figure where is the glucose channel located within the proteins obtained by X-ray.It would be important to see the same areas in silico mutants.It would be recommended to replace throughout the entire MS the word "superimpose" to "superpose"Superposed - Place (something) on or above something else, esp. so that they coincide. Used mostly in scientific or mathematical contexts. To place one geometric figure on top of another in such a way that all common parts coincide.Superimpose-To place an object over another object, usually in such a way that both will be visible. Typically related to 2D images, one does not worry if they are not coinciding.Figure 3. It would be good if the authors will check the orientations GLUT1 and GLUT3 in this figure. It seems that views (front and side) are swapped for GLUT3 compared to the view of GLU1.The last panel of GLUT3  seems to be rotated in plane and in a different orientation with respect to GLUT1 as well. It is strongly recommended to indicate where are the N - and C-termini and the angles between different views. Nothing is shown in green. Please check the legend. No accompanying comment.


As organisms age, they experience time-related decline in their physiological function, which is accompanied by increased incidence of morbidity and mortality. Aging is proposed to be driven by twelve pathways, or hallmarks, such as genome instability, epigenetic alterations, loss of proteostasis, and chronic inflammation . Importain vivo demonstrations of R-loops in the 1990s, and especially during the last decade, R-loops have been primarily studied in dividing cells, with very limited exploration in post-mitotic cells such as neurons. Nevertheless, due to their high level of transcription and post-transcriptional RNA processing, neurons might be particularly vulnerable to perturbations in gene expression due to increased R-loop formation.During transcription, a nascent RNA commonly anneals back to the template DNA strand, thus generating an RNA-DNA hybrid and displaced ssDNA, also called an R-loop . R-loopsDrosophilamelanogaster photoreceptor (PR) neurons and eyes, respectively, revealed decreased levels of several factors involved in RNA metabolism [Our transcriptome and protein profiling of aging tabolism , 4. Thistabolism . To furttabolism , and adaDrosophila. We observed approximately a 10% increase in R-loop levels in flies depleted for Top3β, accompanied by decreased visual response, and decreased expression of a subset of long genes. Importantly, reduction in R-loop levels by eye-specific over-expression of either Top3β or RNase H1 enzyme mitigated age-associated loss of visual function in flies, highlighting the importance of maintaining R-loop homeostasis in gene expression during aging.Further analysis revealed that long genes are particularly susceptible to R-loop formation in aging neurons, possibly through an increase in topological stress generated during transcription elongation. During transcription, movement of elongating RNA polymerase creates underwinding of DNA behind the polymerase, where the more relaxed DNA is prone to RNA transcript invasion and R-loop formation. To resolve torsional stress that occurs during transcription, cells use topoisomerase enzymes to solve both RNA and DNA topological problems. Topoisomerase 3 β (Top3β) is a highly conserved, dual-activity RNA/DNA topoisomerase whose loss of function has been associated with neurological disorders in humans and a decreased lifespan in mice. Given that we observed decreased Top3β protein levels in eyes of aging flies, we decided to test its role in gene expression during neuronal aging. Similar to previous studies in mammalian cells, we demonstrated that Top3β plays a role in R-loop biology during aging in de novo R-loops. Instead, we observed widening of R-loop peaks, which suggests that their increased levels result from stabilization or expansion. Therefore, we propose that age-associated R-loop accumulation is not due to increased formation, but rather to failure to resolve existing R-loops. Notably, recent evidence shows that stable R-loops can be processed into DNA breaks, followed by export of cleaved RNA-DNA hybrids into the cytoplasm, where they can trigger the innate immune response [Our data is in line with a recent report by Gyenis et al., showing substantial mammalian RNA polymerase (RNAP) II stalling during transcription elongation, which leads to a gene-length dependent decrease in transcription . The autresponse . This poOur study provides first evidence of R-loop accumulation in aging neurons and a contributing role in loss of neuronal function during aging. Undoubtedly, R-loops are at the crossroads of several hallmarks of aging, namely transcriptional stress, genome instability, and chronic immune response. Targeting R-loop levels thus may help restore these pathways to a normal/healthy state and slow down or prevent the onset of age-dependent neurodegenerative diseases.


We aimed to investigate the different metastases and prognoses of neuroblastoma (NB) and determine the risk factors of metastasis.Data of 1224 patients with NB were obtained from the Surveillance, Epidemiology and End Results database (2010–2018). Pearson’s chi-square test, Kaplan–Meier analysis, multivariable logistic regression and Cox regression analysis were used to determine the factors associated with prognosis.P < 0.001; CSS: HR: 0.267, P < 0.001). The bone and liver were the two most common isolated metastatic sites in NB. However, no statistical difference was observed in OS and CSS between the only bone metastasis group, only liver metastasis group and bone metastasis combined with liver metastasis group . Additionally, age at diagnosis > 1 year (odds ratio (OR): 3.295, P < 0 .001), grades III–IV  and 5–10 cm tumours  increased the risk of bone metastasis of NB. Moreover, no surgical treatment  increased the risk of liver metastasis of NB.The overall incidence of NB was an age-adjusted rate of 8.2 patients per 1,000,000 children. In total, 1224 patients were included in our study, with 599 patients (48.9%) exhibiting distant metastases. Compared to patients with non-metastatic NB, a greater proportion of patients with metastatic NB were under 1 year, male, had an adrenal primary site, unilateral tumour, a tumour size > 10 cm, neuroblastoma-not otherwise specified (NB-NOS), second malignant neoplasms and were more likely to choose radiotherapy and chemotherapy. Multivariate Cox regression showed that metastasis was an independent risk factor for overall survival (OS) and cancer-specific survival (CSS). The survival rate of non-metastatic patients with NB was better than those with metastasis (OS: hazard ratio (HR): 0.248, Metastatic NB has unique clinicopathological features, with the bone and liver as the most common single metastatic sites of NB. Therefore, more aggressive treatment is recommended for high-risk children with NB displaying distant metastases. Neuroblastoma (NB) is a highly metastatic malignant tumour, with 70% of patients exhibiting metastasis at the time of diagnosis , along wCurrently, internationally recognised treatment-based risk stratification of NB include age, histology, grade, MYCN, 11q aberration and DNA ploidy , 6. The Therefore, this study aims to use the Surveillance, Epidemiology, and End Results (SEER) database to analyse the metastatic pattern of NB, screen for metastasis-related risk factors and determine the clinicopathological features and independent prognostic factors of metastatic patients.We used the SEER database and SEER-stat software (SEER*Stat 8.3.9) to identify and collect the data of patients aged ≤ 18 years with a confirmed NB diagnosis according to the International Classification of Diseases for Oncology (ICD-O-3) between 2010 and 2018. Data on the following variables were extracted: age at diagnosis, gender, race, year of diagnosis, primary site, tumour grade, laterality, tumour size, histology, chemotherapy, surgery, radiotherapy, second malignant neoplasms (SMN), metastasis, survival time and cause of death. SMN was defined as the appearance of other malignant tumours at least 2 months after NB diagnosis.Tumour grades were defined as follows: (1) grade I, also called well-differentiated; (2) grade II, moderately differentiated; (3) grade III, poorly differentiated; and (4) grade IV, undifferentiated or anaplastic.The metastasis of distant organs is defined in SEER as the state of metastasis of distant organs at the time of the first diagnosis of cancer. Among the distant organs, bone metastasis did not include bone marrow metastasis.The inclusion criteria were as follows: 1) patients with ICD-O-3 Hist/behave = neuroblastoma (ICD-O-3 = 9500) or ganglioneuroblastoma (ICD-O-3 = 9490) and (2) patients with NB as the primary tumour. The exclusion criteria were as follows: (1) lack of follow-up data; (2) diagnosis not confirmed by pathological examinations; and (3) age > 18 years. The research flow is presented in Fig.  patientsAll data in the SEER database are anonymised; therefore, ethical approval was not required for this study.P < 0.05) for inclusion in the multivariate Cox regression models. Furthermore, logistic regression was used to estimate the odds ratio (OR) of metastasis in patients with NB.The age-standardised incidence of NB based on the 2000 US Standard Population was identified from the cancer registries of the SEER database. The annual percentage change (APC) of incidence was calculated using the weighted least squares method. Moreover, as the cause of death of seven children was unknown, CSS was excluded from the analysis. Kaplan–Meier was used for survival analysis and between-group differences were assessed using Pearson’s chi-square test and Fisher’s exact probability method. Univariate Cox analysis was performed to identify factors  and R software (version 3.6.3), namely the ‘survival’ and ‘ggplot2’ packages. P > 0.05]  15.0–67.0 months). The overall age-adjusted invasive NB incidence rate for SEER from 2010 to 2018 was 8.2 per 1,000,000 children annually. Notably, the annual incidence rate showed no downtrend decline annually. Furthermore, the APC was 0.1% [95% confidence interval (CI), − 0.2 to 0.4%; P ≤ 0.05). Furthermore, the metastatic group underwent more radiation therapy and chemotherapy and had lesser surgical interventions than the non-metastatic group. However, both groups were identical in race and tumour grade.A total of 599 (48.9%) patients displayed distant metastases. Moreover, 1003 (81.9%) tumours were NB-not otherwise specified (NOS) and 221 (18.1%) were ganglioneuroblastoma. Table P < 0.05) .Univariate analysis showed that age, primary site, laterality, tumour size, histology, chemotherapy, radiotherapy and metastasis were the risk factors for OS and CSS  Table . Additio5) Table . FurtherAs shown in Table vs 53.8%, P < 0.001; liver metastasis only: 22.6% vs 2.0%, P < 0.001). In terms of multiple organ metastasis, the incidence of bone + liver and liver + lung in the ≤ 1-year group was higher than that in > 1 year group .Furthermore, we compared the differences in metastasis pattern between the ≤ 1-year group and > 1-year group, wherein the > 1-year group had a higher incidence of bone metastasis and a lower incidence of liver metastasis than the ≤ 1-year group . As shown in Fig. Kaplan–Meier analysis revealed that the patients without bone metastasis had a better 5-year OS and CSS than those with bone metastasis , tumour grades III–IV  and tumour size 5–10 cm  increased bone metastasis risk in NB. However, a non-adrenal primary site and lack of chemotherapy or radiotherapy without liver, brain or lung metastasis decreased the risk of bone metastasis  increased liver metastasis risk in NB; however, diagnosis > 1 year, non-adrenal primary site, ganglioneuroblastoma, lack of chemotherapy and lack of bone or lung metastasis decreased the risk of liver metastasis  [Currently, new therapies for NB mainly include immunotherapy, targeted therapy, tandem-autologous transplantation or a combination of multiple methods. In immunotherapy, the combination of GD2 monoclonal antibody (mAb), chemotherapy regimen and autologous NK cells or the combination of anti-GD2 antibody and other antibodies or CART, such as CD47 mAb, have been reported to improve the tumour immunosuppressive microenvironment and enhance the therapeutic effect , 16. Che= 0.006) . For higWhen the different metastasis patterns of NB were compared, we found that bone metastasis (42.6%) and liver metastasis (10.9%) were the most common single-organ metastasis, and the combination of bone and liver metastasis in multi-organ metastasis reached 9.5%. On exploring the risk factors of bone and liver metastases, we found that patients without lung metastasis had a lower risk of bone or liver metastasis. Furthermore, although only lung metastasis (1.3%) is uncommon, lung metastases were accelerated when tumours metastasised to other sites (6.6%). Therefore, it was speculated that once the tumour metastasises to one distant tissue, it could accelerate the metastasis in other tissues, which needs to be considered in a clinical setting. There are many conjectures about the mechanism of NB with distant metastasis: (1) NB originates from the sympathetic nervous system; hence, it might distribute along the sympathetic nervous system; (2) NB is an immune cold tumour with multiple immune escape mechanisms, such as Myc and N-Myc downregulating MHC-I to circumvent T cell-mediated cytotoxicity  or tumouNext, we explored the risk factors of patients with bone metastases and liver metastases, respectively, and found that the adrenal primary site was a risk factor for liver and bone metastasis, which could be attributed to the different biological characteristics of NB at different origins, such as ALK mutation levels and MYCN amplification situation , 31. FurThis study acknowledges its limitations. First, missing data in the sample inevitably introduces selection bias. Second, there is a lack of information on chemotherapy regimens, tumour recurrence, immunotherapy, targeted therapy, stem cell transplantation, INSS stage, monthly age and MYCN expansion. Third, owing to the difficulty in obtaining the data on other metastatic sites, only the information available on the liver, lung, bone and brain was used in this study. Finally, this is a retrospective study that requires further prospective, multicentre and large-scale studies to validate our findings.The population-based analysis of 1224 patients with NB revealed the incidence of metastasis to be 48.9% of all NB cases. Moreover, metastasis was an independent risk factor for OS and CSS. Bone metastasis and liver metastasis were the common distant metastasis patterns of NB, with age > 1 year, grades III–IV and tumour size 5–10 cm increasing the risk of bone metastasis in NB. Additionally, surgical treatment reduced the risk of liver metastasis in NB. For high-risk NB with distant metastasis, a combination of tandem autologous stem cell transplantation, immunotherapy and targeted therapy is recommended to improve its poor prognosis.


This paper proposes an improved target detection algorithm, SDE-YOLO, based on the YOLOv5s framework, to address the low detection accuracy, misdetection, and leakage in blood cell detection caused by existing single-stage and two-stage detection algorithms. Initially, the Swin Transformer is integrated into the back-end of the backbone to extract the features in a better way. Then, the 32 × 32 network layer in the path-aggregation network (PANet) is removed to decrease the number of parameters in the network while increasing its accuracy in detecting small targets. Moreover, PANet substitutes traditional convolution with depth-separable convolution to accurately recognize small targets while maintaining a fast speed. Finally, replacing the complete intersection over union (CIOU) loss function with the Euclidean intersection over union (EIOU) loss function can help address the imbalance of positive and negative samples and speed up the convergence rate. The SDE-YOLO algorithm achieves a mAP of 99.5%, 95.3%, and 93.3% on the BCCD blood cell dataset for white blood cells, red blood cells, and platelets, respectively, which is an improvement over other single-stage and two-stage algorithms such as SSD, YOLOv4, and YOLOv5s. The experiment yields excellent results, and the algorithm detects blood cells very well. The SDE-YOLO algorithm also has advantages in accuracy and real-time blood cell detection performance compared to the YOLOv7 and YOLOv8 technologies. Blood cell testing is a technique that analyses blood’s cellular components using specialist instruments. In medicine, the cells circulating in the blood are divided into three categories: white blood cells, red blood cells, and platelets . When thPreviously, blood cell testing mainly used traditional image-processing techniques, most classically, manual photography and staining, followed by manual observation and classification with a microscope , which rDue to the diversity of microscopic image data, traditional image-processing algorithms ,5 can noThe emergence of deep learning ,16,17,18The two-stage algorithms are represented by the RCNN , Faster The single-stage algorithms are represented by SSD  and YOLOThe detection results of blood cells under single-stage and two-stage algorithms, depicted in Recently, some works ,30,31 haFusing the Swin Transformer  with theDeleting the 32 × 32 down-sampling layer in PAN can reduce the number of network parameters while improving the detection rate;Replacing ordinary convolution with depth-separable convolution  in PANetReplacing the original CIOU loss function with the EIOU  loss funIn summary, in this paper, we incorporate the Swin Transformer attention mechanism into YOLOv5s, which we call SDE-YOLO, and SDE-YOLO can solve the problems of insufficient detection accuracy and misdetection and omission in both single-stage and two-stage algorithms, as well as the issue of parameter computation in the attention mechanism. The innovation of this paper is as follows:YOLOv5 is divided into four models, s, m, l, and x, according to the depth and width , and, siOn the Input side, different sizes of input samples are scaled to a fixed size by adaptive image and then fed into the Backbone networks for training. The data enhancement of YOLOv5s adopts Mosaic data enhancement to solve the problems of insufficient samples and uneven distribution in the dataset. Mosaic data enhancement splices four images by random cropping, random brightness adjustment, and flipping, which reduces GPU use and enhances the network’s robustness.The Backbone networks of YOLOv5s mainly consist of the CS, SPPF, and CBS modules, as shown in The Neck parts of YOLOv5s adopt the structure of the feature pyramid network (FPN)  + the paThe head of YOLOv5s uses three 1 × 1 convolutional layers to replace the fully connected layer (FC) for prediction and classification. It generates three different scales of feature maps, namely, 20 × 20, 40 × 40, and 80 × 80, and the outputs are 4 + 1 + C, which are the co-ordinates of the prediction frames, the target confidence, and the probability of the category values, respectively.The loss function mainly comprises the confidence loss The Swin Transformer is based on the transformer with a global information modelling capability to construct hierarchical feature maps while drawing on the idea of the locality to limit the self-attention computation to the non-overlapping window area and allow moving windows for feature interaction, with linear computational complexity for the image size, which can be used as the backbone network of YOLOv5s to extract image features more effectively.As shown in The Swin Transformer block of the Swin Transformer consists of two modules: the window-based multi-head attention mechanism and mobile window-based multi-head attention mechanism. The information passes through the layer norm layer, window-based multi-head self-attention (W-MSA) unit, multilayer perceptron (MLP) layer, and jump connection of the left module to complete the multi-head self-attention calculation based on the window, and then passes through the Layer Norm layer, shifted window-based multi-head self-attention (SW-MSA) unit, MLP layer, and jump connection of the suitable module to complete the multi-head self-attention calculation based on the mobile window. The two modules have the same input and output information dimensions and are directly connected.Window-based multi-head self-attention (W-MSA) models the dependency of a local window instead of the global one in multi-head self-attention (MSW), which helps to reduce the complexity of the model computation. W-MSA is similar to conv: it also has two parameters, the kernel size and stride, but W-MSA has the same kernel size and stride in the same stage, which leads to a problem—there is no difference between the receptive field of 1 W-MSA and n W-MSAs in a stage. A patch is applied to the W-MSA to get shifted window-based multi-head self-attention (SW-MSA) to alleviate this problem.From the above, we know that the PANet network outputs predicted results by 8 × 8, 16 × 16, and 32 × 32 times, down-sampling for three small, medium, and large targets. Taking our blood cell BCCD dataset as an example, our size is 640 × 640 when we perform 8 × 8, 16 × 16, and 32 × 32 down-sampling. The output prediction layers are 80 × 80, 40 × 40, and 20 × 20, respectively. However, since the target sizes of our blood cell BCCD dataset are between 8 × 9 and 285 × 442, and the small- and medium-size targets take up 95% of the total cells, when the image is down-sampled by 32×, the blood cells smaller than 102 × 102 in the image will compress one pixel, which will lead to our inefficiency in small cell detection. In this paper, we have opted not to employ the 32 × 32 down-sampling correlation network layer, which will enable a faster cell detection process while reducing the number of parameters of the network model.Deep separable convolution combines deep convolution and dot convolution (1 × 1 convolution). Deep convolution first performs a separate deep convolution for each input channel, and then mixes the output channels by dot convolution (1 × 1 convolution). The inputs are subject to filtering and combining operations in a standard convolution. Still, the deep separable convolution splits this operation by subjecting the information to a filtering operation in the first step, and then combining them in the second step. Composing a standard convolution reduces the network model’s computational cost and size. M is the number of input channels, N is the number of output channels.A feature map F of size The computational cost The computational cost of deep convolution The computational cost of deep convolution A linear combination of depth convolution and point-by-point convolution (1 × 1 convolution) is called depth-separable convolution. The computational cost of the depth-separable convolution is the sum of the computational cost of the depth convolution and the 1 × 1 point-by-point convolution as in Equation (7).Splitting the standard convolution into two steps, filtering and combining, can drastically reduce the amount of parameter computation, the computational cost of the depth-separable convolution, and the computational cost of the standard convolution, two specific parameter pairs, shown in Equation (8).When we use a deeply separable convolution with a convolution kernel of 3 × 3, the computational effort is one-eighth to one-ninth that of a standard convolution using the same-size convolution kernel, with essentially no difference in accuracy. By substituting the usual convolution in the PANet network with a deeply separable convolution, the detection of small targets improves, while the accuracy and real-time detection are maintained.p ≥ 0.5) is large, the loss value of the positive samples is smaller than that of the negative samples, which makes it difficult for the positive samples to influence the training parameters, thus affecting the detection accuracy. This paper adopts the EIOU loss function to solve the above problem, which is shown in Equation (9).From the classification loss function in Equation (1), it can see that when the predicted probability value , RBCs (red blood cells), and Platelets. The data associated with each image, such as the location and number of cells, serve as a reference point for assessing the algorithm’s performance. The dataset consists of 768 images for training and 106 for testing, with the instances of three types of blood cells displayed in This paper’s experiments use Ubuntu 18.4.0 as the operating system. The experiments are conducted on the E-2136 CPU with a Quadro P5000 graphics card with 16 GB of video memory as its hardware configuration. The PyTorch 1.7.1 version is employed.In In this research, the accuracy of the algorithm is assessed based on three factors : experimTP represents the number of true positives—instances when the model’s assessment is accurate, and the label is positive. FP represents the number of false positives—instances when the model’s assessment is wrong, and the label is positive. FN represents the number of false negative—instances when the model’s assessment is wrong, and the label is negative. TN represents the number of true negatives—instances when the model’s assessment is accurate, and the label is negative. The precision of a blood cell category measures the accuracy of the predicted category out of the total number of predictions. At the same time, the recall is the accuracy of the predicted category out of the total number of actual categories. The number of distinct blood cell types represents N in Equation (12), and the mean average precision of all the categories is mAP.The performance of SDE-YOLO for WBCs (white blood cells), RBCs (red blood cells), and Platelets is visualized in The blood cell detection outcomes are presented in The TP confusion matrix we generated is shown in We can see from SDE-YOLO has a lower FPS than YOLOv5s  due to tThe experimental results indicate that the SDE-YOLO algorithm is the most effective of the models examined, as it improves the mAP without compromising the detection speed.The graph in SDE-YOLO is compared with YOLOv7. The F1_curve is the same as YOLOv7; both are 0.92. SDE-YOLO is 1.3% less than YOLOv7 in terms of precision, but SDE-YOLO is 0.1% more than YOLOv7 in terms of mAP, and 31.3 more in terms of FPS than YOLOv7.Compared with YOLOv8, SDE-YOLO is 0.7% less than YOLOv8 in mAP, 11.4% less than YOLOv8 in FPS, and 1% less than YOLOv8 in F1_curve. However, SDE-YOLO is 1.3% higher than YOLOv8 in terms of accuracy.SDE-YOLO has its advantages in performance over both YOLOv7 and YOLOv8, and all three models meet the accuracy and speed requirements.We also assess SDE-YOLO on additional datasets to corroborate its generalization. The first dataset is the LIDC-IDRI set , a medicThe experimental results show that SDE-YOLO achieves a mAP of 72% on the LIDC-IDRI dataset, which is higher than the 70% of YOLOv5s. As illustrated in The mAP of SDE-YOLO on the TT100K dataset is comparable to YOLOv5s, which are both 96%, and In conclusion, SDE-YOLO has demonstrated its effectiveness across various datasets.We run ablation experiments to assess the impact of each component on SDE-YOLO, with YOLOv5s as the foundation. A represents the combination of Swin Transformer, B represents the excision of the 32 × 32 network layer, C signifies the implementation of depth-separable convolution, and D indicates the use of the EIOU loss function to raise the effectiveness. The findings of the experiment are displayed in This paper presents a new algorithm, SDE-YOLO, which improves upon YOLOv5s. To begin with, we present an overview of the YOLOv5s architecture. Next, this paper outlines four new ideas: the integration of the Swin Transformer, the deletion of the 32 × 32 target layer in PANet, the substitution of standard convolution with depth-separable convolution in PANet, and the use of the EIOU loss function in place of the CIOU loss function. This paper evaluates the single-stage and two-stage algorithms of the past, as well as YOLOv7 and YOLOv8, respectively, by conducting comparison experiments. The SDE-YOLO algorithm has demonstrated its effectiveness on other datasets, and finally, we test the impact of its components through ablation experiments. The run shows that SDE-YOLO is accurate and can be implemented swiftly.However, during the implementation of blood cell testing, one may encounter the following challenges: 1. Quality and quantity of datasets: Blood cell image datasets may be affected by image quality, resolution, cueing conditions, and other factors. Obtaining large-scale and diverse blood cell datasets is challenging and crucial for model training and generalization; 2. Category imbalance: There may be an imbalance in the number of different types of cells in the blood cell images, resulting in a degradation of the performance of a few categories in the model. Strategies are needed to balance different categories of samples to improve the detector’s ability to recognize various blood cells; 3. Geometric variations: The sizes and shapes of blood cells may have more significant variations in the image. The model must adapt to cells with various geometries and can detect targets with different geometries; 4. Overlap and duplication: Blood cells may overlap and duplicate, etc., which makes target detection more urgent.Future research will explore the fusion of multiple data sources to improve detection accuracy. Utilizing multimodal information can allow for a more comprehensive detection of blood cells and enhance the ability of the model to capture targets at different scales.


Bothrops genus. In the present study, we compared four commercial methods for RNA purification and cDNA synthesis from venom  of four clinically relevant species of Peruvian Bothrops. Our results show that the TRIzol method presents the highest yield of RNA purified from venom (59 ± 11 ng/100 µL or 10 mg). The SuperScript First-Strand Synthesis System kit produced high amounts of cDNA (3.2 ± 1.2 ng cDNA/ng RNA), and the highest value was from combination with the Dynabeads mRNA DIRECT kit (4.8 ± 2.0 ng cDNA/ng RNA). The utility of cDNA was demonstrated with the amplification of six relevant toxins: thrombin-like enzymes, P-I and P-III metalloproteinases, acid and basic phospholipases A2, and disintegrins. To our knowledge, this is the first comparative study of RNA purification and cDNA synthesis methodologies from Bothrops genus venom.RNA purification and cDNA synthesis represents the starting point for molecular analyses of snake venom proteins-enzymes. Usually, the sacrifice of snakes is necessary for venom gland extraction to identify protein-coding transcripts; however, the venom can be used as a source of transcripts. Although there are methods for obtaining RNA from venom, no comparative analysis has been conducted in the  Bothrops snakes are the most clinically relevant group in Latin America [Bothrops venoms are composed of highly specialized proteins responsible for triggering coagulopathies, hemorrhage, myo/dermonecrosis, thromboinflammation, and pathologies [Pit vipers from  America ,2. Bothrhologies ,4. Thesehologies ,6. Therehologies . On the hologies  for infeSnake venom glands are an important source for molecular venom characterization, as they contain the venom-producing cells and associated structures; it is also a rich resource for the identification of bioactive molecules for drug development. Many comprehensive multi-omics studies have used the venom gland ,9. HowevPseudechis porphyriacus, Aspidelaps lubricus, and Naja kaouthia), and front-fanged vipers  [Crotalus durissus cumanensis has been performed [A range of adapted RNA isolation methods from venom have been proposed according to the logistics available in each laboratory, such as guanidinium thiocyanate (GITC)-based organic extraction ,15,16,17species) . Recentlerformed . Its strerformed . Nevertherformed ,13,22 thB. pictus, B. barnetti, and B. atrox venoms at the molecular level, using venom-derived RNA [Bothrops genus. This is the first work carried out on Bothrops venom, the genus of greatest medical importance in South America. Additionally, cDNA synthesis from some Bothrops atrox lyophilized venoms up to 30 years old was successful for transcript amplification.Considering all available data as reference, our laboratory has been successfully characterizing several proteins from Peruvian ived RNA ,25,26,27Neither lyophilized venom RNA nor fresh liquid venom RNA had detectable RIN (RNA integrity number) values, while RIN values of venom gland RNA were above 8.0 . HowevercDNA synthesis was possible using TRIzol method-RNA and the four cDNA synthesis kits, cDNA yields (ng cDNA per ng RNA) are shown in B. barnetti venom, and for this cDNA, variation in the intensity of the bands was appreciated; likewise, the P-I SVMP from the fresh liquid venom of B. brazili was not amplified. B. atrox venoms PCR amplification. For these samples, Qubit 2.0 Fluorometer could not detect any ribonucleic acid (<0.20 ng/µL) in most of the samples tested. Nonetheless, cDNA transcripts encoding D49-PLA2, K49-PLA2, and P-III SVMP  were amplified.The cDNA utility was demonstrated with the amplification of the coding region for six of the major clinically relevant toxins such as thrombin-like enzymes (TLE), P-I metalloproteinases (P-I SVMP), P-III metalloproteinase fragments (P-III SVMP), classical D49 phospholipases A2 (D49-PLA2), K49-PLA2 homologs (K49-PLA2), and disintegrins (DIS) . TLEs, PEscherichia coli cells TOP10′F. Culturing of cells transformed with TOPO-TA reactions resulted in an average efficiency of 4.2 × 106 CFU/μg plasmid DNA. The identity of nucleotide sequences was confirmed by Sanger sequencing. We obtained one sequence for each amplified product corroborating the amplification of one isoform per each amplicon. The sequences obtained were then aligned and analyzed for homology with other sequences in the NCBI database. Multiple alignment from D49-PLA2s . For this purpose, it is important to have efficient methods for mRNA or total RNA isolation. Several studies report the efficient isolation of RNA by different methods from snake venom, highlighting its suitability for the identification of protein-coding transcripts ,21,28. Tular RNA ,25,29,30Crotalus and Sistrurus venom. The method based on GITC (guanidine thiocyanate), as TRIzol reagent protocol, offered better results for both sample types. Similar results were obtained in Crotalus venoms, where the TRIzol protocol was widely superior to the other five methods, including those that are based on silica columns, glass microfibers, and magnetic beads, although it was not accompanied by statistical support [To our knowledge, there is only one study comparing different commercial kits of snake venom RNA isolation from fresh and lyophilized snake venoms . Like ou support . Other s support ,32,33,34Despite the higher performance of the TRIzol reagent protocol, physicochemical properties of adipose tissue, protein-rich source (as in the snake venom), and phenol can impair the RNA purity obtained. As expected, the High Pure RNA Isolation and GeneJET RNA Purification kits provided readings (A260/280 ratio: ~1.85) significantly higher than the TRIzol reagent protocol (A260/280 ratio: ~1.72). Some studies have applied diverse strategies to overcome the relatively low purity, such as optimizations in volume used, the use of a column-based secondary purification, or additional wash steps ,36,37. CNo statistically significant differences in RNA yield of lyophilized and fresh samples were observed between species A,B; howeQuantitation based on UV light absorption (as in the NanoDrop) can overestimate the RNA concentration due to certain interfering molecules (which have a similar absorption spectrum), such as phenolic compounds (present in the TRIzol reagent), DNA, and proteins (abundant in the venom) ,41. FluoAdditionally, the high quality of RNA is a prerequisite for the viability of various molecular biology techniques, such as cDNA library construction, real-time PCR, gene cloning, targeted mRNA- and high-throughput sequencing, northern hybridization analysis, etc. Although the three platforms used can measure RNA, only the Bioanalyzer can reliably report the RNA quality (degraded or intact) through an RNA Integrity Number (RIN). We assessed the RNA quality from lyophilized venom, liquid venom, and venom gland samples. The disparity between RIN values suggests that the venom samples provide considerably degraded RNA . The RINWe synthesized 8 to 35 ng of cDNA per 10 ng of RNA using different cDNA synthesis kits. Currier et al. (2012) reported yields close to 12 μg of cDNA from 10 ng of mRNA. This is due to the lower proportion of mRNA versus ribosomal RNA and transfer RNA. However, in any combination used of the extraction method and cDNA synthesis kit, the amounts of cDNA obtained were sufficient for the PCR amplification. Likewise, it was fascinating to be able to synthesize cDNA from venom samples that were lyophilized several years ago. The unexpected stability of the lyophilized venom-derived mRNA has been reported by other authors ,22.Bothrops snake venoms are composed predominantly of phospholipase A2, metalloproteinases, serine proteinases, disintegrins, L-amino acid oxidases, C-type lectin-like proteins, and hyaluronidases [Cataloging of toxins can be carried out at the transcript level (characterization molecular). Omics approaches revealed that onidases ,45,46,47onidases , Barnettonidases , Pictobionidases , Atroxlyonidases , Hyal-Baonidases , Bpic-LAonidases , Pictolyonidases , and BaPonidases .Bothrops pictus (“jergón de costa”), Bothrops barnetti (“sancarranca”), Bothrops brazili (“jergón-shushupe”), and Bothrops atrox (“jergón de selva”) snakes. Five snakes of each species were used for crude venom collection. All snakes were maintained in individual terrariums with temperature- and humidity-monitored environments at the Oswaldo Meneses Serpentarium of the Natural History Museum . Venom samples were collected twice from each specimen with an interval of two months between each milking event. Venom milking was performed three days after the feeding date. The venoms were placed in microtubes containing TRIzol or RNAlater, depending on the protocol to be followed. In addition, long-term storage (from 1980 to 2011) of lyophilized venoms (about 30 samples per species) was provided by the Laboratory of Molecular Biology from the Biological Sciences Faculty (UNMSM).Venom samples were obtained from nineteen adult pit-viper snakes of undetermined sex, B. pictus, three B. atrox, two B. brazili, and one B. barnetti) according to the protocol recommended by [From 2017 to 2022, venom glands were collected from nine snakes  was used to isolate poly(A) RNA. All RNA extractions were performed in triplicate, and samples were immediately used in cDNA synthesis.RNA was isolated from fresh liquid (100 µL) samples and previously stored lyophilized (10 mg) venom samples, as well as from fresh venom glands, using four commercially available kits according to the manufacturer’s instructions. Specifically, TRIzol reagent  was used to isolate total RNA from venom and gland venoms (only protocol applied to glands) with slight modifications . For comAll purified RNA samples were processed into single-stranded cDNA using oligo(dT) primers and different kits according to the manufacturer’s instructions: OneScript™ Plus cDNA Synthesis kit , Maxima First Strand cDNA Synthesis kit , RevertAid First Strand cDNA Synthesis kit , and SuperScript™ First-Strand Synthesis System .RNA and DNA concentrations were measured in the NanoDrop Lite Spectrophotometer (Thermo Fisher Scientific), Qubit 2.0 Fluorometer , and Agilent 2100 Bioanalyzer  according to manufacturer specifications. RNA purity was roughly estimated by measuring the absorbance ratios (260/280 and 260/230) using the NanoDrop Spectrophotometer. The RNA profile and integrity of all TRIzol-treated samples from venoms and venom glands were assessed using the Agilent 2100 Bioanalyzer.Bothrops thrombin-like enzymes, P-III and P-I metalloproteinases, disintegrins, phospholipases A2, and PLA2 homologs  agar plates overnight at 37 °C spiked with ampicillin (50 µg/mL) for selection. Positive colonies (n = 5) were analyzed by colony PCR amplification using M13 primers and the Platinum™ SuperFi II Green PCR Master Mix. Only 1 positive band (resulting in the estimated size) was excised and purified, using the GeneJET Gel Extraction kit, from the plate tested. Purified amplicons were sequenced by Macrogen, Inc.  on an ABI 3730 XL automated sequencer. All inserts were amplified and sequenced using M13 primers from 100 pg plasmid DNA.Adding 3′ A-overhangs was performed using the Platinum™ II Taq Hot-Start DNA Polymerase. cDNA amplified by Phusion™ DNA Polymerase according to manufacturer specifications. PCR products were inserted with the pCR2.1-TOPO vector following the TOPO-TA cloning strategy and transformed into One Shot™ https://www.ncbi.nlm.nih.gov/genbank/, accessed on 15 May 2023). Protein sequences were deduced using the Translate tool , and UniProtKB http://www.uniprot.org/, accessed on 30 May 2023) was used for comparison. The multiple sequence alignments (MSA) for all protein families were performed by MUSCLE algorithm [cDNA and sequences were compared with other nucleotide sequences deposited in GenBank  followed by Tukey’s (post-hoc) multiple comparisons tests. In cases where the RNA concentrations did not meet the assumptions of normality (Shapiro–Wilk test) and homogeneity of variance, so to assess the differences between the groups, non-parametric Kruskal–Wallis test and multiple comparisons with Dunn’s test were used. For all analytical proposes, statistical significance was declared at Bothrops venom-derived mRNA are enough to consider the venom as an alternative source to partially explore the glandular transcriptome. This avoids the need to sacrifice the animals, especially with species in a vulnerable state such as Bothrops pictus and Bothrops barnetti. On the other hand, the long-lasting stability of RNA in lyophilized venom is surprising, which may even reduce the need for venom collection events. Finally, we hope that these results will facilitate work based on snake venom RNA to determine the composition of a little-studied snake through the identification and molecular characterization of unknown/known toxins.In conclusion, we confirmed that the stability, purity, and quantity of the Peruvian


Transfer RNA-derived small RNAs (tsRNAs) tRF-LeuCAG-002 (ts3011a RNA) is a novel class of non-coding RNAs biomarker for pancreatic cancer (PC). Reverse transcription polymerase chain reaction (RT-qPCR) has been unfit for community hospitals that are short of specialized equipment or laboratory setups. It has not been reported whether isothermal technology can be used for detection, because the tsRNAs have rich modifications and secondary structures compared with other non-coding RNAs. Herein, we have employed a catalytic hairpin assembly (CHA) circuit and clustered regularly interspaced short palindromic repeats (CRISPR) to develop an isothermal and target-initiated amplification method for detecting ts3011a RNA. In the proposed assay, the presence of target tsRNA triggers the CHA circuit that transforms new DNA duplexes to activate collateral cleavage activity of CRISPR-associated proteins (CRISPR-Cas) 12a, achieving cascade signal amplification. This method showed a low detection limit of 88 aM at 37 °C within 2 h. Moreover, it was demonstrated for the first time that, this method is less likely to produce aerosol contamination than RT-qPCR by simulating aerosol leakage experiments. This method has good consistency with RT-qPCR in the detection of serum samples and showed great potential for PC-specific tsRNAs point-of-care testing (POCT). Catalytic hairpin assembly (CHA) circuit and clustered regularly interspaced short palindromic repeats (CRISPR) (CHA-CRISPR) is a simple, isothermal, ultrasensitive, and target-initiated amplification method to detect ts3011a RNA, a novel class of non-coding RNAs biomarkers for pancreatic cancer (PC).Based on CHA-CRISPR method, this assay achieved simple, isothermal, ultrasensitive and target-initiated amplification of ts3011a RNA with a low detection limit of 88 aM in 2 h at 37 °C.Moreover, it was demonstrated for the first time that, this method is less likely to produce aerosol contamination than Reverse transcription polymerase chain reaction (RT-qPCR) by simulating aerosol leakage experiments. This method has good consistency with RT-qPCR in detection of serum samples and showed great potential for PC specific tsRNAs point-of-care testing (POCT). Its diagnostic performance was found to be similar to that of RT-qPCR, and it is more suitable in community hospitals without precision instruments and PCR testing standard environments.Pancreatic cancer (PC) is the cancer with the lowest 5-year survival rate . BecauseAt present, reverse transcription PCR (RT-qPCR) is a commonly used non-coding RNA detection technology . This teHerein, we illustrated a simple method by coupling CRISPR/Cas12a and CHA to realize the detection of ts3011a RNA. In our experiment, two hairpins from the CHA circuit were triggered by tsRNAs and transformed into new DNA duplexes. The new DNA duplexes consisted of protospacer adjacent motif (PAM) and protospacer sequence that can be recognized by Cas12a/CRISPR RNA (crRNA) complexes. Therefore, the collateral cleavage activity of CRISPR/Cas12a was initiated, and cascade signal amplification was realized through the cleavage events of fluorescent ssDNA reporter molecules . Based o2, and glycerol were purchased from Macklin Biochemical Co., Ltd., Shanghai, China. Tris hydrochloric acid (TrisHCl) was purchased from Dalian Meilun Biological Technology Co., Ltd., Suzhou, China. Dithiothreitol (DTT) was purchased from SaiGuo Biotech Co., Ltd., Guangzhou, China. Lachnospiraceae bacterium (LbCas12a) was purchased from GenScript Biotech Co., Ltd., Nanjing, China, and the protein was stored at −80 °C until later use. Agarose powder, ×10 Trizma base boric acid ethylene diamine tetra acetic acid (TBE) buffer, and ×6 Loading buffer were obtained from Sangon Biotech, Shanghai, China. All oligonucleotides  containing 400 nM CRISPR-Cas12a, 200 nM crRNA, and 1 μM FAM-TTATT-BHQ1 reporter molecules (FQ), making a final volume of 10 μL. The fluorescence emission measurements were recorded at 518 nm in the thermal cycler at 37 °C for 1 h. The stem length, CHA reaction time, temperature, and concentration of crRNA and Cas enzyme in 10 μL volume were optimized.Subsequently, 1 μL of the above CHA reaction solution was added into the cleavage buffer  and 0.5 μL of primer script enzyme, which were mixed with 0.5 μL primer, 2 μL of total RNA, and 5 μL of DEPC treated water. The reactions were then incubated at 42 °C for 15 min, 85 °C for 5 s, and cDNA was stored at −20 °C until further use.PANC-1 and hTERT-HPNE were grown in DMEM supplemented with 10% FBS, 1%penicillin–streptomycin (100 U/mL penicillin and 100 μg/mL streptomycin) and cultured at 37 °C in a humidified incubator containing 95% air and 5% CO2–ΔΔ Ct method . The relative expression of cell samples was evaluated by referring to the expression of the mir39  using the t method .CHA, CHA-CRISPR, and RT-qPCR were performed with 1 nM ts3011a, respectively, to simulate the possible release of aerosol after CHA, CHA-CRISPR, and RT-qPCR reaction. The closed hood was opened to ventilate for 0.5 h. The air sampler with 2 mL of sampling liquid (CHA or RT-qPCR reaction buffer) was put into the closed hood. The 8-strip tubes with CHA or CHA-CRISPR or RT-qPCR products were then put into the closed hood and uncapped. The closed-hood lead was closed and left for 5 min. The sampler was then turned on for 15 min with an air velocity of 200 L/min . CHA or All the pancreatic cancer serum samples and healthy serum samples were collected from consented patients at the Nanjing Drum Tower Hospital  from January to October 2022. Approval was obtained from our hospital ethics committee. 200 μL of serum samples were centrifuged at 1700 g for 20 min at 4 °C. The TRIzol RNA isolation reagents were used to extract the total RNA. 1 μL of total RNA was added to the CHA-CRISPR system to validate its potential feasibility.In the first step of amplification, CHA cycles contained the following three main elements: two hairpins (H1 and H2) and target ts3011a RNAs as initiators . Both de, with a limit of detection (LOD) of 88 aM  (The standard ts3011a RNA samples were diluted with DEPC and quantified by RT-qPCR . Fluoresn curve) . The senn curve) .The detection device is shown in , the fluorescence intensity of the CHA-CRISPR assay demonstrated a good agreement with the Ct values of RT-qPCR in the detection of serum samples (R = 0.9367).The expression levels of ts3011a RNA in PANC-1 and hTERT-HPNE were analyzed with CHA-CRISPR and RT-qPCR . The quaIn summary, based on CHA and CRISPR-Cas12a, we have first developed a target-initiated amplification detection of pancreatic-cancer-specific tsRNAs. Under simulated experimental conditions, aerosol pollution was not produced by products of CHA-CRISPR with 1 nM tsRNA. The detection was achieved at 37 °C within 2 h, with a detection limit of 88 aM. Its diagnostic performance was found to be similar to that of RT-qPCR, and it is more suitable in community hospitals without precision instruments and PCR testing standard environments.


The maternal-fetal interface is defined as the interface between maternal tissue and sections of the fetus in close contact. RNA methylation modifications are the most frequent kind of RNA alterations. It is effective throughout both normal and pathological implantation and placentation during pregnancy. By influencing early embryo development, embryo implantation, endometrium receptivity, immune microenvironment, as well as some implantation and placentation-related disorders like miscarriage and preeclampsia, it is essential for the establishment of the maternal-fetal interface. Our review focuses on the role of dynamic RNA methylation at the maternal-fetal interface, which has received little attention thus far. It has given the mechanistic underpinnings for both normal and abnormal implantation and placentation and could eventually provide an entirely novel approach to treating related complications. It is primarily made up of the placenta and maternal decidua . At the RNAs play a crucial role in controlling biological functions. They have emerged as key regulators of RNA function and metabolism . In eukaDespite significant breakthroughs in the study of pregnancy, it is still exceedingly challenging to comprehend how RNA methylation works in human implantation. Our review covers the most recent RNA methylation studies and discusses the several forms of RNA methylation in humans, as well as the role of regulatory factors. Furthermore, we discuss the significance of dynamic RNA methylation in implantation and placental development. In addition, our review highlights the previously unknown involvement of RNA methylation in the formation of the maternal-fetal interface. In brief, this paper aims to review recent findings on developmental changes at the maternal-fetal interface associated with RNA methylation and its implications for implantation and placenta-associated diseases. This review is crucial because it provides a comprehensive overview of the current state of research on RNA methylation at the maternal-fetal interface, points to future research directions, and offers the prospect of a new strategy for the future treatment of related disorders Figure 12In both prokaryotes and eukaryotes, over 100 different types of post-transcriptional chemical modifications of RNA have been discovered in messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and small RNA . RNA met2.1As early as 1977, Karen Beemon et al. found residues of m6A in Rous sarcoma virus RNA . Since tMETTL3 is the first methyltransferase discovered and it is one of the most fundamentally important “writers” . METTL14A growing body of evidence suggests that m6A is involved in RNA structural stability, translation, and degradation at the mRNA level –103. It Similar to mRNA, m6A-related modifications have also been found to be involved in non-coding RNA synthesis and metabolism , 105. MEIn addition, the RNA writers, erasers, or readers mentioned above are thought to be involved in DNA methylation processes. FTO and ALKBH5, two m6A erasers, may also be related to DNA methylation. Evidence suggests that ALKBH5 demethylates DNA’s 3-methylcytosine and has limited DNA repair activity . FTO, ho2.2Aside from m6A, m5C is another common modification found in RNAs. During m5C modification, “writers”, “erasers”, and “readers” may act through similar mechanisms compared to m6A . InteresAs an important epigenetic modifier in RNA, m5C plays a central role in governing transcript abundance. NOP2/NSUN1, a m5C RNA methyltransferase, inhibits transcription and promotes viral latency by competing with TAT for TAR binding and methylation . m5C RNAAdditionally, these RNA m5C methylation-related writers, readers, and erasers might also be involved in DNA methylation. For example, DNA methylation is carried out by members of the DNA methyltransferase (DNMT) family . Howeverm5C alterations are also involved in the maturation and structural changes of non-coding RNAs. Based on miCLIP-Seq data, Han Liao et al. discovered that NOP2/NSUN1 catalyzes the deposition of m5C on 28S rRNA and regulates the processing of pre-rRNA . The NSUm5C modification in RNA is considered to be reversible. Demethylation is carried out by ten-eleven translocation (TET) enzymes. Although the catalytic activity of TET proteins was thought to be limited to DNA for several years, current research has begun to uncover their potential significance in altering RNA bases. TET2 has been shown to promote the conversion of 5-methylcytosine on tRNA to hm5C as well as to regulate the processing and stabilization of different classes of tRNA fragments . This maFurthermore, readers are responsible for reading the information on RNA m5C modification. ALYREF was characterized as a nucleoplasmic mRNA m5C reader . FurtherOverall, the functional roles of m5C modification involve pre-RNA processing and splicing, mRNA stability, and other processes –135. How2.3As a novel form of RNA modification, m1A has received considerable attention in recent years. m1A is mainly present in the T-loop structure of tRNA and is introduced by the TRMT6/TRMT61A complex. It appears less commonly in mRNA. Modi Safra et al. discovered a m1A site catalyzed by TRMT10C in mitochondrial ND5 mRNA with high tissue-specific and tight developmental control of methylation levels .Erasers act as m1A demethylases, removing the methyl group from m1A and making it functionally reversible. ALKBH1 and ALKBH3 are two common demethylases , 54.Using a photocrosslinking approach, Seo KW et al. discovered that YTHDF1/2 is the reader of m1A modification on RNA and that the binding of YTHDF2 to m1A promotes the degradation of m1A RNA .Nevertheless, little is known about m1A development pathways, and their function in RNA remains to be elucidated.Notably, over the last few years, more than 100 alterations inside RNAs have been discovered, including m1G, m6G, and others. Further inquiry is required to determine their role in molecular function.3During the establishment of pregnancy, early embryonic development and implantation are important components . Normal During the first trimester of human pregnancy, an interface is formed between the maternal decidua and the placenta of the fetus, which is also known as the maternal-fetal interface . It is aRNA methylation may have a major role in the development of the maternal-fetal interaction. In this section, we summarized the function of RNA methylation in this section, focusing on four aspects in particular: early embryo development, embryo implantation, endometrial receptivity, and immunological microenvironment Figure 23.1Semen RNA epigenetic modifications have potential effects on early embryogenesis and paternal epigenetic inheritance . METTL3-Maternal RNA degradation, comprising maternal (M) and zygotic (Z) decay pathways, is also essential for oocyte maturation and embryogenesis. YAP and TUT4/7 may regulate maternal mRNA Z-decay during the human maternal-to-zygote transition (MZT) and play an important role in the early development of mouse and human embryos , 145. RNAfter implantation of the embryo in the uterus, RNA post-transcriptional modifications also regulate germ layer growth. The rRNA m6A methyltransferase METTL5 is engaged in the developmental process . METTL5 Other methylation changes, such as m5C methylation, have been demonstrated to have similar consequences to m6A methylation in early embryonic development.The majority of m5C sites in protein-coding RNAs are catalyzed by NSUN6. Evidence suggests that embryonic development in mice is largely unaffected by the complete loss of NSUN6. In vitro cellular investigations and in vivo animal assays were carried out for verification. Reduced levels of NSUN6-targeted mRNAs are demonstrated in embryonic-like bodies lacking NSUN6 . HoweverBased on m7G methylated tRNA immunoprecipitation sequencing (MeRIP-Seq) and tRNA reduction and cleavage sequencing (TRAC-Seq), increased ribosome occupancy of the corresponding codon in METTL1 knockout mESCs was observed. This suggests an important role for the mammalian m7G tRNA methylome in ESCs .However, the specific effects of RNA methylation on different phases of embryonic development, whether in m5C modification or m7G modification, have yet to be studied. Their different roles and mechanisms in sperm development, egg maturation, MZT, and embryo layer differentiation after embryo implantation remain the focus of future research.Overall, RNA methylation is involved in spermatogenesis, egg maturation and MZT, and embryo layer differentiation in early embryo development Figure 33.2Embryo implantation is one of the important aspects of maternal-fetal interface formation. The embryo implantation requires the degradation of the extracellular matrix (ECM) and the establishment of uterine receptivity . EmbryonTrophoblast invasion can be regulated by dynamic methylation modifications of mRNA. By modulating MYLK expression, METTL3 enhances trophoblast cell invasion . FurtherThese studies demonstrated that trophoblast cell invasion is mediated by revisable RNA methylation. Additionally, RNA methylation alterations encourage trophoblast invasion, while demethylation modifications prevent it. Nonetheless, a few considerations challenge the hypothesis. Evidence shows that hypoxia treatment can significantly upregulate ALKBH5 expression and induced ALKBH5 translocation from the nucleus to the cytoplasm, followed by ALKBH5 demethylation of m6A modification on SMAD1/5 mRNA, which promoted its expression and activated the TGF-β signaling pathway to induce MMP9 expression, thereby increasing trophoblast migration and invasion . ALKBH5 Non-coding RNA methylation also affects trophoblast cell proliferation and invasion. YTHDC1, for example, degrades circMPP1 via mA6 alteration, enhancing placental villi function . The m6AMounting evidence implies that B(a)P/benzo(a) pyrene-7,8-dihydro diol-9,10-epoxide (BPDE) inhibits the proliferation of human chorionic trophoblast cells and is related to RNA m6A modification. Mengyuan Dai e al. discovered a novel lncRNA, lnc-HZ09, and its putative function in recurrent miscarriage villous tissue and human trophoblast cells exposed to BPDE. Cells over-expressing METTL3 have higher levels of m6A RNA modification on lnc-HZ09 and result in reduced stability of lnc-HZ09. lnc-HZ09 regulates trophoblast migration and invasion through the PLD1/RAC1/CDC42 pathway, and lnc-HZ09-silenced cells exhibit significantly greater migration and invasion . In addiThese findings highlight the significance of RNA methylation in controlling the biological behaviors of trophoblast cells. However, whether RNA methylation changes on trophoblast cells promote or impede invasion is debatable. Furthermore, the majority of the aforementioned pathways were discovered during the aberrant trophoblast implantation experiments. It is unclear whether these mechanisms have a role in normal trophoblast invasion and maternal-fetal interface establishment during early pregnancy.3.3Decidualization refers to the functional and morphological changes occurring in the endometrium. The main changes that occur during metaplasia are changes in the endometrial stromal cells, increased uterine gland secretion, increased uterine natural killer cells, and vascular remodeling. Accumulating experimental and clinical evidence demonstrated that defective decidualization or blocked decidualization may lead to abnormal maternal-fetal interface .f/f mice, the m6A-modified PGR mRNA in 5 ‘- UTR is recognized by YTHDF1, thereby promoting PGR protein translation. However, in Mettl3d/d mice, the m6A modification in PGR mRNA is missing, and the PGR protein cannot be effectively translated. The low level of PGR protein ultimately leads to implantation and decidualization failure. The METTL3-PGR axis may be conserved between mice and humans  mRNA is a direct target of METTL3-mediated m6A modifications. In METTL3d humans . Of noted humans .Additionally, a major hallmark of decidualization is the mesenchymal-epithelial transformation (MET) of endometrial mesenchymal cells . The traCollectively, the above findings imply that m6A methylation is linked to the decidualization of human endometrial stromal cells and may potentially be linked to MET. More research, however, was required to confirm the rationale behind these findings.3.4The immune microenvironment at the maternal-fetal interface is essential for maintaining normal embryonic development. Decidual natural killer (dNK) cells are the most prevalent in the maternal-fetal interface, followed by decidual macrophages (dM Φ) and then decidual T cells .There is evidence that m6A methylation and NK cell effector function are linked to METTL3 expression levels. METTL3-mediated m6A methylation protects the signaling pathway downstream of IL-15R, thereby regulating the responsiveness of NK cells to IL-15 . NPM1 isRNA methylation can affect macrophage polarization. The m1A “reader” YTHDF3 may be involved in regulating macrophage polarization that promotes aortic inflammation . AccordiRNA methylation also affects TCR signaling and determines T cell activation and survival. WTAP and m6A methyltransferase functions are required for thymocyte differentiation and control of activation-induced peripheral T-cell death . By targBased on the above findings, we propose that RNA methylation may have important effects on the immune microenvironment. Changes in NK cell function, macrophage polarization, and T cell activation are all linked to RNA methylation. Although the majority of the current studies on the impact of RNA methylation in the immune microenvironment focus on the tumor immune microenvironment, these studies can provide some insight into the role of RNA methylation in the maternal-fetal immune microenvironment to a certain extent.Taken together, due to its ability to promote embryo development, regulate embryo implantation, extend uterine receptivity, and provide a stable immune microenvironment, RNA methylation is important in the formation of the maternal-fetal interface.4Common implantation and placentation-related diseases like miscarriage, preeclampsia, and preterm birth have been proposed to be associated with aberrant RNA methylation in the formation of the maternal-fetal interface. Abnormalities in RNA methylation may impact the establishment of the maternal-fetal contact, ultimately leading to illness.4.1Miscarriage, especially recurrent miscarriage, may be multifactorial. Problems in any part of the maternal-fetal interface formation can lead to miscarriage. In this process, RNA methylation probably plays an important role. It was discovered that m6A levels in the uterus rise throughout pregnancy. The mRNA levels of the methyltransferases METTL16 and WTAP were lower in the endometrium of patients with miscarriage and infertility, whereas the mRNA levels of ALKBH5 and IGF2BP2 were higher .In the process of early embryonic development, abnormal methylation in epigenetic factors of both the father and mother can affect embryonic development . AbnormaMoreover, FTO deficiency in the chorionic villi affects immunological tolerance, leading to aberrant methylation and oxidative stress and, eventually, spontaneous abortion. Compared with normal pregnant women, the levels of FTO-binding HLA-G, VEGFR, and MMP9 mRNA in spontaneous abortion patients were significantly reduced, and the concentration of FTO-binding MMP7 was significantly increased. This is due to oxidative stress and abnormal m6A accumulation at the maternal-fetal interface .These all prove that abnormal methylation of RNA might be one of the reasons for spontaneous abortion.4.2Preeclampsia is classified as early-onset or late-onset based on when it appears, which is usually at 34 weeks of gestation. Early-onset preeclampsia often results in a high perinatal mortality rate, significant placental dysfunction, and poor maternal and fetal prognosis. Our review focuses on early-onset preeclampsia because it is the most severe and is a major cause of maternal and fetal death. It is strongly associated with RNA methylation. Trophoblast dysfunction is thought to be an important cause of early-onset preeclampsia, and RNA methylation has a major impact on trophoblast function in two ways, according to the existing studies.On the one hand, RNA methylation influences trophoblast invasion in patients with preeclampsia. We discovered that DNMT3B and TET3 were significantly elevated in PE samples compared to the normal group by analyzing the mRNA expression levels of m5C RNA methylation regulators. Surprisingly, these two methylation regulators have a negative correlation. A comprehensive analysis of UMI-MeRIP Seq data revealed a decrease in the overall peak value of m5C methylation in placental tissue from patients with preeclampsia. However, compared with the normal blood pressure group, the m5C peak value before the coding sequence was stopped in the preeclampsia group significantly increased . In addiRNA methylation, on the other hand, induces trophoblast apoptosis. YTHDF2 can target TMBIM6 mRNA via METTL3, resulting in ROS production and trophoblast apoptosis, exacerbating endoplasmic reticulum stress in preeclampsia . SimilarIn short, in patients with preeclampsia, a decrease in the overall peak of m5C methylation or an increase in m6A modification may lead to trophoblast invasion dysfunction. In addition, increased RNA methylation can promote trophoblast cell apoptosis, further exacerbating preeclampsia. Interestingly, the methylation trends of m5C and m6A in preeclampsia appear to be different. In addition, in the process of m5c methylation regulation, DNMT3B and TET3 seem to have opposite effects, but they are highly expressed in preeclampsia patients. In this process, the molecular mechanism of regulators is still unknown.4.3Preterm birth is defined by the World Health Organization (WHO) as delivery before 37 completed weeks of gestation. Between 25% and 40% of all preterm births are associated with intrauterine infection, including viral and bacterial infections .m6A factors can regulate the virus life cycle. By adding m6A to the genome RNA and mRNA of a virus, changing gene expression, or the fate of corresponding RNA involved in virus or replication, the effect of m6A modification can directly or indirectly regulate virus replication. The strength and duration of innate immune response activation can also be determined by m6A methylation . m6A-modBacterial infections, in addition to viral infections, can result in premature delivery. The P/Q/N rich domain in m6A reader YTHDF1 interacts with the DEAD domain in host factor DDX60, thereby regulating the immune response to bacterial infection by recognizing the target Traf6 transcript .Moreover, in patients with spontaneous preterm delivery, abnormalities in immune function at the maternal-fetal interface may be a major cause of their preterm delivery . The assCurrently, research in the field of RNA modification in preterm birth is mainly focused on infection. The m6A methylation is the most common type of RNA alteration involved. However, whether or not RNA methylation alteration is one of the major biochemical pathways causing premature delivery is still being contested.Above all, an imbalance in RNA methylation is critical in the development of implantation and placental disorders. Interestingly, the newly discovered programmed cell death pathways, including autophagy, ferroptosis, and pyroptosis are also associated with implantation and placenta disorders. Recently, autophagy has shown to have a role in miscarriage. In women who have had an early abortion, dysregulated autophagy promotes oxidative stress and aberrant production of ABC transporter proteins . Many inThere is currently a growing body of evidence linking RNA methylation to programmed cell death. m6A methylation is associated with autophagy and plays a role in apoptosis induced diseases. Silencing METTL3 enhances autophagic flux and inhibits apoptosis in H/R-treated cardiomyocytes . MoreoveHowever, it is unclear whether RNA methylation affects implantation and placenta-associated diseases by influencing programmed cell death. As a result, more evidence is needed to demonstrate whether RNA methylation-mediated programmed death is directly involved in these diseases.5This is the first description of the dynamic RNA methylation function during the maternal-fetal interface development. We performed in-depth investigations of RNA methylation for this review. The three most frequent RNA modifications, m6A, m1A, and m5C, are all essential for the biogenesis, metabolism, structural stability, and function of RNA.RNA methylation may have a considerable impact on the creation of the maternal-fetal interface by influencing early embryonic development, embryo implantation, endometrial receptivity, and the immunological microenvironment. Furthermore, abnormal RNA methylation during the formation of the maternal-fetal interface is thought to be associated to implantation and placenta-related disorders such as miscarriage, preeclampsia, and preterm birth.Given the significance of RNA methylation in the formation of the maternal-fetal interface during pregnancy, further investigation is warranted to elucidate the underlying mechanism. The potential various mechanisms of RNA methylation and demethylation during the development of embryos before and after implantation still require further research. Furthermore, there are differing views on whether trophoblast cell RNA methylation changes encourage or impede invasion. The majority of RNA methylation mechanisms in trophoblast cells have been discovered in studies of abnormal trophoblast implantation, but it is still unknown whether these mechanisms contribute to normal trophoblast invasion and the establishment of the maternal-fetal interface in the early stages of pregnancy. There is also no direct evidence for the effects of RNA methylation on the immunological milieu at the maternal-fetal interface. It is noteworthy that whereas m6A methylation has been linked to EMT and metaphase, the underlying regulatory mechanism is yet unknown.Additionally, it is unclear whether RNA methylation affects implantation and placenta-associated diseases by influencing programmed cell death. From a broader perspective, the role of RNA methylation and programmed cell death in implantation- and placentation-related illnesses is another significant area of challenge and promise for future research.Together, our study highlights the importance of dynamic RNA methylation at the maternal-fetal interface. It has given the molecular underpinnings for both normal and abnormal implantation and placentation and could eventually offer a fresh approach to treating related disorders.HY and YJ designed the work and made major revisions to the article. SW and HX wrote the manuscript. YS, XJ, and YM drafted the manuscript and revised the article. All authors contributed to the article and approved the submitted version.


We leveraged the s2m RNA from the SARS-CoV-1 3’UTR as a model for our structural studies as it adopts a well-defined structure with several uridines, two of which are unpaired and thus highly probable targets for nsp15 cleavage. We found that SARS-CoV-2 nsp15 specifically cleaves s2m at the unpaired uridine within the GNRNA pentaloop of the RNA. Further investigation revealed that the position of uridine within the pentaloop also impacted nsp15 cleavage efficiency suggesting that positioning within the pentaloop is necessary for optimal presentation of the scissile uridine and alignment within the nsp15 catalytic pocket. Our findings indicate that RNA secondary structure is an important determinant of nsp15 cleavage and provides insight into the molecular mechanisms of RNA recognition by nsp15.SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, has had an enduring impact on global public health. However, SARS-CoV-2 is only one of multiple pathogenic human coronaviruses (CoVs) to have emerged since the turn of the century. CoVs encode for several nonstructural proteins (nsps) that are essential for viral replication and pathogenesis. Among them is nsp15, a uridine-specific viral endonuclease that is important in evading the host immune response and promoting viral replication. Despite the established endonuclease function of nsp15, little is known about other determinants of its cleavage specificity. In this study we investigate the role of RNA secondary structure in SARS-CoV-2 nsp15 endonuclease activity. Using a series of  Nidovirales order. CoVs infect a wide range of host species and are associated with mild to severe disease in livestock and humans . Fo. Fo35]. in vitro assays performed here and in other studies provide a tractable system for dissecting the molecular mechanism of nsp15-RNA interactions and cleavage determinants, a recent computational model suggests that nsp15 is part of a larger replication-transcription complex (RTC) which contains other viral proteins and host factors. How nsp15 protein-protein interactions and RNA interactions with other replicase proteins  impacts nsp15 cleavage of RNA remains unsolved. Indeed, recent models of the RTC suggest the hexameric structure of nsp15 provides the central structure and arrangement of a hexameric RTC [2+ and the structural conformation of nsp15 is highly dependent on the pH of the environment [in vitro.Collectively, our data suggests that RNA secondary structure plays two distinct roles in determining nsp15 cleavage specificity. First, thermodynamically stable structures likely sterically hinder nsp15-RNA interactions, thus preventing engagement and cleavage of some RNA sequences. Second, susceptible bases are presented in a structural context which facilitates optimal positioning of the scissile nucleotide in the catalytic pocket of nsp15 . However, several questions remain unanswered, most notably, what are the biologically relevant targets  of nsp15 cleavage during CoV infection and are viral targets conserved across different CoVs? Two studies to date have identified cleavage sites within the positive-sense genomic RNA and 3’ polyU tails of the negative-sense RNA of MHV , 22. Deseric RTC . In thisironment . Given tOverall, our findings suggest a role for RNA secondary structure in regulation of nsp15 cleavage. Identification of bona fide nsp15 cleavage sites in SARS-CoV-2 RNA during replication and examination of how perturbation of these sites impacts the outcome of replication and pathogenesis will provide key insights into the broader roles for nsp15 endoU activity during infection . Furthermore, defining key structural features of RNAs which are resistant or susceptible to nsp15 cleavage may provide avenues for development of antiviral therapeutics.S1 Figmut4 and Δs2mmut7 RNAs were denatured and folded as outlined in the materials and methods prior to being electrophoresed on a native gel. Denatured RNA was heated in 2x RNA Loading Dye (NEB) at 70°C for 10 minutes. Folded RNAs show the presence of conformers as indicated by arrows.Δs2m(TIF)Click here for additional data file.S2 Figmut7 RNA was incubated with wt nsp15, catalytically inactive nsp15 (K290A), or denatured wt or K290A nsp15. For denatured samples, nsp15 was denatured by heating recombinant protein for 5 minutes at 95°C prior to the addition of folded RNA. Endonuclease reactions were incubated for 15 minutes then terminated with the addition of EDTA to a final concentration of 20mM and flash frozen (see ∆s2mozen see .(TIF)Click here for additional data file.S3 FigA. Minimum free energy structures of RNAs 1, 2, and 3 as predicted by RNAfold. B. Published structures of RNA 1, 2, and 3 as resolved by SHAPE-MaP . Nucleotides highlighted in blue match with SHAPE data while bases highlighted in purple and red do not. Purple nucleotides are paired with a different nucleotide whereas red nucleotides are unpaired in SHAPE structures.(TIF)Click here for additional data file.S4 FigA. Minimum free energy structures of SARS-CoV-1 and SARS-CoV-2 s2m. The U25  and U30 (bulge) nucleotides are highlighted in blue. Two nucleotide changes between the RNAs in the stem (highlighted in red) change the MFE structure. B. Thermodynamic stability, frequency of MFE, and ensemble diversity of s2m from each virus. Increased ∆G and ensemble diversity in SARS-CoV-2 s2m indicate that it does not maintain the highly conserved structure of SARS-CoV-1 s2m. C. Alignment of s2m from both viruses with relevant polymorphisms in SARS-CoV-2 s2m indicated by red arrows.(TIF)Click here for additional data file.S5 FigB. Fold change in each cleavage product relative to the amount present at the one-minute time point. Cleavage products above the dashed lines indicate accumulating products while those below are diminishing over the course of the reaction. Data represents three independent experiments.Endonuclease assays of full-length s2m mutant RNAs as shown in (TIF)Click here for additional data file.S6 FigA. Endonuclease assays of additional ∆s2m mutant RNAs. Reactions were carried out at a nsp15 hexamer:RNA ratio of 1:6. RNAs were run on a 22.5% TBE-Urea Gel. B. Thermodynamic stability, frequency of MFE, and ensemble diversity of ∆s2m mutant RNAs indicated as predicted by RNAfold. C. Percent of full-length RNA remaining as measured by densitometry. Percentage of uncut RNA was calculated by normalizing to a denatured nsp15 control (indicated by (-) in the far-right lane). RNAs with uridines in the second position of the pentaloop (∆s2mmut6/7) were consistently cleaved more rapidly than ∆s2mmut2 in which the uridine is in the fourth position. P-values were calculated using one-way ANOVA with multiple comparisons analysis of the AUC. Data represents three independent experiments.(TIF)Click here for additional data file.S7 FigA. Minimum free energy structures of s2m-like and control RNAs. Representative images from endonuclease assays are indicated below each RNA. Reactions were carried out at a nsp15 hexamer:RNA ratio of 1:120. RNAs were run on a 22.5% TBE-Urea Gel. B. Percent of full-length RNA remaining as measured by densitometry. Percentage of uncut RNA was calculated by normalizing to a denatured nsp15 control (indicated by (-) in the far-right lane). AUC was calculated for each RNA, and one-way ANOVA with multiple comparisons was performed on the AUC. Data represents three independent experiments.(TIF)Click here for additional data file.S8 Figmut2, ∆s2mmut4, ∆s2mmut5, RNAs (from Time course of ∆s2mNAs from  run on a(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S1 Raw images(PDF)Click here for additional data file.