Two-subunit enzymes involved in eukaryotic post-transcriptional tRNA modification

A comprehensive analysis of m6A/m7G/m5C/m1A-related gene expression and immune infiltration in liver ischemia-reperfusion injury by integrating bioinformatics and machine learning algorithms

BACKGROUND

Liver ischemia-reperfusion injury (LIRI) is closely associated with immune infiltration, which commonly occurs after liver surgery, especially liver transplantation. Therefore, it is crucial to identify the genes responsible for LIRI and develop effective therapeutic strategies that target immune response. Methylation modifications in mRNA play various crucial roles in different diseases. This study aimed to identify potential methylation-related markers in patients with LIRI and evaluate the corresponding immune infiltration.

METHODS

Two Gene Expression Omnibus datasets containing human liver transplantation data (GSE12720 and GSE151648) were downloaded for integrated analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted to investigate the functional enrichment of differentially expressed genes (DEGs). Differentially expressed methylation-related genes (DEMRGs) were identified by overlapping DEG sets and 65 genes related to N6-methyladenosine (m6A), 7-methylguanine (m7G), 5-methylcytosine (m5C), and N1-methyladenosine (m1A). To evaluate the relationship between DEMRGs, a protein-protein interaction (PPI) network was utilized. The core DEMRGs were screened using three machine learning algorithms: least absolute shrinkage and selection operator, random forest, and support vector machine-recursive feature elimination. After verifying the diagnostic efficacy using the receiver operating characteristic curve, we validated the expression of the core DEMRGs in clinical samples and performed relative cell biology experiments. Additionally, the immune status of LIRI was comprehensively assessed using the single sample gene set enrichment analysis algorithm. The upstream microRNA and transcription factors of the core DEMRGs were also predicted.

RESULTS

In total, 2165 upregulated and 3191 downregulated DEGs were identified, mainly enriched in LIRI-related pathways. The intersection of DEGs and methylation-related genes yielded 28 DEMRGs, showing high interaction in the PPI network. Additionally, the core DEMRGs YTHDC1, METTL3, WTAP, and NUDT3 demonstrated satisfactory diagnostic efficacy and significant differential expression and corresponding function based on cell biology experiments. Furthermore, immune infiltration analyses indicated that several immune cells correlated with all core DEMRGs in the LIRI process to varying extents.

CONCLUSIONS

We identified core DEMRGs (YTHDC1, METTL3, WTAP, and NUDT3) associated with immune infiltration in LIRI through bioinformatics and validated them experimentally. This study may provide potential methylation-related gene targets for LIRI immunotherapy.

Identification of Amino Acids in Trm734 Required for 2'-O-Methylation of the tRNAPhe Wobble Residue

All organisms methylate their nucleic acids, and this methylation is critical for proper gene expression at both the transcriptional and translational levels. For proper translation in eukaryotes, 2'-O-methylation of C32 (Cm32) and G34 (Gm34) in the anticodon loop of tRNAPhe is critical, with defects in these modifications associated with human disease. In yeast, Cm32 is formed by an enzyme that consists of the methyltransferase Trm7 in complex with the auxiliary protein Trm732, and Gm34 is formed by an enzyme that consists of Trm7 in complex with Trm734. The role of Trm732 and Trm734 in tRNA modification is not fully understood, although previous studies have suggested that Trm734 is important for tRNA binding. In this report, we generated Trm734 variants and tested their ability to work with Trm7 to modify tRNAPhe. Using this approach, we identified several regions of amino acids that are important for Trm734 activity and/or stability. Based on the previously determined Trm7-Trm734 crystal structure, these crucial amino acids are near the active site of Trm7 and are not directly involved in Trm7-Trm734 protein-protein interactions. Immunoprecipitation experiments with these Trm734 variants and Trm7 confirm that these residues are not involved in Trm7-Trm734 binding. Further experiments should help determine if these residues are important for tRNA binding or have another role in the modification of the tRNA. Furthermore, our discovery of a nonfunctional, stable Trm734 variant will be useful in determining if the reported roles of Trm734 in other biological processes such as retromer processing and resistance to Ty1 transposition are due to tRNA modification defects or to other bona fide cellular roles of Trm734.

The impact of tRNA modifications on translation in cancer: identifying novel therapeutic avenues

Recent advancements have illuminated the critical role of RNA modifications in post-transcriptional regulation, shaping the landscape of gene expression. This review explores how tRNA modifications emerge as critical players, fine-tuning functionalities that not only maintain the fidelity of protein synthesis but also dictate gene expression and translation profiles. Highlighting their dysregulation as a common denominator in various cancers, we systematically investigate the intersection of both cytosolic and mitochondrial tRNA modifications with cancer biology. These modifications impact key processes such as cell proliferation, tumorigenesis, migration, metastasis, bioenergetics and the modulation of the tumor immune microenvironment. The recurrence of altered tRNA modification patterns across different cancer types underscores their significance in cancer development, proposing them as potential biomarkers and as actionable targets to disrupt tumorigenic processes, offering new avenues for precision medicine in the battle against cancer.

m7GHub V2.0: an updated database for decoding the N7-methylguanosine (m7G) epitranscriptome

With recent progress in mapping N7-methylguanosine (m7G) RNA methylation sites, tens of thousands of experimentally validated m7G sites have been discovered in various species, shedding light on the significant role of m7G modification in regulating numerous biological processes including disease pathogenesis. An integrated resource that enables the sharing, annotation and customized analysis of m7G data will greatly facilitate m7G studies under various physiological contexts. We previously developed the m7GHub database to host mRNA m7G sites identified in the human transcriptome. Here, we present m7GHub v.2.0, an updated resource for a comprehensive collection of m7G modifications in various types of RNA across multiple species: an m7GDB database containing 430 898 putative m7G sites identified in 23 species, collected from both widely applied next-generation sequencing (NGS) and the emerging Oxford Nanopore direct RNA sequencing (ONT) techniques; an m7GDiseaseDB hosting 156 206 m7G-associated variants (involving addition or removal of an m7G site), including 3238 disease-relevant m7G-SNPs that may function through epitranscriptome disturbance; and two enhanced analysis modules to perform interactive analyses on the collections of m7G sites (m7GFinder) and functional variants (m7GSNPer). We expect that m7Ghub v.2.0 should serve as a valuable centralized resource for studying m7G modification. It is freely accessible at: www.rnamd.org/m7GHub2.

Quantitative analysis of tRNA abundance and modifications by nanopore RNA sequencing

Transfer RNAs (tRNAs) play a central role in protein translation. Studying them has been difficult in part because a simple method to simultaneously quantify their abundance and chemical modifications is lacking. Here we introduce Nano-tRNAseq, a nanopore-based approach to sequence native tRNA populations that provides quantitative estimates of both tRNA abundances and modification dynamics in a single experiment. We show that default nanopore sequencing settings discard the vast majority of tRNA reads, leading to poor sequencing yields and biased representations of tRNA abundances based on their transcript length. Re-processing of raw nanopore current intensity signals leads to a 12-fold increase in the number of recovered tRNA reads and enables recapitulation of accurate tRNA abundances. We then apply Nano-tRNAseq to Saccharomyces cerevisiae tRNA populations, revealing crosstalks and interdependencies between different tRNA modification types within the same molecule and changes in tRNA populations in response to oxidative stress.

Expanded tRNA methyltransferase family member TRMT9B regulates synaptic growth and function

Nervous system function rests on the formation of functional synapses between neurons. We have identified TRMT9B as a new regulator of synapse formation and function in Drosophila. TRMT9B has been studied for its role as a tumor suppressor and is one of two metazoan homologs of yeast tRNA methyltransferase 9 (Trm9), which methylates tRNA wobble uridines. Whereas Trm9 homolog ALKBH8 is ubiquitously expressed, TRMT9B is enriched in the nervous system. However, in the absence of animal models, TRMT9B's role in the nervous system has remained unstudied. Here, we generate null alleles of TRMT9B and find it acts postsynaptically to regulate synaptogenesis and promote neurotransmission. Through liquid chromatography-mass spectrometry, we find that ALKBH8 catalyzes canonical tRNA wobble uridine methylation, raising the question of whether TRMT9B is a methyltransferase. Structural modeling studies suggest TRMT9B retains methyltransferase function and, in vivo, disruption of key methyltransferase residues blocks TRMT9B's ability to rescue synaptic overgrowth, but not neurotransmitter release. These findings reveal distinct roles for TRMT9B in the nervous system and highlight the significance of tRNA methyltransferase family diversification in metazoans.

m7G-related genes predict prognosis and affect the immune microenvironment and drug sensitivity in osteosarcoma

Background: Osteosarcoma (OS), a primary malignant bone tumor, confronts therapeutic challenges rooted in multidrug resistance. Comprehensive understanding of disease occurrence and progression is imperative for advancing treatment strategies. m7G modification, an emerging post-transcriptional modification implicated in various diseases, may provide new insights to explore OS pathogenesis and progression. Methods: The m7G-related molecular landscape in OS was probed using diverse bioinformatics analyses, encompassing LASSO Cox regression, immune infiltration assessment, and drug sensitivity analysis. Furthermore, the therapeutic potential of AZD2014 for OS was investigated through cell apoptosis and cycle assays. Eventually, multivariate Cox analysis and experimental validations, were conducted to investigate the independent prognostic m7G-related genes. Results: A comprehensive m7G-related risk model incorporating eight signatures was established, with corresponding risk scores correlated with immune infiltration and drug sensitivity. Drug sensitivity analysis spotlighted AZD2014 as a potential therapeutic candidate for OS. Subsequent experiments corroborated AZD2014's capability to induce G1-phase cell cycle arrest and apoptosis in OS cells. Ultimately, multivariate Cox regression analysis unveiled the independent prognostic importance of CYFIP1 and EIF4A1, differential expressions of which were validated at histological and cytological levels. Conclusion: This study furnishes a profound understanding of the contribution of m7G-related genes to the pathogenesis of OS. The discerned therapeutic potential of AZD2014, in conjunction with the identification of CYFIP1 and EIF4A1 as independent risk factors, opens novel vistas for the treatment of OS.

N 2-methylguanosine modifications on human tRNAs and snRNA U6 are important for cell proliferation, protein translation and pre-mRNA splicing

Modified nucleotides in non-coding RNAs, such as tRNAs and snRNAs, represent an important layer of gene expression regulation through their ability to fine-tune mRNA maturation and translation. Dysregulation of such modifications and the enzymes installing them have been linked to various human pathologies including neurodevelopmental disorders and cancers. Several methyltransferases (MTases) are regulated allosterically by human TRMT112 (Trm112 in Saccharomyces cerevisiae), but the interactome of this regulator and targets of its interacting MTases remain incompletely characterized. Here, we have investigated the interaction network of human TRMT112 in intact cells and identify three poorly characterized putative MTases (TRMT11, THUMPD3 and THUMPD2) as direct partners. We demonstrate that these three proteins are active N2-methylguanosine (m2G) MTases and that TRMT11 and THUMPD3 methylate positions 10 and 6 of tRNAs, respectively. For THUMPD2, we discovered that it directly associates with the U6 snRNA, a core component of the catalytic spliceosome, and is required for the formation of m2G, the last 'orphan' modification in U6 snRNA. Furthermore, our data reveal the combined importance of TRMT11 and THUMPD3 for optimal protein synthesis and cell proliferation as well as a role for THUMPD2 in fine-tuning pre-mRNA splicing.

Novel sights on therapeutic, prognostic, and diagnostics aspects of non-coding RNAs in glioblastoma multiforme

Glioblastoma Multiforme (GBM) is the primary brain tumor and accounts for 200,000 deaths each year worldwide. The standard therapy includes surgical resection followed by temozolomide (TMZ)-based chemotherapy and radiotherapy. The survival period of GBM patients is only 12-15 months. Therefore, novel treatment modalities for GBM treatment are urgently needed. Mounting evidence reveals that non-coding RNAs (ncRNAs) were involved in regulating gene expression, the pathophysiology of GBM, and enhancing therapeutic outcomes. The combinatory use of ncRNAs, chemotherapeutic drugs, and tumor suppressor gene expression induction might provide an innovative, alternative therapeutic approach for managing GBM. Studies have highlighted the role of Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in prognosis and diagnosis. Dysregulation of ncRNAs is observed in virtually all tumor types, including GBMs. Studies have also indicated the blood-brain barrier (BBB) as a crucial factor that hinders chemotherapy. Although several nanoparticle-mediated drug deliveries were degrading effectively against GBM in vitro conditions. However, the potential to cross the BBB and optimum delivery of oligonucleotide RNA into GBM cells in the brain is currently under intense clinical trials. Despite several advances in molecular pathogenesis, GBM remains resistant to chemo and radiotherapy. Targeted therapies have less clinical benefit due to high genetic heterogeneity and activation of alternative pathways. Thus, identifying GBM-specific prognostic pathways, essential genes, and genomic aberrations provide several potential benefits as subtypes of GBM. Also, these approaches will provide insights into new strategies to overcome the heterogenous nature of GBM, which will eventually lead to successful therapeutic interventions toward precision medicine and precision oncology.

Small RNA modifications: regulatory molecules and potential applications

Small RNAs (also referred to as small noncoding RNAs, sncRNA) are defined as polymeric ribonucleic acid molecules that are less than 200 nucleotides in length and serve a variety of essential functions within cells. Small RNA species include microRNA (miRNA), PIWI-interacting RNA (piRNA), small interfering RNA (siRNA), tRNA-derived small RNA (tsRNA), etc. Current evidence suggest that small RNAs can also have diverse modifications to their nucleotide composition that affect their stability as well as their capacity for nuclear export, and these modifications are relevant to their capacity to drive molecular signaling processes relevant to biogenesis, cell proliferation and differentiation. In this review, we highlight the molecular characteristics and cellular functions of small RNA and their modifications, as well as current techniques for their reliable detection. We also discuss how small RNA modifications may be relevant to the clinical applications for the diagnosis and treatment of human health conditions such as cancer.

TMEM11 regulates cardiomyocyte proliferation and cardiac repair via METTL1-mediated m7G methylation of ATF5 mRNA

The mitochondrial transmembrane (TMEM) protein family has several essential physiological functions. However, its roles in cardiomyocyte proliferation and cardiac regeneration remain unclear. Here, we detected that TMEM11 inhibits cardiomyocyte proliferation and cardiac regeneration in vitro. TMEM11 deletion enhanced cardiomyocyte proliferation and restored heart function after myocardial injury. In contrast, TMEM11-overexpression inhibited neonatal cardiomyocyte proliferation and regeneration in mouse hearts. TMEM11 directly interacted with METTL1 and enhanced m⁷G methylation of Atf5 mRNA, thereby increasing ATF5 expression. A TMEM11-dependent increase in ATF5 promoted the transcription of Inca1, an inhibitor of cyclin-dependent kinase interacting with cyclin A1, which suppressed cardiomyocyte proliferation. Hence, our findings revealed that TMEM11-mediated m⁷G methylation is involved in the regulation of cardiomyocyte proliferation, and targeting the TMEM11-METTL1-ATF5-INCA1 axis may serve as a novel therapeutic strategy for promoting cardiac repair and regeneration.

Identification of m7G-Related miRNA Signatures Associated with Prognosis, Oxidative Stress, and Immune Landscape in Lung Adenocarcinoma

The role of N7-methylguanosine(m7G)-related miRNAs in lung adenocarcinoma (LUAD) remains unclear. We used LUAD data from The Cancer Genome Atlas (TCGA) to establish a risk model based on the m7G-related miRNAs, and divided patients into high-risk or low-risk subgroups. A nomogram for predicting overall survival (OS) was then constructed based on the independent risk factors. In addition, we performed a functional enrichment analysis and defined the oxidative stress-related genes, immune landscape as well as a drug response profile in the high-risk and low-risk subgroups. This study incorporated 28 m7G-related miRNAs into the risk model. The data showed a significant difference in the OS between the high-risk and low-risk subgroups. The receiver operating characteristic curve (ROC) predicted that the area under the curve (AUC) of one-year, three-year and five-year OS was 0.781, 0.804 and 0.853, respectively. The C-index of the prognostic nomogram for predicting OS was 0.739. We then analyzed the oxidative stress-related genes and immune landscape in the high-risk and low-risk subgroups. The data demonstrated significant differences in the expression of albumin (ALB), estimated score, immune score, stromal score, immune cell infiltration and functions between the high-risk and low-risk subgroups. In addition, the drug response analysis showed that low-risk subgroups may be more sensitive to tyrosine kinase inhibitor (TKI) and histone deacetylase (HDAC) inhibitors. We successfully developed a novel risk model based on m7G-related miRNAs in this study. The model can predict clinical prognosis and guide therapeutic regimens in patients with LUAD. Our data also provided new insights into the molecular mechanisms of m7G in LUAD.

Prognostic and immune implications of a novel 7-methylguanosine-related microRNA signature in breast invasive carcinoma: from exploration to validation

OBJECTIVES

This study aims to develop and validate a prognostic signature based on 7-methylguanosine-related (M7G-related) miRNAs for predicting prognosis and immune implications in breast invasive carcinoma (BRCA).

MATERIALS AND METHODS

M7G-related miRNA data of BRCA were obtained from The Cancer Genome Atlas (TCGA). Least absolute shrinkage and selection operator (LASSO)-penalized, univariate, and multivariate Cox regression analyses were used to construct the prognostic signature. Furthermore, the predictive validity was verified using Kaplan-Meier (KM) survival risk and receiver operating characteristic (ROC) plots. Internal random sampling verification was used to simplify and validate the signature. RT-qPCR was used to quantify the expression level of transcriptional profiles. The independent prognostic role of the risk score was validated using univariate and multivariate regression. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used for functional and immune enrichment analysis.

RESULTS

A total of 18 M7G-related miRNAs were identified to construct the prognostic signature in BRCA. The low-risk group exhibited significantly higher overall survival than the high-risk group in the KM survival plot (P < 0.001). The area under the curve (AUC) for 1-, 3-, and 5-year survivals in the ROC curve were 0.737, 0.724, and 0.702, respectively. The survival significance in the training and testing cohorts was confirmed by random sampling verification. The most prominent miRNAs in the signature were the miR-7, miR-139, miR-10b, and miR-4728. Furthermore, immune scores for B, mast, and Th1 cells varied between risk groups. Our research demonstrated that CD52 was the most positively correlated gene with immune cells and functions in BRCA.

CONCLUSION

Our study presents a comprehensive and systematic analysis of M7G-related miRNAs to construct a prognostic signature in BRCA. The signature demonstrated excellent prognostic validity, with the risk score as an independent prognostic factor. These results provide critical evidence for further investigation of M7G miRNAs and offer new insights for BRCA patients in the context of effective immunotherapy.

Recent expansion of metabolic versatility in Diplonema papillatum, the model species of a highly speciose group of marine eukaryotes

BACKGROUND

Diplonemid flagellates are among the most abundant and species-rich of known marine microeukaryotes, colonizing all habitats, depths, and geographic regions of the world ocean. However, little is known about their genomes, biology, and ecological role.

RESULTS

We present the first nuclear genome sequence from a diplonemid, the type species Diplonema papillatum. The ~ 280-Mb genome assembly contains about 32,000 protein-coding genes, likely co-transcribed in groups of up to 100. Gene clusters are separated by long repetitive regions that include numerous transposable elements, which also reside within introns. Analysis of gene-family evolution reveals that the last common diplonemid ancestor underwent considerable metabolic expansion. D. papillatum-specific gains of carbohydrate-degradation capability were apparently acquired via horizontal gene transfer. The predicted breakdown of polysaccharides including pectin and xylan is at odds with reports of peptides being the predominant carbon source of this organism. Secretome analysis together with feeding experiments suggest that D. papillatum is predatory, able to degrade cell walls of live microeukaryotes, macroalgae, and water plants, not only for protoplast feeding but also for metabolizing cell-wall carbohydrates as an energy source. The analysis of environmental barcode samples shows that D. papillatum is confined to temperate coastal waters, presumably acting in bioremediation of eutrophication.

CONCLUSIONS

Nuclear genome information will allow systematic functional and cell-biology studies in D. papillatum. It will also serve as a reference for the highly diverse diplonemids and provide a point of comparison for studying gene complement evolution in the sister group of Kinetoplastida, including human-pathogenic taxa.

A new prediction model of hepatocellular carcinoma based on N7-methylguanosine modification

PURPOSE

Hepatocellular carcinoma (HCC) is a kind of primary liver cancer. It is a common malignant tumor of digestive system that is difficult to predict the prognosis of patients. As an important epigenetic modification, N7 methyl guanosine (m7G) is indispensable in gene regulation. This regulation may affect the development and occurrence of cancer. However, the prognosis of long non coding RNAs (lncRNAs) in HCC is limited, especially how m7G-related lncRNAs regulate the development of HCC has not been reported.

METHODS

The Cancer Genome Atlas (TCGA) provides us with the expression data and corresponding clinical information of HCC patients we need. We used a series of statistical methods to screen four kinds of m7G-related lncRNAs related to HCC prognosis and through a series of verifications, the results were in line with our expectations. Finally, we also explored the IC50 difference and correlation analysis of various common chemotherapy drugs.

RESULT

Our study identified four differentially expressed m7g-related lncRNAs associated with HCC prognosis. Survival curve analysis showed that high risk lncRNAs would lead to poor prognosis of HCC patients. M7G signature's AUC was 0.789, which shows that the prognosis model we studied has certain significance in predicting the prognosis of HCC patients. Moreover, our study found that different risk groups have different immune and tumor related pathways through gene set enrichment analysis. In addition, many immune cell functions are significantly different among different risk groups, such as T cell functions, including coordination of type I INF response and coordination of type II INF response. The expression of PDCD1, HHLA2, CTLA-4 and many other immune checkpoints in different risk groups is also different. Additionally, we analyzed the differences of IC50 and risk correlation of 15 chemotherapeutic drugs among different risk groups.

CONCLUSION

A novel lncRNAs associated with m7G predicts the prognosis of HCC.

The thiolation of uridine 34 in tRNA, which controls protein translation, depends on a [4Fe-4S] cluster in the archaeum Methanococcus maripaludis

Thiolation of uridine 34 in the anticodon loop of several tRNAs is conserved in the three domains of life and guarantees fidelity of protein translation. U34-tRNA thiolation is catalyzed by a complex of two proteins in the eukaryotic cytosol (named Ctu1/Ctu2 in humans), but by a single NcsA enzyme in archaea. We report here spectroscopic and biochemical experiments showing that NcsA from Methanococcus maripaludis (MmNcsA) is a dimer that binds a [4Fe-4S] cluster, which is required for catalysis. Moreover, the crystal structure of MmNcsA at 2.8 Å resolution shows that the [4Fe-4S] cluster is coordinated by three conserved cysteines only, in each monomer. Extra electron density on the fourth nonprotein-bonded iron most likely locates the binding site for a hydrogenosulfide ligand, in agreement with the [4Fe-4S] cluster being used to bind and activate the sulfur atom of the sulfur donor. Comparison of the crystal structure of MmNcsA with the AlphaFold model of the human Ctu1/Ctu2 complex shows a very close superposition of the catalytic site residues, including the cysteines that coordinate the [4Fe-4S] cluster in MmNcsA. We thus propose that the same mechanism for U34-tRNA thiolation, mediated by a [4Fe-4S]-dependent enzyme, operates in archaea and eukaryotes.

Molecular mechanism of tRNA binding by the Escherichia coli N7 guanosine methyltransferase TrmB

Among the large and diverse collection of tRNA modifications, 7-methylguanosine (m⁷G) is frequently found in the tRNA variable loop at position 46. This modification is introduced by the TrmB enzyme, which is conserved in bacteria and eukaryotes. However, the molecular determinants and the mechanism for tRNA recognition by TrmB are not well understood. Complementing the report of various phenotypes for different organisms lacking TrmB homologs, we report here hydrogen peroxide sensitivity for the Escherichia coli ΔtrmB knockout strain. To gain insight into the molecular mechanism of tRNA binding by E. coli TrmB in real-time, we developed a new assay based on introducing a 4-thiouridine modification at position 8 of in vitro transcribed tRNA^Phe enabling us to fluorescently label this unmodified tRNA. Using rapid kinetic stopped-flow measurements with this fluorescent tRNA, we examined the interaction of wildtype and single substitution variants of TrmB with tRNA. Our results reveal the role of SAM for rapid and stable tRNA binding, the rate-limiting nature of m⁷G46 catalysis for tRNA release, and the importance of residues R26, T127 and R155 across the entire surface of TrmB for tRNA binding.

Determining RNA Natural Modifications and Nucleoside Analog-Labeled Sites by a Chemical/Enzyme-Induced Base Mutation Principle

The natural chemical modifications of messenger RNA (mRNA) in living organisms have shown essential roles in both physiology and pathology. The mapping of mRNA modifications is critical for interpreting their biological functions. In another dimension, the synthesized nucleoside analogs can enable chemical labeling of cellular mRNA through a metabolic pathway, which facilitates the study of RNA dynamics in a pulse-chase manner. In this regard, the sequencing tools for mapping both natural modifications and nucleoside tags on mRNA at single base resolution are highly necessary. In this work, we review the progress of chemical sequencing technology for determining both a variety of naturally occurring base modifications mainly on mRNA and a few on transfer RNA and metabolically incorporated artificial base analogs on mRNA, and further discuss the problems and prospects in the field.

Identification and verification of m7G-Related genes as biomarkers for prognosis of sarcoma

Background: Increasing evidence indicates a crucial role for N7-methylguanosine (m7G) methylation modification in human disease development, particularly cancer, and aberrant m7G levels are closely associated with tumorigenesis and progression via regulation of the expression of multiple oncogenes and tumor suppressor genes. However, the role of m7G in sarcomas (SARC) has not been adequately evaluated. Materials and methods: Transcriptome and clinical data were gathered from the TCGA database for this study. Normal and SARC groups were compared for the expression of m7G-related genes (m7GRGs). The expression of m7GRGs was verified using real-time quantitative PCR (RT-qPCR) in SARC cell lines. Then, differentially expressed genes (DEGs) were identified between high and low m7GRGs expression groups in SARC samples, and GO enrichment and KEGG pathways were evaluated. Next, prognostic values of m7GRGs were evaluated by Cox regression analysis. Subsequently, a prognostic model was constructed using m7GRGs with good prognostic values by Lasso regression analysis. Besides, the relationships between prognostic m7GRGs and immune infiltration, clinical features, cuproptosis-related genes, and antitumor drugs were investigated in patients with SARC. Finally, a ceRNA regulatory network based on m7GRGs was constructed. Results: The expression of ten m7GRGs was higher in the SARC group than in the control group. DEGs across groups with high and low m7GRGs expression were enriched for adhesion sites and cGMP-PKG. Besides, we constructed a prognostic model that consists of EIF4A1, EIF4G3, NCBP1, and WDR4 m7GRGs for predicting the survival likelihood of sarcoma patients. And the elevated expression of these four prognostic m7GRGs was substantially associated with poor prognosis and elevated expression in SARC cell lines. Moreover, we discovered that these four m7GRGs expressions were negatively correlated with CD4⁺ T cell levels, dendritic cell level and tumor purity, and positively correlated with tumor mutational burden, microsatellite instability, drug sensitivity and cuproptosis-related genes in patients with sarcomas. Then, a triple regulatory network of mRNA, miRNA, and lncRNA was established. Conclusion: The current study identified EIF4A1, EIF4G3, NCBP1, and WDR4 as prognostic genes for SARC that are associated with m7G.These findings extend our knowledge of m7G methylation in SARC and may guide the development of innovative treatment options.

Transfer RNA Modification Enzymes with a Thiouridine Synthetase, Methyltransferase and Pseudouridine Synthase (THUMP) Domain and the Nucleosides They Produce in tRNA

The existence of the thiouridine synthetase, methyltransferase and pseudouridine synthase (THUMP) domain was originally predicted by a bioinformatic study. Since the prediction of the THUMP domain more than two decades ago, many tRNA modification enzymes containing the THUMP domain have been identified. According to their enzymatic activity, THUMP-related tRNA modification enzymes can be classified into five types, namely 4-thiouridine synthetase, deaminase, methyltransferase, a partner protein of acetyltransferase and pseudouridine synthase. In this review, I focus on the functions and structures of these tRNA modification enzymes and the modified nucleosides they produce. Biochemical, biophysical and structural studies of tRNA 4-thiouridine synthetase, tRNA methyltransferases and tRNA deaminase have established the concept that the THUMP domain captures the 3'-end of RNA (in the case of tRNA, the CCA-terminus). However, in some cases, this concept is not simply applicable given the modification patterns observed in tRNA. Furthermore, THUMP-related proteins are involved in the maturation of other RNAs as well as tRNA. Moreover, the modified nucleosides, which are produced by the THUMP-related tRNA modification enzymes, are involved in numerous biological phenomena, and the defects of genes for human THUMP-related proteins are implicated in genetic diseases. In this review, these biological phenomena are also introduced.

Internal m7G methylation: A novel epitranscriptomic contributor in brain development and diseases

In recent years, N7-methylguanosine (m7G) methylation, originally considered as messenger RNA (mRNA) 5' caps modifications, has been identified at defined internal positions within multiple types of RNAs, including transfer RNAs, ribosomal RNAs, miRNA, and mRNAs. Scientists have put substantial efforts to discover m7G methyltransferases and methylated sites in RNAs to unveil the essential roles of m7G modifications in the regulation of gene expression and determine the association of m7G dysregulation in various diseases, including neurological disorders. Here, we review recent findings regarding the distribution, abundance, biogenesis, modifiers, and functions of m7G modifications. We also provide an up-to-date summary of m7G detection and profile mapping techniques, databases for validated and predicted m7G RNA sites, and web servers for m7G methylation prediction. Furthermore, we discuss the pathological roles of METTL1/WDR-driven m7G methylation in neurological disorders. Last, we outline a roadmap for future directions and trends of m7G modification research, particularly in the central nervous system.

Integrated analysis from multicentre studies identities m7G-related lncRNA-derived molecular subtypes and risk stratification systems for gastric cancer

Introduction

Gastric cancer (GC) is the fourth leading cause of cancer death worldwide. Due to the lack of effective chemotherapy methods for advanced gastric cancer and poor prognosis, the emergence of immunotherapy has brought new hope to gastric cancer. Further research is needed to improve the response rate to immunotherapy and identify the populations with potential benefits of immunotherapy. It is unclear whether m7G-related lncRNAs influence tumour immunity and the prognosis of immunotherapy.

Methods

This study evaluated 29 types of immune cells and immune functions in gastric cancer patients, and m7G-related lncRNAs and their molecular subtypes were identified. In addition, we also studied the biological function characteristics of m7G-related lncRNA molecular subtypes. Finally, the patient's risk score was calculated based on m7G-related lncRNAs, and a nomogram of staging and risk groups was established to predict the prognosis. For experimental verification, RT-qPCR were preformed from the native cohort.

Results

After identifying m7G-related lncRNAs and their molecular subtypes, we found three molecular subtypes, the B subtype had the highest level of infiltration, and the B subtype may benefit more from immunotherapy. We divided GC patients into two regulator subtypes based on biological function. The two subtypes have significant immunological differences and can be used to judge ICI treatment. We established a risk score formula based on five lncRNAs, including LINC00924, LINC00944, LINC00865, LINC00702, and ZFAS1. Patients with poor prognoses were closely related to patients in the high-risk group. After comprehensive analysis of different risk groups, the efficacy of the high-risk group on bleomycin, cisplatin, docetaxel, doxorubicin and etoposide was better than that of the low-risk group, suggesting that risk subgroups based on risk scores play a guiding role in chemotherapy and that the high-risk group may benefit more from immunotherapy. RT-qPCR results showed that LINC00924, LINC00944, and LINC00865 were highly expressed in tumour tissues, while LINC00702 and ZFAS1 were expressed at low levels in tumour tissues.

Discussion

In conclusion, we were the first to discover that m7G-related lncRNAs play a vital role in the tumour immune microenvironment of gastric cancer, and a risk prediction model was established to identify patients with potential benefits from immunotherapy and predict the prognosis of GC patients.

m7G-quant-seq: Quantitative Detection of RNA Internal N7-Methylguanosine

Methods for the precise detection and quantification of RNA modifications are critical to uncover functional roles of diverse RNA modifications. The internal m⁷G modification in mammalian cytoplasmic tRNAs is known to affect tRNA function and impact embryonic stem cell self-renewal, tumorigenesis, cancer progression, and other cellular processes. Here, we introduce m⁷G-quant-seq, a quantitative method that accurately detects internal m⁷G sites in human cytoplasmic tRNAs at single-base resolution. The efficient chemical reduction and mild depurination can almost completely convert internal m⁷G sites into RNA abasic sites (AP sites). We demonstrate that RNA abasic sites induce a mixed variation pattern during reverse transcription, including G → A or C or T mutations as well as deletions. We calculated the total variation ratio to quantify the m⁷G modification fraction at each methylated site. The calibration curves of all relevant motif contexts allow us to more quantitatively determine the m⁷G methylation level. We detected internal m⁷G sites in 22 human cytoplasmic tRNAs from HeLa and HEK293T cells and successfully estimated the corresponding m⁷G methylation stoichiometry. m⁷G-quant-seq could be applied to monitor the tRNA m⁷G methylation level change in diverse biological processes.

Analysis of m7G methylation modification patterns and pulmonary vascular immune microenvironment in pulmonary arterial hypertension

Background

M⁷G methylation modification plays an important role in cardiovascular disease development. Dysregulation of the immune microenvironment is closely related to the pathogenesis of PAH. However, it is unclear whether m⁷G methylation is involved in the progress of PAH by affecting the immune microenvironment.

Methods

The gene expression profile of PAH was obtained from the GEO database, and the m⁷G regulatory factors were analyzed for differences. Machine learning algorithms were used to screen characteristic genes, including the least absolute shrinkage and selection operator, random forest, and support vector machine recursive feature elimination analysis. Constructed a nomogram model, and receiver operating characteristic was used to evaluate the diagnosis of disease characteristic genes value. Next, we used an unsupervised clustering method to perform consistent clustering analysis on m⁷G differential genes. Used the ssGSEA algorithm to estimate the relationship between the m⁷G regulator in PAH and immune cell infiltration and analyze the correlation with disease-characteristic genes. Finally, the listed drugs were evaluated through the screened signature genes.

Results

We identified 15 kinds of m⁷G differential genes. CYFIP1, EIF4E, and IFIT5 were identified as signature genes by the machine learning algorithm. Meanwhile, two m⁷G molecular subtypes were identified by consensus clustering (cluster A/B). In addition, immune cell infiltration analysis showed that activated CD4 T cells, regulatory T cells, and type 2 T helper cells were upregulated in m⁷G cluster B, CD56 dim natural killer cells, MDSC, and monocyte were upregulated in the m⁷G cluster A. It might be helpful to select Calpain inhibitor I and Everolimus for the treatment of PAH.

Conclusion

Our study identified CYFIP1, EIF4E, and IFIT5 as novel diagnostic biomarkers in PAH. Furthermore, their association with immune cell infiltration may facilitate the development of immune therapy in PAH.

Construction of N-7 methylguanine-related mRNA prognostic model in uterine corpus endometrial carcinoma based on multi-omics data and immune-related analysis

N-7 methylguanine (m7G) is one of the most common RNA base modifications in post-transcriptional regulation, which participates in multiple processes such as transcription, mRNA splicing and translation during the mRNA life cycle. However, its expression and prognostic value in uterine corpus endometrial carcinoma (UCEC) have not been systematically studied. In this paper, the data such as gene expression profiles, clinical data of UCEC patients, somatic mutations and copy number variants (CNVs) are obtained from the cancer genome atlas (TCGA) and UCSC Xena. By analyzing the expression differences of m7G-related mRNA in UCEC and plotting the correlation network maps, a risk score model composed of four m7G-related mRNAs (NSUN2, NUDT3, LARP1 and NCBP3) is constructed using least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression in order to identify prognosis and immune response. The correlation of clinical prognosis is analyzed between the m7G-related mRNA and UCEC via Kaplan-Meier method, receiver operating characteristic (ROC) curve, principal component analysis (PCA), t-SNE, decision curve analysis (DCA) curve and nomogram etc. It is concluded that the high risk is significantly correlated with (P < 0.001) the poorer overall survival (OS) in patients with UCEC. It is one of the independent risk factors affecting the OS. Differentially expressed genes are identified by R software in the high and low risk groups. The functional analysis and pathway enrichment analysis have been performed. Single sample gene set enrichment analysis (ssGSEA), immune checkpoints, m6A-related genes, tumor mutation burden (TMB), stem cell correlation, tumor immune dysfunction and rejection (TIDE) scores and drug sensitivity are also used to study the risk model. In addition, we have obtained 3 genotypes based on consensus clustering, which are significantly related to (P < 0.001) the OS and progression-free survival (PFS). The deconvolution algorithm (CIBERSORT) is applied to calculate the proportion of 22 tumor infiltrating immune cells (TIC) in UCEC patients and the estimation algorithm (ESTIMATE) is applied to work out the number of immune and matrix components. In summary, m7G-related mRNA may become a potential biomarker for UCEC prognosis, which may promote UCEC occurrence and development by regulating cell cycles and immune cell infiltration. It is expected to become a potential therapeutic target of UECE.

Dysfunctional tRNA reprogramming and codon-biased translation in cancer

Many cancers hijack translation to increase the synthesis of tumor-driving proteins, the messenger mRNAs of which have specific codon usage patterns. Termed 'codon-biased translation' and originally identified in stress response regulation, this mechanism is supported by diverse studies demonstrating how the 50 RNA modifications of the epitranscriptome, specific tRNAs, and codon-biased mRNAs are used by oncogenic programs to promote proliferation and chemoresistance. The epitranscriptome writers METTL1-WDR4, Elongator complex protein (ELP)1-6, CTU1-2, and ALKBH8-TRM112 illustrate the principal mechanism of codon-biased translation, with gene amplifications, increased RNA modifications, and enhanced tRNA stability promoting cancer proliferation. Furthermore, systems-level analyses of 34 tRNA writers and 493 tRNA genes highlight the theme of tRNA epitranscriptome dysregulation in many cancers and identify candidate tRNA writers, tRNA modifications, and tRNA molecules as drivers of pathological codon-biased translation.

BRPCA: Bounded Robust Principal Component Analysis to Incorporate Similarity Network for N7-Methylguanosine(m7G) Site-Disease Association Prediction

Recent studies have revealed that N7-methylguanosine(m⁷G) plays a pivotal role in various biological processes and disease pathogenesis. To date, transcriptome-wide m⁷G modification sites have been identified by high-throughput sequencing approaches, and some related information has been recorded in a few biological databases. However, the mechanism of site action in disease remains uncharted. Wet experiments can help identify true m⁷G sites with high confidence, but it is time-consuming to find the true ones in such a large number of sites, which will also cost too much. Thus, computational methods are emergently needed to predict the associations between m⁷G sites and various diseases, thus help to uncover potential active sites for specific diseases. In this article, we proposed a bounded robust principal component analysis (BRPCA) method to predict unknown m⁷G-disease association based on similarity information. Importantly, BRPCA tolerates the noise and redundancy existing in association and similarity information. Moreover, a suitable bounded constraint is incorporated into BRPCA to ensure that the predicted association scores locate in a meaningful interval. The extensive experiments demonstrate the superiority and robustness of the BRPCA.

N7-methylguanosine-related lncRNAs: Predicting the prognosis and diagnosis of colorectal cancer in the cold and hot tumors

Background: 7-Methylguanosine(m7G) contributes greatly to its pathogenesis and progression in colorectal cancer. We proposed building a prognostic model of m7G-related LncRNAs. Our prognostic model was used to identify differences between hot and cold tumors.

Methods: The study included 647 colorectal cancer patients (51 cancer-free patients and 647 cancer patients) from The Cancer Genome Atlas (TCGA). We identified m7G-related prognostic lncRNAs by employing the univariate Cox regression method. Assessments were conducted using univariate Cox regression, multivariate Cox regression, receiver operating characteristics (ROC), nomogram, calibration curves, and Kaplan-Meier analysis. All of these procedures were used with the aim of confirming the validity and stability of the model. Besides these two analyses, we also conducted half-maximal inhibitory concentration (IC50), immune analysis, principal component analysis (PCA), and gene set enrichment analysis (GSEA). The entire set of m7G-related (lncRNAs) with respect to cold and hot tumors has been divided into two clusters for further discussion of immunotherapy.

Results: The risk model was constructed with 17 m7G-related lncRNAs. A good correlation was found between the calibration plots and the prognosis prediction in the model. By assessing IC50 in a significant way across risk groups, systemic treatment can be guided. By using clusters, it may be possible to distinguish hot and cold tumors effectively and to aid in specific therapeutic interventions. Cluster 1 was identified as having the highest response to immunotherapy drugs and thus was identified as the hot tumor.

Conclusion: This study shows that 17 m7G-related lncRNA can be used in clinical settings to predict prognosis and use them to determine whether a tumor is cold or hot in colorectal cancer and improve the individualization of treatment.

Comprehensive analysis of the prognosis and immune infiltration landscape of RNA methylation-related subtypes in pancreatic cancer

Background

RNA methylation refers to a form of methyl modification in RNA that modulates various epigenetic alterations. Mounting studies have focused on its potential mechanisms in cancer initiation and progression. However, the prognostic value and potential role of RNA methylation in the immune microenvironment of pancreatic cancer remain unclear.

Methods

Comprehensive bioinformatics analysis was performed to illuminate the expression profiles of RNA methylation modulators. In addition, the ConsensusClusterPlus algorithm was utilized to identify two remarkably different subtypes, and a feasible risk stratification method was established to accurately estimate prognosis. In addition, we validated our signature at the cytology and histology levels and conducted functional experiments to explore the biological functions of our key genes.

Results

Two subtypes with remarkable survival differences were identified by the consensus clustering algorithm. Cluster 2 tended to have higher expression levels of RNA methylation regulators and to be the high RNA methylation group. In addition, cluster 1 exhibited a significantly higher abundance of almost all immune cells and increased immune checkpoint expression compared to cluster 2. Chemotherapeutic sensitivity analysis indicated that there were significant differences in the sensitivity of four of the six drugs between different subgroups. Mutation investigation revealed a higher mutation burden and a higher number of mutations in cluster 2. An accurate and feasible risk stratification method was established based on the expression of key genes of each subtype. Patients with low risk scores exhibited longer survival times in one training (TCGA) and two validation cohorts (ICGC, GSE57495), with p values of 0.001, 0.0081, and 0.0042, respectively. In addition, our signature was further validated in a cohort from Fudan University Shanghai Cancer Center. The low-risk group exhibited higher immune cell abundance and immune checkpoint levels than the high-risk group. The characteristics of the low-risk group were consistent with those of cluster 1: higher stromal score, estimate score, and immune score and lower tumor purity. Additionally, cell function investigations suggested that knockdown of CDKN3 remarkably inhibited the proliferation and migration of pancreatic cancer cells.

Conclusions

RNA methylation has a close correlation with prognosis, immune infiltration and therapy in pancreatic cancer. Our subtypes and risk stratification method can accurately predict prognosis and the efficacy of immune therapy and chemotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09863-z.

Construction of a Novel Prognostic Model in Lung Adenocarcinoma Based on 7-Methylguanosine-Related Gene Signatures

Increasing evidence has implicated the modification of 7-methylguanosine (m7G), a type of RNA modification, in tumor progression. However, no comprehensive analysis to date has summarized the predicted role of m7G-related gene signatures in lung adenocarcinoma (LUAD). Herein, we aimed to develop a novel prognostic model in LUAD based on m7G-related gene signatures. The LUAD transcriptome profiling data and corresponding clinical data were acquired from the Cancer Genome Atlas (TCGA) and two Gene Expression Omnibus datasets. After screening, we first obtained 29 m7G-related genes, most of which were upregulated in tumor tissues and negatively associated with overall survival (OS). According to the expression similarity of m7G-related genes, the combined samples from the TCGA-LUAD and GSE68465 datasets were further classified as two clusters that exhibit distinct OS rates and genetic heterogeneity. Then, we constructed a novel prognostic model involving four genes by using 130 differentially expressed genes among the two clusters. The combined samples were randomly divided into a training cohort and an internal validation cohort in a 1:1 ratio, and the GSE72094 dataset was used as an external validation cohort. The samples were divided into high- and low-risk groups. We demonstrated that a higher risk score was an independent negative prognostic factor and predicted poor OS. A nomogram was further constructed to better predict the survival of LUAD patients. Functional enrichment analyses indicated that cell cycle and DNA replication-related biological processes and pathways were enriched in the high-risk group. More importantly, the low-risk group had greater infiltration and enrichment of most immune cells, as well as higher ESTIMATE, immune, and stromal scores. In addition, the high-risk group had a lower TIDE score and higher expressions of most immune checkpoint-related genes. We finally noticed that patients in the high-risk group were more sensitive to chemotherapeutic agents commonly used in LUAD. In conclusion, we herein summarized for the first time the alterations and prognostic role of m7G-related genes in LUAD and then constructed a prognostic model based on m7G-related gene signatures that could accurately and stably predict survival and guide individualized treatment decision-making in LUAD patients.

N7-Methylguanosine Genes Related Prognostic Biomarker in Hepatocellular Carcinoma

Background: About 90% of liver cancer-related deaths are caused by hepatocellular carcinoma (HCC). N7-methylguanosine (m7G) modification is associated with the biological process and regulation of various diseases. To the best of our knowledge, its role in the pathogenesis and prognosis of HCC has not been thoroughly investigated.

Aim: To identify N7-methylguanosine (m7G) related prognostic biomarkers in HCC. Furthermore, we also studied the association of m7G–related prognostic gene signature with immune infiltration in HCC.

Methods: The TCGA datasets were used as a training and GEO dataset “GSE76427” for validation of the results. Statistical analyses were performed using the R statistical software version 4.1.2.

Results: Functional enrichment analysis identified some pathogenesis related to HCC. We identified 3 m7G-related genes (CDK1, ANO1, and PDGFRA) as prognostic biomarkers for HCC. A risk score was calculated from these 3 prognostic m7G-related genes which showed the high-risk group had a significantly poorer prognosis than the low-risk group in both training and validation datasets. The 3- and 5-years overall survival was predicted better with the risk score than the ideal model in the entire cohort in the predictive nomogram. Furthermore, immune checkpoint genes like CTLA4, HAVCR2, LAG3, and TIGT were expressed significantly higher in the high-risk group and the chemotherapy sensitivity analysis showed that the high-risk groups were responsive to sorafenib treatment.

Conclusion: These 3 m7G genes related signature model can be used as prognostic biomarkers in HCC and a guide for immunotherapy and chemotherapy response. Future clinical study on this biomarker model is required to verify its clinical implications.

m7G Methylation-Related Genes as Biomarkers for Predicting Overall Survival Outcomes for Hepatocellular Carcinoma

Aim: The search for prognostic biomarkers and the construction of a prognostic risk model for hepatocellular carcinoma (HCC) based on N7-methyladenosine (m7G) methylation regulators. Methods: HCC transcriptomic data and clinical data were obtained from The Cancer Genome Atlas database and Shanghai Ninth People's Hospital, respectively. m7G methylation regulators were extracted, differential expression analysis was performed using the R software "limma" package, and one-way Cox regression analysis was used to screen for prognostic associations of m7G regulators. Using multi-factor Cox regression analysis, a prognostic risk model for HCC was constructed. Each patient's risk score was calculated using the model, and patients were divided into high- and low-risk groups according to the median risk score. Cox regression analysis was used to verify the validity of the model in the prognostic assessment of HCC in conjunction with clinicopathological characteristics. Results: The prognostic model was built using the seven genes, namely, CYFIP1, EIF4E2, EIF4G3, GEMIN5, NCBP2, NUDT10, and WDR4. The Kaplan-Meier survival analysis showed poorer 5-years overall survival in the high-risk group compared with the low-risk group, and the receiver-operating characteristic (ROC) curve suggested good model prediction (area under the curve AUC = 0.775, 0.820, and 0.839 at 1, 3, and 5 years). The Cox regression analysis included model risk scores and clinicopathological characteristics, and the results showed that a high-risk score was the only independent risk factor for the prognosis of patients with HCC. Conclusions: The developed bioinformatics-based prognostic risk model for HCC was found to have good predictive power.

The crucial mechanism and therapeutic implication of RNA methylation in bone pathophysiology

Methylation is the most common posttranscriptional modification in cellular RNAs, which has been reported to modulate the alteration of RNA structure for initiating relevant functions such as nuclear translocation and RNA degradation. Recent studies found that RNA methylation especially N6-methyladenosine (m⁶A) regulates the dynamic balance of bone matrix and forms a complicated network in bone metabolism. The modulation disorder of RNA methylation contributes to several pathological bone diseases including osteoporosis (OP), osteoarthritis (OA), rheumatoid arthritis (RA), and so on. In the review, we will discuss advanced technologies for detecting RNA methylation, summarize RNA methylation-related biological impacts on regulating bone homeostasis and pathological bone diseases. In addition, we focus on the promising roles of RNA methylation in early diagnosis and therapeutic implications for bone-related diseases. Then, we aim to establish a theoretical basis for further investigation in this meaningful field.

A N7-Methylguanine-Related Gene Signature Applicable for the Prognosis and Microenvironment of Prostate Cancer

Background

Despite the constant iteration of small-molecule inhibitors and immune checkpoint inhibitors, PRAD (prostate adenocarcinoma) patients with distant metastases and biochemical recurrence maintain a poor survival outcome along with an increasing morbidity in recent years. N⁷-Methylguanine, a new-found type of RNA modification, has demonstrated an essential role in tumor progression but has hardly been studied for its effect on prostate carcinoma. The current study aimed to seek m⁷G (N7-methylguanosine) related prognostic biomarkers and potential targets for PRAD treatment.

Methods

42 genes related to m⁷G were collected from former literatures and GSEA (Gene Set Enrichment Analysis) website. Then, RNA-seq (RNA sequencing) and clinical data from TCGA-PRAD (The Cancer Genome Atlas-Prostate) cohort were retrieved to screen the differentially expressed m⁷G genes to further construct a multivariate Cox prognostic model for PRAD. Next, GSE116918, a prostate cancer cohort acquired from GEO (Gene Expression Omnibus) database, was analyzed for the external validation group to assess the ability to predict BFFS (biochemical failure-free survival) of our m⁷G prognostic signature. Kaplan-Meier, ROC (receiver operator characteristic), AUC (areas under ROC curve), and calibration curves were adopted to display the performance of this prognostic signature. In addition, immune infiltration analysis was implemented to evaluate the effect of these m⁷G genes on immunoinfiltrating cells. Correlation with drug susceptibility of the m⁷G signature was also analyzed by matching drug information in CellMiner database.

Results

The m⁷G-related prognostic signature, including three genes (EIF3D, EIF4A1, LARP1) illustrated superior prognostic ability for PRAD in both training and validation cohorts. The 5-year AUC were 0.768 for TCGA-PRAD and 0.608 for GSE116918. It can well distinguish patients into different risk groups of biochemical recurrence (p =1e-04 for TCGA-PRAD and p =0.0186 for GSE116918). Immune infiltration analysis suggested potential regulation of m⁷G genes on neutrophils and dendritic cells in PRAD.

Conclusions

A m⁷G-related prognostic signature was constructed and validated in the current study, giving new sights of m⁷G methylation in predicting the prognostic and improving the treatment of PRAD.

Expression and Potential Biomarkers of Regulators for M7G RNA Modification in Gliomas

Gliomas are the most frequent primary malignant brain tumors of the central nervous system, causing significant impairment and death. There is mounting evidence that N7 methylguanosine (m7G) RNA dysmethylation plays a significant role in the development and progression of cancer. However, the expression patterns and function of the m7G RNA methylation regulator in gliomas are yet unknown. The goal of this study was to examine the expression patterns of 31 critical regulators linked with m7G RNA methylation and their prognostic significance in gliomas. To begin, we systematically analyzed patient clinical and prognostic data and mRNA gene expression data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We found that 17 key regulators of m7G RNA methylation showed significantly higher expression levels in gliomas. We then divided the sample into two subgroups by consensus clustering. Cluster 2 had a poorer prognosis than cluster 1 and was associated with a higher histological grade. In addition, cluster 2 was significantly enriched for cancer-related pathways. Based on this discovery, we developed a risk model involving three m7G methylation regulators. Patients were divided into high-risk and low-risk groups based on risk scores. Overall survival (OS) was significantly lower in the high-risk group than in the low-risk group. Further analysis showed that the risk score was an independent prognostic factor for gliomas.

Required Elements in tRNA for Methylation by the Eukaryotic tRNA (Guanine-N2-) Methyltransferase (Trm11-Trm112 Complex)

The Saccharomyces cerevisiae Trm11 and Trm112 complex (Trm11-Trm112) methylates the 2-amino group of guanosine at position 10 in tRNA and forms N²-methylguanosine. To determine the elements required in tRNA for methylation by Trm11-Trm112, we prepared 60 tRNA transcript variants and tested them for methylation by Trm11-Trm112. The results show that the precursor tRNA is not a substrate for Trm11-Trm112. Furthermore, the CCA terminus is essential for methylation by Trm11-Trm112, and Trm11-Trm112 also only methylates tRNAs with a regular-size variable region. In addition, the G10-C25 base pair is required for methylation by Trm11-Trm112. The data also demonstrated that Trm11-Trm112 recognizes the anticodon-loop and that U38 in tRNA^Ala acts negatively in terms of methylation. Likewise, the U32-A38 base pair in tRNA^Cys negatively affects methylation. The only exception in our in vitro study was tRNA^Val_AAC1. Our experiments showed that the tRNA^Val_AAC1 transcript was slowly methylated by Trm11-Trm112. However, position 10 in this tRNA was reported to be unmodified G. We purified tRNA^Val_AAC1 from wild-type and trm11 gene deletion strains and confirmed that a portion of tRNA^Val_AAC1 is methylated by Trm11-Trm112 in S. cerevisiae. Thus, our study explains the m²G10 modification pattern of all S. cerevisiae class I tRNAs and elucidates the Trm11-Trm112 binding sites.

RNA modifications as emerging therapeutic targets

The field of epitranscriptome, posttranscriptional modifications to RNAs, is still growing up and has presented substantial evidences for the role of RNA modifications in diseases. In terms of new drug development, RNA modifications have a great promise for therapy. For example, more than 170 type of modifications exist in various types of RNAs. Regulatory genes and their roles in critical biological process have been identified and they are associated with several diseases. Current data, for example, identification of inhibitors targeting RNA modifications regulatory genes, strongly support the idea that RNA modifications have potential as emerging therapeutic targets. Therefore, in this review, RNA modifications and regulatory genes were comprehensively documented in terms of drug development by summarizing the findings from previous studies. It was discussed how RNA modifications or regulatory genes can be targeted by altering molecular mechanisms. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Processing > RNA Editing and Modification.

THUMPD3-TRMT112 is a m2G methyltransferase working on a broad range of tRNA substrates

Post-transcriptional modifications affect tRNA biology and are closely associated with human diseases. However, progress on the functional analysis of tRNA modifications in metazoans has been slow because of the difficulty in identifying modifying enzymes. For example, the biogenesis and function of the prevalent N2-methylguanosine (m2G) at the sixth position of tRNAs in eukaryotes has long remained enigmatic. Herein, using a reverse genetics approach coupled with RNA-mass spectrometry, we identified that THUMP domain-containing protein 3 (THUMPD3) is responsible for tRNA: m2G6 formation in human cells. However, THUMPD3 alone could not modify tRNAs. Instead, multifunctional methyltransferase subunit TRM112-like protein (TRMT112) interacts with THUMPD3 to activate its methyltransferase activity. In the in vitro enzymatic assay system, THUMPD3-TRMT112 could methylate all the 26 tested G6-containing human cytoplasmic tRNAs by recognizing the characteristic 3'-CCA of mature tRNAs. We also showed that m2G7 of tRNATrp was introduced by THUMPD3-TRMT112. Furthermore, THUMPD3 is widely expressed in mouse tissues, with an extremely high level in the testis. THUMPD3-knockout cells exhibited impaired global protein synthesis and reduced growth. Our data highlight the significance of the tRNA: m2G6/7 modification and pave a way for further studies of the role of m2G in sperm tRNA derived fragments.

BERT-m7G: A Transformer Architecture Based on BERT and Stacking Ensemble to Identify RNA N7-Methylguanosine Sites from Sequence Information

As one of the most prevalent posttranscriptional modifications of RNA, N7-methylguanosine (m7G) plays an essential role in the regulation of gene expression. Accurate identification of m7G sites in the transcriptome is invaluable for better revealing their potential functional mechanisms. Although high-throughput experimental methods can locate m7G sites precisely, they are overpriced and time-consuming. Hence, it is imperative to design an efficient computational method that can accurately identify the m7G sites. In this study, we propose a novel method via incorporating BERT-based multilingual model in bioinformatics to represent the information of RNA sequences. Firstly, we treat RNA sequences as natural sentences and then employ bidirectional encoder representations from transformers (BERT) model to transform them into fixed-length numerical matrices. Secondly, a feature selection scheme based on the elastic net method is constructed to eliminate redundant features and retain important features. Finally, the selected feature subset is input into a stacking ensemble classifier to predict m7G sites, and the hyperparameters of the classifier are tuned with tree-structured Parzen estimator (TPE) approach. By 10-fold cross-validation, the performance of BERT-m7G is measured with an ACC of 95.48% and an MCC of 0.9100. The experimental results indicate that the proposed method significantly outperforms state-of-the-art prediction methods in the identification of m7G modifications.

The occurrence order and cross-talk of different tRNA modifications

Chemical modifications expand the composition of RNA molecules from four standard nucleosides to over 160 modified nucleosides, which greatly increase the complexity and utility of RNAs. Transfer RNAs (tRNAs) are the most heavily modified cellular RNA molecules and contain the largest variety of modifications. Modification of tRNAs is pivotal for protein synthesis and also precisely regulates the noncanonical functions of tRNAs. Defects in tRNA modifications lead to numerous human diseases. Up to now, more than 100 types of modifications have been found in tRNAs. Intriguingly, some modifications occur widely on all tRNAs, while others only occur on a subgroup of tRNAs or even only a specific tRNA. The modification frequency of each tRNA is approximately 7% to 25%, with 5-20 modification sites present on each tRNA. The occurrence and modulation of tRNA modifications are specifically noticeable as plenty of interplays among different sites and modifications have been discovered. In particular, tRNA modifications are responsive to environmental changes, indicating their dynamic and highly organized nature. In this review, we summarized the known occurrence order, cross-talk, and cooperativity of tRNA modifications.

Multi-omics integration of methyltransferase-like protein family reveals clinical outcomes and functional signatures in human cancer

Human methyltransferase-like (METTL) proteins transfer methyl groups to nucleic acids, proteins, lipids, and other small molecules, subsequently playing important roles in various cellular processes. In this study, we performed integrated genomic, transcriptomic, proteomic, and clinicopathological analyses of 34 METTLs in a large cohort of primary tumor and cell line data. We identified a subset of METTL genes, notably METTL1, METTL7B, and NTMT1, with high frequencies of genomic amplification and/or up-regulation at both the mRNA and protein levels in a spectrum of human cancers. Higher METTL1 expression was associated with high-grade tumors and poor disease prognosis. Loss-of-function analysis in tumor cell lines indicated the biological importance of METTL1, an m⁷G methyltransferase, in cancer cell growth and survival. Furthermore, functional annotation and pathway analysis of METTL1-associated proteins revealed that, in addition to the METTL1 cofactor WDR4, RNA regulators and DNA packaging complexes may be functionally interconnected with METTL1 in human cancer. Finally, we generated a crystal structure model of the METTL1–WDR4 heterodimeric complex that might aid in understanding the key functional residues. Our results provide new information for further functional study of some METTL alterations in human cancer and might lead to the development of small inhibitors that target cancer-promoting METTLs.

RNA methylation in mammalian development and cancer

Similar to epigenetic DNA and histone modifications, epitranscriptomic modifications (RNA modifications) have emerged as crucial regulators in temporal and spatial gene expression during eukaryotic development. To date, over 170 diverse types of chemical modifications have been identified upon RNA nucleobases. Some of these post-synthesized modifications can be reversibly installed, removed, and decoded by their specific cellular components and play critical roles in different biological processes. Accordingly, dysregulation of RNA modification effectors is tightly orchestrated with developmental processes. Here, we particularly focus on three well-studied RNA modifications, including N⁶-methyladenosine (m⁶A), 5-methylcytosine (m⁵C), and N¹-methyladenosine (m¹A), and summarize recent knowledge of underlying mechanisms and critical roles of these RNA modifications in stem cell fate determination, embryonic development, and cancer progression, providing a better understanding of the whole association between epitranscriptomic regulation and mammalian development.

Epitranscriptomics: A New Layer of microRNA Regulation in Cancer

MicroRNAs are pervasive regulators of gene expression at the post-transcriptional level in metazoan, playing key roles in several physiological and pathological processes. Accordingly, these small non-coding RNAs are also involved in cancer development and progression. Furthermore, miRNAs represent valuable diagnostic and prognostic biomarkers in malignancies. In the last twenty years, the role of RNA modifications in fine-tuning gene expressions at several levels has been unraveled. All RNA species may undergo post-transcriptional modifications, collectively referred to as epitranscriptomic modifications, which, in many instances, affect RNA molecule properties. miRNAs are not an exception, in this respect, and they have been shown to undergo several post-transcriptional modifications. In this review, we will summarize the recent findings concerning miRNA epitranscriptomic modifications, focusing on their potential role in cancer development and progression.

m7G Methyltransferase METTL1 Promotes Post-ischemic Angiogenesis via Promoting VEGFA mRNA Translation

Post-transcriptional modifications play pivotal roles in various pathological processes and ischemic disorders. However, the role of N7-methylguanosine (m7G), particularly m7G in mRNA, on post-ischemic angiogenesis remains largely unknown. Here, we identified that methyltransferase like 1 (METTL1) was a critical candidate responsible for a global decrease of m7G within mRNA from the ischemic tissues. The in vivo gene transfer of METTL1 improved blood flow recovery and increased angiogenesis with enhanced mRNA m7G upon post-ischemic injury. Increased METTL1 expression using plasmid transfection in vitro promoted HUVECs proliferation, migration, and tube formation with a global increase of m7G in mRNA. Mechanistically, METTL1 promoted VEGFA mRNA translation in an m7G methylation-dependent manner. Our findings emphasize a critical link between mRNA m7G and ischemia and provide a novel insight of targeting METTL1 in the therapeutic angiogenesis for ischemic disorders, including peripheral arterial disease.

m7GDisAI: N7-methylguanosine (m7G) sites and diseases associations inference based on heterogeneous network

Background

Recent studies have confirmed that N7-methylguanosine (m⁷G) modification plays an important role in regulating various biological processes and has associations with multiple diseases. Wet-lab experiments are cost and time ineffective for the identification of disease-associated m⁷G sites. To date, tens of thousands of m⁷G sites have been identified by high-throughput sequencing approaches and the information is publicly available in bioinformatics databases, which can be leveraged to predict potential disease-associated m⁷G sites using a computational perspective. Thus, computational methods for m⁷G-disease association prediction are urgently needed, but none are currently available at present.

Results

To fill this gap, we collected association information between m⁷G sites and diseases, genomic information of m⁷G sites, and phenotypic information of diseases from different databases to build an m⁷G-disease association dataset. To infer potential disease-associated m⁷G sites, we then proposed a heterogeneous network-based model, m⁷G Sites and Diseases Associations Inference (m⁷GDisAI) model. m⁷GDisAI predicts the potential disease-associated m⁷G sites by applying a matrix decomposition method on heterogeneous networks which integrate comprehensive similarity information of m⁷G sites and diseases. To evaluate the prediction performance, 10 runs of tenfold cross validation were first conducted, and m⁷GDisAI got the highest AUC of 0.740(± 0.0024). Then global and local leave-one-out cross validation (LOOCV) experiments were implemented to evaluate the model’s accuracy in global and local situations respectively. AUC of 0.769 was achieved in global LOOCV, while 0.635 in local LOOCV. A case study was finally conducted to identify the most promising ovarian cancer-related m⁷G sites for further functional analysis. Gene Ontology (GO) enrichment analysis was performed to explore the complex associations between host gene of m⁷G sites and GO terms. The results showed that m⁷GDisAI identified disease-associated m⁷G sites and their host genes are consistently related to the pathogenesis of ovarian cancer, which may provide some clues for pathogenesis of diseases.

Conclusion

The m⁷GDisAI web server can be accessed at http://180.208.58.66/m7GDisAI/, which provides a user-friendly interface to query disease associated m⁷G. The list of top 20 m⁷G sites predicted to be associted with 177 diseases can be achieved. Furthermore, detailed information about specific m⁷G sites and diseases are also shown.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04007-9.

Roles of RNA Methylation on Tumor Immunity and Clinical Implications

RNA methylation is a kind of RNA modification that exists widely in eukaryotes and prokaryotes. RNA methylation occurs not only in mRNA but also in ncRNA. According to the different sites of methylation, RNA methylation includes m⁶A, m⁵C, m⁷G, and 2-O-methylation modifications. Modifications affect the splicing, nucleation, stability and immunogenicity of RNA. RNA methylation is involved in many physiological and pathological processes. In the immune system, especially for tumor immunity, RNA methylation affects the maturation and response function of immune cells. Through the influence of RNA immunogenicity and innate immune components, modifications regulate the innate immunity of the body. Some recent studies verified that RNA methylation can regulate tumor immunity, which also provides a new idea for the future of treating immunological diseases and tumor immunotherapy.

Epitranscriptome machinery in Trypanosomatids: New players on the table?

Trypanosoma and Leishmania parasites cause devastating tropical diseases resulting in serious global health consequences. These organisms have complex life cycles with mammalian hosts and insect vectors. The parasites must, therefore, survive in different environments, demanding rapid physiological and metabolic changes. These responses depend upon regulation of gene expression, which primarily occurs posttranscriptionally. Altering the composition or conformation of RNA through nucleotide modifications is one posttranscriptional mechanism of regulating RNA fate and function, and modifications including N6-methyladenosine (m6A), N1-methyladenosine (m1A), N5-methylcytidine (m5C), N4-acetylcytidine (ac4C), and pseudouridine (Ψ), dynamically regulate RNA stability and translation in diverse organisms. Little is known about RNA modifications and their machinery in Trypanosomatids, but we hypothesize that they regulate parasite gene expression and are vital for survival. Here, we identified Trypanosomatid homologs for writers of m1A, m5C, ac4C, and Ψ and analyze their evolutionary relationships. We systematically review the evidence for their functions and assess their potential use as therapeutic targets. This work provides new insights into the roles of these proteins in Trypanosomatid parasite biology and treatment of the diseases they cause and illustrates that Trypanosomatids provide an excellent model system to study RNA modifications, their molecular, cellular, and biological consequences, and their regulation and interplay.

Post-Transcriptional Modifications of Conserved Nucleotides in the T-Loop of tRNA: A Tale of Functional Convergent Evolution

The high conservation of nucleotides of the T-loop, including their chemical identity, are hallmarks of tRNAs from organisms belonging to the three Domains of Life. These structural characteristics allow the T-loop to adopt a peculiar intraloop conformation able to interact specifically with other conserved residues of the D-loop, which ultimately folds the mature tRNA in a unique functional canonical L-shaped architecture. Paradoxically, despite the high conservation of modified nucleotides in the T-loop, enzymes catalyzing their formation depend mostly on the considered organism, attesting for an independent but convergent evolution of the post-transcriptional modification processes. The driving force behind this is the preservation of a native conformation of the tRNA elbow that underlies the various interactions of tRNA molecules with different cellular components.

Distinct evolutionary pathways for the synthesis and function of tRNA modifications

Transfer ribonucleicacids (RNAs) (tRNAs) are essential adaptor molecules for translation. The functions and stability of tRNAs are modulated by their post-transcriptional modifications (tRNA modifications). Each domain of life has a specific set of modifications that include ones shared in multiple domains and ones specific to a domain. In some cases, different tRNA modifications across domains have similar functions to each other. Recent studies uncovered that distinct enzymes synthesize the same modification in different organisms, suggesting that such modifications are acquired through independent evolution. In this short review, I outline the mechanisms by which various modifications contribute to tRNA function, including modulation of decoding and tRNA stability, using recent findings. I also focus on modifications that are synthesized by distinct biosynthetic pathways.