Mouse embryonic stem cells (mESCs) can adopt naïve, ground, and paused pluripotent states that give rise to unique transcriptomes. Here, we use transient transcriptome sequencing (TT‐seq) to define both coding and non‐coding transcription units (TUs) in these three pluripotent states and combine TT‐seq with RNA polymerase II occupancy profiling to unravel the kinetics of RNA metabolism genome‐wide. Compared to the naïve state (serum), RNA synthesis and turnover rates are globally reduced in the ground state (2i) and the paused state (mTORi). The global reduction in RNA synthesis goes along with a genome‐wide decrease of polymerase elongation velocity, which is related to epigenomic features and alterations in the Pol II termination window. Our data suggest that transcription activity is the main determinant of steady state mRNA levels in the naïve state and that genome‐wide changes in transcription kinetics invoke ground and paused pluripotent states. Genome‐wide analyses of RNA metabolism kinetics in naïve, ground and paused pluripotent mouse embryonic stem cells (mESCs) indicate that the three states show markedly different global transcriptional kinetics. In vitro cultures of mESC provide facile model systems for understanding the molecular underpinnings of pluripotency and self‐renewal. The 2i‐induced ground state resembles naïve pre‐implantation E4.5 epiblast cells  II ChIP to estimate elongation velocities at the level of TUs. Our results led to a description of RNA metabolism in three states of pluripotent cells and to several unexpected findings.Although most studies have focused on rewiring of regulatory circuits, activating, or disengaging individual enhancers and transcripts, globally reduced transcription activity in 2i and mTORi states was suggested by cell‐level 5‐Ethynyl Uridine (EU) incorporation  for 5 min and performed the TT‐seq method as previously described  was developed offering previously described algorithms for TU discovery , downstream sense (dsRNA), and downstream antisense (daRNA) TUs , which suggests that mRNA abundance change is dominantly regulated via RNA synthesis. Further with length‐normalized read counts (log‐RPK), mRNA, intergenic RNA, and asRNA‐labeled RNA levels correlated well with their total RNA levels  followed by a GRO‐seq time course  site/TES, allowing identification of transcription termination sites (TTSs)  were associated with faster estimated termination/gene body velocity, higher histone H3K36me3 occupancy, and longer gene lengths. In contrast, short termination distances (< 3 kb) were associated with histone H3.3, Chd8, Chd1 enrichment, and GC content in the called termination window Fig . A totalTo understand if altered elongation velocity in 2i and mTORi conditions also affected termination distance, we estimated termination sites in 2i and mTORi condition analogous to above, and found that slower elongation in 2i/mTORi coincided with shorter median termination distances: SL 5.1 kb, 2i 4.6 kb, and mTORi 3.9 kb. Termination sites in 2i and mTORi cells were mainly located within 5‐kb downstream of the TES Fig . Shorteret al, Regardless of whether or not the termination sites shifted, a sharp local maximum in G/C richness was observed around both the old and new termination sites Fig . This reet al, et al, et al, et al, et al, With the assistance of quantitative techniques, we find that related pluripotent states of mouse embryonic stem cells exhibit markedly different global transcriptional kinetics. A global reduction of transcription serves to attenuate cell growth in the paused state  transcription accelerates and expression increases in 2i state in line with its known function promoting mESC proliferation and self‐renewal  or mTORi (INK128) decreases global transcriptional output, total RNA abundance, RNA turnover, RNA polymerase II elongation velocity, and termination distance.Mouse embryonic stem cell RW4  were cultured in 0.1% gelatin‐coated dishes with serum medium: Knockout DMEM medium with 15% FBS , 0.1 mM ESGRO LIF , 2 mM GlutaMAX , 0.1 mM Non‐Essential Amino Acid , 0.1 mM β‐mercaptoethanol ; 2i medium: ESGRO Complete Basal Medium , 3 μM GSK3β inhibitor CHIR99021 , 1 μM Mek 1/2 inhibitor PD0325901 , 0.1 mM LIF. Inhibition of mTOR was in serum‐LIF (SL) medium supplemented with 200 nM INK128 (CAYM11811‐1).et al, et al, TT‐seq labeling steps were performed as described before  by STAR 2.7.3a with setting:‐‐outFilterMismatchNoverReadLmax 0.02 ‐‐outFilterMultimapScoreRange 0 ‐‐alignEndsType EndToEnd.et al, Mapped reads were subjected to transcription unit annotation as described before   cell lysis to eliminate technical errors retained during the steps of biotinylating, RNA purification, and library preparation.de novo annotated ncTUs and spike‐in transcripts using the alignment‐free mapper . For normalization, we calculated the size factors of all spike‐ins in the total transcriptome and labeled spike‐ins  in the labeled transcriptome according to DESeq's method  and total (XF) read counts in response to the respective label rates r: log2(r) ~ XF + XL. The evaluation was performed with five‐fold cross‐validation with 10 times subsampling, and the predictions on the hold‐on set were examined. Then, a final model with spike‐in counts of all samples was trained, which resulted in an adjusted R‐squared 0.9917. The labeling rate was predicted from transcript's labeled/total read counts. RNA half‐life was extrapolated from the predicted labeled rate in 5 min to replacing half of the current total RNA amount. In the same way, a second model was trained to predict all spike‐in weights per cell w: log2(w) ~ XF + XL, which resulted in an adjusted R‐squared 0.991. Then, the copy number per cell was transformed from weight with transcript effective length from kallisto. Finally, RNA synthesis rate  was calculated by multiplying labeled rate and transcript copy number.The estimated labeled/total RNA read counts of GENCODE transcripts and spike‐ins from kallisto were first normalized by respective spike‐ins sizes from each sample. The gene‐level estimated counts took the sum of transcript variants. A linear model was trained only with the normalized labeled spike‐ins  log6 cells were collected and samples were processed according to the MINUTE‐ChIP workflow . Subsequently, dsDNA adaptors  were ligated onto the fragmented chromatin in the same pot supplemented with blunting and ligation reagents. Barcoded samples were then pooled and aliquoted for ChIP (saving 5% as Input) for 4 h at 4°C with Protein G magnetic beads  pre‐coupled with 5 µg of RNA Pol II S5p antibody . Next, ChIP and input DNA were purified and final libraries were generated through sequential steps of in vitro transcription, RNA 3′ adapter ligation, reverse transcription, and PCR amplification. Post library cleanup and quality assessment, libraries were combined at 4‐nM concentration and sequenced on the Illumina NextSeq500 platform.For each growth condition , three pellets of 1 × 10Short reads genome coverage was processed with R/Bioconductor packages “rtracklayer”, “bamsignals”, and “rsamtools” by piling up only the uniquely mapped and paired‐end reads with insertion size < 2 kb. Sample‐wise coverage of each genomic interval was normalized by the respective size factors. For TT‐seq sample size factors were generated from spike‐in RNA read counts; for MINUTE‐ChIP samples, scaling factors were from the respective ChIP and input sizes after combining sample replicates. In the heatmaps and coverage profile plots, each coverage vector of different lengths was resized by “spline” function to the same number of positions.et al, et al, q 0.01” cutoff, and overlapped on the ternary plot of the combined intergenic TUs .RNA polymerases ChIP‐seq datasets  on the sense strand. CpG number was by CG dinucleotide, TATA number was by the “TATA” pattern. For counting ChIP‐seq signal density, TSS intervals (−500 to 500 bp) and Ensembl (GRCm38.79) gene body intervals were used, with the samples as below:R2, by a multivariate linear regression through the origin.Log1p‐transformed ChIP read densities were trimmed at 99.5% quantile to remove technical outliers, and standardized to normal distribution N. Each feature explaining a particular response  and their cumulative sums from the beginning to the end of the potential termination window were calculated. The maximum position was defined as the termination site Fig .n = 8,348). The trained model of predicting numerical termination distance was applied to the test set, and compared to the actual distances  to evaluate the non‐linear response of termination distance with 41 chromatin features. The model training control was 10‐fold cross‐validation, and with the tuned depth 40 and 1,000 trees under 0.1 shrinkage. Test set was split by the ratio 0.2 of total cases (nces Fig . Featurences Fig . We evalnces Fig .Rui Shao: Conceptualization; Resources; Data curation; Software; Formal analysis; Validation; Investigation; Visualization; Methodology; Writing – original draft; Writing – review & editing. Banushree Kumar: Investigation; Methodology; Writing – original draft; Writing – review & editing. Katja Liedschreiber: Supervision; Investigation; Methodology. Michael Lidschreiber: Conceptualization; Data curation; Formal analysis; Supervision; Validation; Methodology; Writing – original draft; Writing – review & editing. Patrick Cramer: Conceptualization; Supervision; Funding acquisition; Validation; Project administration; Writing – review & editing. Simon J Elsässer: Conceptualization; Data curation; Supervision; Funding acquisition; Investigation; Methodology; Writing – original draft; Project administration; Writing – review & editing.CRediT author contributions listed above, the contributions in detail are:In addition to the RS and SJE conceived study. RS performed TT‐seq and BK performed MINUTE‐ChIP experiments. RS analyzed all data. KL, ML, and PC advised on TT‐seq. RS generated figures. RS and SJE wrote the manuscript with input from all authors.The author declares that he has no conflict of interest.Expanded View Figures PDFClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.


Oral microbiota can alter cancer susceptibility and progression by modulating metabolism and inflammation. We assessed the association between the oral microbiome and lymph node (LN) metastasis in oral squamous cell carcinoma (OSCC). We collected a total of 54 saliva samples from patients with OSCC before surgery. LN metastasis was assessed based on postoperative pathological examination. We used QIIME2, linear discriminant analysis effect size (LEfSe), and PICRUSt2 methods to analyze microbial dysbiosis. A random forest classifier was used to assess whether the oral microbiome could predict LN metastasis. Among the 54 OSCC samples, 20 had LN metastasis, and 34 had no evidence of metastasis. There was a significant difference in β-diversity between the metastasis and no metastasis groups. Through LEfSe analysis, the metastasis group was enriched in the genera Prevotella, Stomatobaculum, Bifidobacterium, Peptostreptococcaceae, Shuttleworthia and Finegoldia. Pathways related to signal peptidase II were predominant in the no metastasis group. The RF model showed a modestly high accuracy for predicting metastasis. Differences in microbial community composition and functions were observed in the oral microbiome of patients with OSCC with and without LN metastasis. However, the finding that specific taxa may be associated with LN metastasis should be verified in a further prospective study. The presence of LN metastasis is associated with an advanced disease, reducing the overall survival (OS) rate of OSCC patients to 50%2. Although clinical evaluation, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasonography (US), and positron emission tomography (PET)-CT, are used to evaluate LN metastasis of oral cancer before treatment, occult LN metastasis may still occur. Nevertheless, therapeutic neck dissection (ND) is performed in patients with OSCC with clinically positive LN metastasis, but controversy remains as to whether selective ND should be performed in patients without LN metastasis3. Therefore, prediction of LN metastasis in OSCC is important not only for diagnosis, but also for establishing a therapeutic strategy. In this respect, biomarkers associated with metastasis are useful tools for identifying invasion and metastasis, and monitoring treatment response in patients undergoing cancer treatment. Several histopathological parameters, gene expression signatures, and immunohistochemical markers have been reported as biomarkers for predicting LN metastasis in patients with OSCC6. However, most existing methods have limited predictive value, and surgical resection is often required to obtain precise clinical information.Lymph node (LN) metastasis in oral squamous cell carcinoma (OSCC) is observed in 12–50% of patients, depending on the location of the primary carcinoma7. Oral microbiota may play a role in carcinogenesis. The mechanisms by which changes in the human microbiome are involved in the carcinogenesis process include (i) induction of a chronic inflammatory response, (ii) induction of anti-apoptotic signals in cells, (iii) release of carcinogens, and (iv) regulation of anti-cancer immunity8. The mucosal microenvironment is a specific ecological niche. In this system, bacteria can genetically influence the epithelial cells7. Furthermore, the microbiome is accepted as a new component of the tumor microenvironment that impairs tumor cell metabolism by maintaining mucosal barriers or causing inflammation and producing genetic toxins and bacterial metabolites with various functions9. A number of microorganisms have been associated with the promotion of carcinogenesis or metastasis through manipulation of the Wnt signaling pathway, which is associated with the E-cadherin–catenin complex10. E-cadherin and related catenin complexes play an important role in cell adhesion, and misregulated E-cadherin is believed to be a key step in cancer metastasis11. The squamous epithelium of the oral mucosa is constantly exposed to various oral microbes, both of which communicate with each other at the cellular and molecular level12. Thus, there have been several studies over the past decades suggesting a link between oral cancer and oral microbes13. Previous studies have reported that several specific oral microbiomes, such as Streptococcus, Peptostreptococcus, Prevotella, Porphyromonas gingivalis, and Capnocytophaga gingivalis, are strongly associated with oral squamous cell cancer14. A recent meta-analysis showed thar Fusobacterium is more abundant in head and neck cancer (HNC) samples than non-HNC samples, suggesting the possible contribution of Fusobacterium to the carcinogenesis of OSCC15. In particular, Fusobacterium nucleatum may play an important role in tumorigenesis of oral cancer through several mechanisms16.With more than 700 species of microbes, the oral cavity is one of the most densely populated human microbial habitatsAlthough the role of oral microbiota in the pathogenesis of OSCC is becoming more evident, little is known about its association with the occurrence of metastasis. We hypothesized that the oral microbiome is related to metastasis in OSCC patients. Using saliva samples, we compared the abundance of microbial taxa and functional diversity between OSCC patients with and without LN metastasis.Among the 54 OSCC patients, 20 had LN metastasis in the postoperative pathologic report, and 34 had no evidence of metastasis. There were no significant differences in age, history of smoking, and denture use between the two groups. The proportion of male patients and the number of patients with a higher T stage in the LNM group was higher than in the non-LNM group Table .Table 1Bp = 0.75), Shannon entropy (p = 0.75), chao1index (p = 0.39) or the pielou evenness index (p = 0.63). . Fig. A–D.FigurOral bacterial communities were explored at genus levels in the patient samples . Frequent genera were Streptococcus, Neisseria, Capnocytophaga, Rothia, Prevotella, Veillonella, Enterococcus, Haemophilus and Peptostreptococcus. Figure When comparing the relative abundance at the genus level between the two groups, it was observed that Streptococcus, Rothia, Prevotella and Peptostreptococcus increased, but Neisseria and Haemophilus decreased in the metastasis group Fig. .Figure 4We performed LEfSe analysis at the genus levels, resulting in 30 differential features between patients in metastasis and no metastasis groups. The genera Prevotella, Stomatobaculum, Bifidobacterium, Peptostreptococcaceae, Shuttleworthia and Finegoldia were enriched in patients with metastasis. The genus Tannerella and Fusobacterium were dominant in patients without metastasis. A cladogram representing the differential phylogenic taxonomic classifications Fig. A with a 17. Pathways with a significant difference in mean proportions between the metastasis and no metastasis groups were identified. Pathways related to iron (III)-enterobactin esterase (p = 0.001) and toxin FitB (p = 0.002) were over-represented in the LNM group, whereas signal peptidase II (p < 0.001), DNA gyrase subunit A (p = 0.018), subunit B (p = 0.025), and methyonyl aminopeptidase (p = 0.026) were under-represented  orthologsThe random forest classifier machine learning technique was used to predict whether a sample was from a patient with metastasis. The estimator parameter, which adjusts the number of trees grown by the ensemble estimators, was 200. The sample dataset was partitioned at 80:20 ratio, using 80% of the data to train the machine learning algorithms and the remaining 20% for the independent testing. We performed k-fold cross- validation (k = 5) during training of the machine learning algorithm. We presented classification accuracy results in the form of a confusion matrix, as well as receiver operating characteristic (ROC) curves. The confusion matrix shows that the overall accuracy of prediction is 86.3%, indicating a moderately high predictive accuracy Fig. A. ROC anWe compared the oral microbiome of saliva samples obtained before surgery from patients with or without LN metastasis. In the diversity analysis between metastasis and no metastasis groups, there was a statistically significant difference in the β-diversity, which represents the difference in the composition of the oral bacterial community in the two groups. Several genera were differentially identified in patients with metastasis. To our knowledge, this is the first study to identify an association between the oral microbiome and LN metastasis in OSCC.18. Second, activation of NF-κB and inhibition of cell death by the oral microbiome can affect cell proliferation and cause carcinogenesis19. Finally, bacteria can produce metabolites that affect carcinogenic pathways20. Among the mechanisms that influence the tumor microenvironment, the main pathway of signaling between the microbiome and host is the production of bacterial metabolites. Fusobacterium nucleatum is known to be involved in the metastasis process and is predicted to activate β-catenin signaling through lipopolysaccharide (LPS). It has been reported that overexpression of β -catenin, and the oncogenes C-myc and cyclin D1, are involved in metastasis22. Miko et al. demonstrated that lithocholic acid (LCA), a secondary bile acid produced exclusively by bacteria, inhibits epithelial-mesenchymal transition (EMT) and vascular endothelial growth factor A (VEGFA) expression and boosts antitumor immunity in a breast cancer cell line23. However, there is a lack of research on the oral microbiome and its association with metastasis in oral cancer.Three theories have been proposed to explain the mechanisms of oral microflora in carcinogenesis. First, inflammatory mediators generated through chronic inflammation induced by bacteria can induce or promote cell proliferation, mutagenesis, oncogene activation, and angiogenesis25. In particular, Prevotella inermedia produces methyl mercaptan, which is involved in activation stress and DNA damage in oral cells. This methyl mercaptan is involved in angiogenesis and invasiveness of oral cancer cell through type 4 collagen cleavage26. Stomatobaculum is a genus of anaerobic bacteria of the Lachnospiraceae family, and Stomatobaculum longum belongs to this genus. Stomatobaculum longum is usually isolated from dental plaques27. Interestingly, in previous studies, Stomatobaculum were reported to be significantly abundant in oral swab samples of control patients compared to those with oral cancer28. However, according to our results, an increase in these taxa may be associated with metastasis in OSCC. Previous studies have suggested that the role of the oral microbiome in the aggressiveness of cancer in oral cancer is the induction of inflammation. It has been reported that infection with Porphyromonas gingivalis induces EMT by downregulation of E-cadherin and nuclear cytoplasmic accumulation of β-catenin, which induces aggressive and/or metastatic potential in OSCC29. However, in this study, no significant difference in abundance of Porphyromonas gingivalis was observed between the two groups. It has been demonstrated that Bifidobacterium can modulate host immunity and immune responses, alleviate allergic symptoms, treat inflammatory diseases, and have synergistic effects on tumor immunotherapy31. However, in this study, Bifidobacterium were significantly increased in the saliva of patients with metastatic oral cancer.Therefore, we hypothesized that oral microbiota related to oral cancer metastasis could be found through differences in oral microbial communities in OSCC patients. We analyzed whether metastasis in OSCC could be predicted through the composition of the oral microbiome using a machine-learning model (RF). Modest predictive performance was achieved using taxonomy features. LEfSe analysis indicated that there were 30 features differentiating patients with LN metastasis from patients without it. The genera Prevotella, Stomatobaculum, Bifidobacterium, and Fusobacterium were the most prevalent differentiating features between with and without metastasis groups. Prevotella, a gram-negative bacteria associated with the pathogenesis of periodontitis, has been reported to be detected in OSCC32. Future studies are required to determine this enzyme's role in metastasis.To characterize the functional role of the oral microbiota in metastasis, we annotated the taxa using the Kyoto Gene and Genome Encyclopedia (KEGG) database. The pathway that showed the most significant difference between the two groups was signal peptidase II. Signal peptidase II is present in bacterial inner membranes and releases signal peptides from murein prolipoproteins and other bacterial membrane proteins. It serves as a potential target for the development of antibacterial agents with a novel mode of action, as it cleaves off the signal peptide from secreted proteins, making it essential for protein secretion, and hence for bacterial viability33. Other clinicopathologic factors have been reported to predict nodal disease, including sex, tumor location, histologic grade, pattern of invasion, and perineural and lymphovascular invasion34. In addition to the clinicopathological factors, this study showed that the composition of the oral microbiome could be another predictive factor for OSCC metastasis. While many existing studies have focused on the role of the microbiome in carcinogenesis of oral cancer, this is the first study to show that there is a change in the oral microbiome after metastasis. However, our study has some limitations. First, this study had a small sample size. Another limitation is that since this study investigated samples from patients of a single ethnicity and from a single geographical site, the results may not be generalizable to other groups, especially considering differences in the oral microbiome based on dietary habits. In addition, 16s rRNA amplicon sequencing may have limitations in identifying activated bacteria associated with metastasis in saliva. Amplicon sequencing can be applied to low-biomass specimens or samples contaminated by host DNA. Many species of oral bacteria are identical along the full length of the 16s rRNA gene, and in principle it therefore is impossible to distinguish all bacterial species using that gene. In general, 16s rRNA amplicon sequencing allows genus-level resolution for most bacterial taxa, but difficult species resolution35. Metatranscriptomics or whole-genome shotgun (WGS) sequencing analysis will provide more information for the determination of functionally relevant salivary microbiome. Finally, we did not consider sufficient information about oral health conditions that could affect the oral microbiome of the participants. Factors related to the wearing of the prosthesis were considered, but the influence of other oral health factors could not be excluded.The strength of this study is that it evaluated potential oral microbiome biomarkers for the prediction of LNM in OSCC patients through various analyses. The depth of invasion (DOI) of OSCC is the most commonly used histopathologic variable for predicting occult nodal metastasisWe uncovered differentiated microbial communities and functions following LN metastasis in patients with OSCC undergoing surgery. It was confirmed that Prevotella, Stomatobaculum, Bifidobacterium were differential features of saliva microbiomes at the genus level in patients with LN metastasis. On the other hand, Fusobacterium were decreased in patients with LN metastasis. Additionally, LN metastasis can be predicted using machine-learning techniques based on oral microbiome features. However, based on the results of this preliminary study, mechanism study and large-scale sample analyses are required to validate the association between the oral microbiome and metastasis.This study was approved by the Institutional Review Board of Kyung Hee University Hospital at Gangdong (IRB No: KHNMC 2018-06-023). All participants gave written, informed consent for their participation. Informed consent was obtained according to the Declaration of Helsinki. Between 2018 and 2019, 54 saliva samples were collected before surgery. Patients newly diagnosed with OSCC were enrolled in this study. All participants did not take any oral antibiotics at the time of saliva sample collection other than prophylactic intravenous antibiotics just before surgery. Briefly, OSCC patients were instructed to refrain from oral hygiene procedures such as brushing their teeth, eating, drinking, or smoking for a minimum of two hours prior to saliva sample collection. At least 1 mL of saliva was accumulated on the bottom of the oral cavity and then spit naturally into specimen tubes for collection. Each sample tube was immediately sealed and stored at − 80 °C until analysis.36. After incubation, the samples were centrifuged at 8000 rpm to harvest pellets. Genomic DNA was extracted from the pellets using the DNeasy® PowerSoil Pro kit  according to the manufacturer’s instructions. Finally, the extracted DNA was eluted in autoclaved distilled water, and the yield was measured using a NanoDrop™ 2000 spectrophotometer .Frozen saliva samples were thawed at room temperature for 1 h and subsequently incubated at 37 °C with shaking for 1 h after adding 1M dithiothreitol  to a final concentration of 65 mM, allowing bacterial cells to be released from the mucus portion of saliva samplesGenomic DNA from the saliva samples was used for profiling the oral microbial community through 16 s rRNA amplicon sequencing on the Illumina MiSeq platform. Primer combinations of 341 F (5-TCGTCGGCAGCGTC-AGATGTGTATAAGAGACAG-CCTACGGGNGGCWGCAG-3) and 805R (5-GTCTCGTGGGCTCGG-AGATGTGTATAAGAGACAG-GACTACHVGGGTATCTAATCC-3) targeting the V3-V4 hypervariable regions of the 16s rRNA gene were used to construct the 16s rRNA amplicon from the extracted DNA with Premix Taq™ (Ex Taq Version 2.0) . Subsequently, the amplicons were purified using a QIAquick PCR Purification Kit (Qiagen). The purified amplicons were amplified with unique pairs of i5 forward index (5-AATGATACGGCGACCACCGAGATCTACAC-[i5]-TCGTCGGCAGCGTC-3) and i7 reverse index (5-CAAGCAGAAGACGGCATACGAGAT-[i7]-GTCTCGTGGGCTCGG-3) primers provided by the Nextera XT Index kit . Each set of PCRs included extraction and PCR blanks. Both [i5] and [i7] are the 8 bp sequences of unique barcodes, which allowed the pooled libraries to be sequenced simultaneously, known as sample multiplexing. The index barcoded amplicons were purified using a QIAquick PCR Purification kit  and quantified using a NanoDrop™ 2000 spectrophotometer . Equal amounts of each purified amplicon containing barcode were pooled to construct libraries. Libraries were sequenced on the Illumina MiSeq platform using the 2 × 300 bp paired-end protocol.https://qiime2.org/) (version 2020.6) and phyloseq R package (https://joey711.github.io/phyloseq/)37. Raw sequence data were denoised using DADA2 in the QIIME 2 package38. The paired-end FASTQ files were processed by Phred quality score-based quality filtering, merging of the paired ends, chimera removal, singleton removal, and construction of a feature table consisting of amplicon sequence variants (ASV). Using DADA2 denoise-single method, we removed low quality regions of the sequences. We set the truncating sequence option to 260. All ASV were aligned to mafft through q2 alignment, and phylogenetic analysis was performed using fasttree2. A scikitlearn naïve Bayes machinelearning taxonomy classifier against the eHOMD 16S rRNA RefSeq Version 15.1 reference sequences was trained with the q2featureclassifier plugin using the V3V4 regions of 16S rRNA sequences39. Taxonomy was assigned to each ASV using the ‘classifysklearn’ command in q2featureclassifier. Using the constructed rooted phylogenic tree, we performed diversity analysis using the diversity plugin within the QIIME 2 package. The α-diversity and β-diversity were calculated between groups using the Kruskal–Wallis pairwise test and permutational multivariate analysis of variance, respectively. We analyzed differential microbial abundance between the two groups with Linear discriminant analysis Effect Size (LEfSe) in the Galaxy web application 40. The alpha value for the factorial Kruskal–Wallis test among classes was 0.5, and the threshold on the logarithmic LDA score for discriminative features was 3.0. The strategy for multiclass analysis was all-against-all, which is the relatively stricter option. Based on the LEfSe results, with a threshold of |log10 LDA|≥ 2, a cladogram was plotted. Functional metagenomes were predicted based on the 16S rRNA sequencing data of the salivary microbiome using PICRUSt2 (phylogenetic investigation of communities by reconstruction of unobserved states) v2.0.3 (https://github.com/picrust/picrust2)41. We also performed KO (KEGG Orthology) pathway prediction using metagenome data.Raw sequence data were analyzed using the QIIME 2 pipeline (Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Gene fusions play a significant role in cancer etiology, making their detection crucial for accurate diagnosis, prognosis, and determining therapeutic targets. Current diagnostic methods largely focus on either targeted or low-resolution genome-wide techniques, which may be unable to capture rare events or both fusion partners. We investigate if RNA sequencing can overcome current limitations with traditional diagnostic techniques to identify gene fusion events.We first performed RNA sequencing on a validation cohort of 24 samples with a known gene fusion event, after which a prospective pan-pediatric cancer cohort (n = 244) was tested by RNA sequencing in parallel to existing diagnostic procedures. This cohort included hematologic malignancies, tumors of the CNS, solid tumors, and suspected neoplastic samples. All samples were processed in the routine diagnostic workflow and analyzed for gene fusions using standard-of-care methods and RNA sequencing.We identified a clinically relevant gene fusion in 83 of 244 cases in the prospective cohort. Sixty fusions were detected by both routine diagnostic techniques and RNA sequencing, and one fusion was detected only in routine diagnostics, but an additional 24 fusions were detected solely by RNA sequencing. RNA sequencing, therefore, increased the diagnostic yield by 38%-39%. In addition, RNA sequencing identified both gene partners involved in the gene fusion, in contrast to most routine techniques. For two patients, the newly identified fusion by RNA sequencing resulted in treatment with targeted agents.We show that RNA sequencing is sufficiently robust for gene fusion detection in routine diagnostics of childhood cancers and can make a difference in treatment decisions. It is estimated that gene fusions play a key role in the tumorigenesis and metastasis in 20% of all human cancers.Key ObjectiveWe investigate if whole transcriptome RNA sequencing can overcome current limitations with traditional techniques to identify gene fusion events within a diagnostic setting in a diverse cohort of patients.Knowledge GeneratedClassical gene fusion detection methods are often limited because of resolution or specificity issues. RNA sequencing identified 83 gene fusions in 244 patients, of which 24 were additionally detected in comparison with existing diagnostic procedures (increase of 38%). Several of these additionally detected fusions had added clinical value (prognosis and/or treatment). In addition, data generated by RNA sequencing hold promise for future diagnostic applications, such as expression analysis and tumor classification.RelevanceThe use of gene fusion detection with RNA sequencing can significantly increase the diagnostic yield of gene fusion detection.9 (2) breakpoints may be atypical,13 and (3) fusion partners may be promiscuous.14 Both reverse transcriptase polymerase chain reaction (RT-PCR)–based assays and fluorescence in situ hybridization (FISH) assays using break-apart probes are a sensitive, but time-consuming method to detect chromosomal rearrangements. Furthermore, because of the location of the probe, atypical fusions might be missed.16 Both methods are also targeted by design and lack flexibility to robustly detect rare events and/or novel fusion genes. Genome-wide approaches used in routine diagnostics to detect structural variation include karyotyping and single nucleotide polymorphism (SNP) arrays. Karyotyping, however, has a limited resolution not suitable to detect local rearrangements,18 whereas SNP arrays are unable to detect balanced rearrangements.21 Therefore, for most cancer samples, multiple methods are performed in parallel to detect clinically relevant gene fusions. Most of these challenges and technical limitations are mitigated by targeted RNA sequencing (RNA-seq) approaches, which allow testing of multiple fusion genes and detection of novel fusion partners. Nevertheless, current implementations of targeted RNA-seq are usually based on gene fusion panels, designed to capture a specific set of gene fusion events for a specific tumor type.24Gene fusion detection in a diagnostic setting poses several challenges: (1) specific gene fusions may be rare,26 The impact of these limitations on RNA-seq–based gene fusion detection in cancer diagnostics is currently unknown. Here, we report the clinical implementation of sequencing ribo-depleted RNA for the identification of chimeric fusion transcripts in a prospective heterogeneous cohort of pediatric cancers.Sequencing of total RNA can identify chimeric fusion transcripts genome-wide, providing a potential single assay that maintains the flexibility required to detect rare gene fusion events and atypical breakpoints across a broad variety of tumor types. However, RNA-seq has its own limitations, including technical artifacts that can result in false positives and poor sensitivity for detecting lowly expressed gene fusions or fusions diluted by accompanying noncancerous cells within the sample.27 and annotated using a custom pipeline. The resulting list of potential relevant gene fusions was manually assessed and curated (Data Supplement).We created a prospective cohort of consecutive patients entering our center with a suspected neoplasm. Consent was obtained for diagnostic procedures, and RNA could be extracted for 244 patients. The samples included hematologic tumors (n = 97), solid tumors (n = 55), sarcoma (n = 37), brain tumors (n = 41), and suspected neoplasm (n = 14). In parallel to the RNA-seq, existing diagnostic protocols were applied to the samples on the basis of clinical indication (forming the reference standard), including SNP array, karyotyping, FISH, RT-PCR, and in selected cases, targeted RNA-seq (Data Supplement). RNA-seq was performed on fresh (frozen) tissue or bone marrow. In summary, total RNA was isolated and the generated libraries were sequenced on a NovaSeq 6000 . After aligning the sequence data, gene fusion detection was performed using Star fusion (version 1.6.0)We first screened a small cohort of 24 samples, harboring 25 protein-protein coding fusions to validate and optimize our pipeline (Data Supplement), and then we prospectively sequenced samples in parallel to existing routine diagnostic procedures. In total, we received 261 diagnostic requests. Samples clearly not containing any malignancy were excluded (n = 10), and seven samples were excluded because no RNA could be extracted  but not identified using RNA-seq. Further inspection showed that this fusion transcript was present with a fusion fragments per million < 0.1, and neither COL6A2 nor USP6 were initially on the inclusion list. The fusion was therefore filtered out of the results. Subsequent rectification and reanalysis results in the correct identification of this fusion. In the remaining 23 discordant cases, the RNA-seq workflow identified protein-protein coding fusion transcripts not identified using traditional methods. These RNA-seq–specific gene fusion events were validated when possible by an independent technique (Data Supplement). This equated to a concordance rate > 98% (55 of 56) with traditional diagnostics and an increase in diagnostic yield from 23% (56 of 244) to 32% ; see the Data Supplement for recurrence analysis. Both RNA-seq and routine diagnostics identified 55 of the 78 clinically relevant protein-protein coding fusion events. However, in several cases, traditional methods only identified the clinically relevant 3′ fusion partner and not the 5′ fusion partner. Hence, although the RNA-seq results were concordant with traditional diagnostics, the RNA-seq data provided additional information by identifying the fusion partner. In case PMABM000BJK/27, a EML4-NTRK3 fusion detected by RNA-seq in an infantile fibrosarcoma (IF) sample. Histologically, this case resembled dermatofibrosarcoma protuberans although IF was considered a differential diagnosis. An ETV6 split FISH was requested,29 but yielded negative results because of EML4 being the 5′ fusion partner.30 The KMT2A-AFDN fusion detected in an acute myeloid leukemia sample was readily identified by RNA-seq, but undetected in routine diagnostics, probably because of the low resolution of karyotyping or nonmitosis of leukemic cells , IGH-CEBPB, and IGH-IGF2BP1,33 and a T-cell receptor promoter fusion, TAL1-TRDC.34 In general, promoter and/or enhancer fusions do not result in chimeric transcripts per se and may not be identified by our RNA-seq workflow. However, in four cases, a chimeric transcript between IGH and CASC11 (upstream of MYC), CEBPB, and AC091133.2 (upstream of IGF2BP1) were detected. Both IGH-MYC fusions were also identified by MYC break-apart FISH probes and IGH-IGF2BP1 by karyotyping. We also detected a fusion with the promoter region of TRDC driving expression of TAL1, which was also detected by karyotyping. Including these promoter fusions in our results, the increase in diagnostic yield of RNA-seq compared with traditional methods decreases from 39% (78 of 56) to 38% (83 of 60) since most promoter/enhancer fusions (4/5) were also detected by traditional methods.In addition to the 78 clinically relevant protein-protein fusion events identified by RNA-seq, we found indications for COL3A1-PLAG1 fusion in a lipoblastoma and the KIAA1549-BRAF fusions in two pilocytic astrocytomas. Seven events modified the diagnosis and/or possible treatment of the patient. For example, case PMABM000CCU/104 is an acute megakaryoblastic leukemia (FAB M7) in which a CBFA2T3-GLIS2 was detected. This could have affected patient care had it been detected at disease presentation36 since stem-cell transplantation is considered early on as a treatment option for leukemias harboring this fusion. Several other RNA-specific fusions are tyrosine kinase fusions. These have previously been described in (pediatric) tumors, although their incidence is very low, and therefore not always routinely tested. However, these tyrosine kinase fusions are known (putative) targets for targeted agents and could potentially widen treatment options. Case PMABM000CZK/193 resembled dermatofibrosarcoma protuberans histologically, but the EML4-NTRK3 fusion established this case as an IF. This patient was subsequently enrolled in a clinical trial with larotrectinib. Similarly, case PMABM000BQE/7, in which a ZCCHC8-ROS1 fusion was identified, received targeted treatment.We further analyzed the added clinical value of the 24 RNA-seq–specific gene fusions. Seventeen fusions confirmed or corresponded to the initial diagnosis (Data Supplement), for example, the 37 This tumor type is characterized by targetable kinase fusions and a better outcome than glioblastoma.38 RNA-seq identified a ZCCHC8-ROS1 fusion expression of the ROS1 transcript and compared the ROS1 expression with that of all other brain tumor samples in our cohort. The tumor's ROS1 expression is the second highest  was started at the recommended phase II dose of 300 mg/m2 once a day, and the medication was tolerated well. A magnetic resonance imaging was performed 1 month after commencing therapy and showed a decrease in the solid component. The patient remained stable on entrectinib for almost a year but showed progression thereafter.Shortly after surgery, chemotherapy was commenced according to the HGG HIT infant protocol , but switched to BBSFOP HGGWe present the results of detecting clinically relevant fusion transcripts using RNA-seq on a prospective pediatric pan-cancer cohort Table . The RNACBFA2T3-GLIS2 fusion 36 and the five druggable events : ZCCHC8-ROS1 (high-grade glioma), PPP1CB-ALK (high-grade glioma), EML4-ALK (papillary thyroid carcinoma), FGFR2-VCL , and EML4-NTRK3 (IF). Novel treatments targeting these events could have a significant impact on clinical management, highlighted by the patient with the ZCCHC8-ROS1–positive glioma that was treated with entrectinib. This demonstrates the power of RNA-seq in routine diagnostics.Most RNA-seq–specific fusions that were specifically identified by RNA-seq independently confirm a histologic diagnosis, but a subset of fusions identified were highly relevant and could affect treatment outcome. Examples are the IGH and TCR rearrangements in five samples despite the data analysis not being optimized to detect promoter and enhancer fusions. Additional validations are needed to show if all promoter fusions can reliably be detected by RNA-seq, but this is probably an advantage over targeted assays that are unlikely to detect these rearrangements because of the size of the breakpoint regions. Although not all promoter/enhancer fusions may result in chimeric transcripts, the effect of these fusions, for example, (over)expression of the 3′ fusion partner, is likely to manifest itself in the RNA-seq data, highlighting additional tests that could be developed: One could think of quality controls in the analysis pipeline to screen for potential fusion events that result in overexpression . This information is indispensable when developing patient-specific assays to determine disease dissemination and minimal residual disease, for example, in patients with lymphoma and leukemia.49 This is especially valuable when rare fusions are identified for which no predesigned  diagnostic test is available.Although the use of expression values holds promises for the future, there are several more immediate advantages of RNA-seq over traditional diagnostic methods. First, it is more efficient to use a single assay for gene fusion detection than a range of different techniques that require specific expertise and equipment. Furthermore, RNA-seq can detect fusion events not yet known to be clinically relevant. Hence, the assay itself does not have to be redesigned when novel fusion biomarkers are introduced into the clinic, in contrast to targeted approaches that will need to be tested, validated, and implemented.KIAA1549-BRAF fusions are lowly expressed. As a result, these fusions would normally be filtered out had it not been for an inclusion list in our analysis pipeline. Similarly, fusions lowly expressed or in samples with a low percentage of neoplastic cells are likely to be missed if data analysis is not specifically adapted to detect these fusions. For example, the COL6A2-USP6 fusion that was initially missed by RNA-seq because of low fusion fragments per million (< 0.1) combined with the absence of COL6A2 or USP6 on the inclusion list. (4) Finally, RNA-seq has a longer turnaround time of 7-21 days compared with more targeted approaches.Although RNA-seq provides clear advantages over current diagnostic methods, it also has limitations: (1) We deliberately chose to use fresh (frozen) material for RNA isolation. Although this is normal in hemato-oncology , in tissue diagnostics, it is standard practice to use formalin-fixed, paraffin-embedded material to isolate DNA and/or RNA. Instead of adapting our RNA-seq workflow to compensate for the suboptimal quality and yield of formalin-fixed, paraffin-embedded tissue–derived RNA, we opted to change the tissue processing workflow in our center. This resulted in a high percentage of fresh frozen tissue being available for RNA-seq (> 97%), and when fresh frozen material is available, this resulted in RNA of sufficient quantity and quality to perform RNA-seq in 243 of 244 samples. Since our center is the only centralized center for pediatric oncology in the Netherlands, this was relatively easy to implement, but we realize that this is more difficult for referral laboratories. (2) Our assay requires an input of 300 ng of total RNA, although as little as 50 ng can be used for small biopsies. This is still more than the amount required for some assays, for example, 10 ng for an RT-PCR or a 4 μm tissue slice for FISH analysis. However, since we had insufficient or low-quality RNA to perform RNA-seq in only 3% of cases (n = 7 + 1), in our daily practice, this is not a major issue. For cases with low amounts of RNA, it is still possible to use targeted fusion gene detection methods. (3) Some fusions are more readily detected than others, depending on the expression level of the specific fusion and the neoplastic cell content of the sample. For example, On the basis of the results described in this paper, we perform RNA-seq for all patient samples in our center. For now, we will continue the workflow as described here, RNA-seq in parallel to classical methods. One reason for this is that some clinical study protocols requiring specific  diagnostic tests, but we also aim to define the minimum number of reads required for a reliable RNA-seq result. We are confident that RNA-seq will replace many of the classical tests routinely performed in our center in the future.We show the advantages of detecting fusion transcripts using RNA-seq compared with current diagnostic approaches. It detects both fusion partners and breakpoint regions for follow-up assays and yields data that could advance future diagnostic testing. More importantly, it has a significantly higher diagnostic yield than current diagnostic assays and can be easily implemented in routine diagnostics.


Non-Coding RNA Scientific Board wishes you an exciting and fruitful read.We are glad to share with you our eighth Journal Club and to highlight some of the most interesting papers published recently. We hope to help you keep aware of non-coding RNA research outside of your area. The In recent years, researchers have started to literally wander in an enormous, mostly unknown, universe of different RNA species (mostly non-coding) in biofluids as potential biomarkers for diseases. However, a clear image of the RNA representation and abundance in different specimens is still missing, and no criteria for validation and reproducibility have been assessed. The situation is even more complex since there is no consensus regarding the collection of biofluids, the way non-coding RNAs are transported in the matrix (freely circulating or in extracellular vesicles), the techniques used for transcriptome assessment and the pipeline of analyses, as well as the best site/specimen to be investigated according to the disease.Researchers have tried to create some order in this complex universe. The recent work of Hulstaert et al.  reportedAs a final goal, the data could be a reference for the selection of the most relevant biofluids to monitor particular diseases and also give some guidelines for better analyses and comparisons among biomarker studies in extracellular matrices. Molecular Cell has provided a breakthrough, demonstrating an advance in live cell non-coding RNA imaging utilizing CRISPR–Cas13 systems [Despite the success of RNA detection techniques in fixed-cell experiments, the conventional ways of tracking RNA in live cells are limited, being non-specific and complicated to establish. A 2019 publication by Ling-Ling Chen’s group in MS2-MCP live cell RNA detection has limitations with ease of use, RNA structure issues and/or expression in widespread applications. Rectifying this, the authors described the use of dPspCas13b and dPguCas13b for robust imaging of prominent coding and non-coding RNA transcripts including NEAT1 (Nuclear Paraspeckle Assembly Transcript 1), MUC4 (Mucin 4), GCN4  and SatIII (Satellite III repeated sequences) in live cells. Using this new method, with GFP-dPspCas13b and guide RNAs targeting NEAT1, the authors rapidly and efficiently observed NEAT1-labeled paraspeckle dynamics, revealing a consistency with a “kiss-and-run/fusion” model after de novo formation. Combinatorial experiments also demonstrated viable single-cell, dual-color RNA imaging and simultaneous RNA/DNA visualization by combining dCas13 with MS2-MCP or dCas9 systems, respectively. As the localization and dynamics of non-coding RNAs are critical for their function, this improved visualization method is important for advancing the field. Further experiments may aim to improve the capability of this technique through additional Cas13 protein identification. Type 2 transglutaminase (TGM2) is a ubiquitous enzyme associated with neurodegenerative inflammatory pathologies, celiac disease and cancer progression. Furthermore, it is also related to epithelial–mesenchymal transition, supporting motility, metastasis, survival and drug resistance. The enzyme structure and catalytic activities of TGM2 have been investigated for many years, and recently, Franzese et al. found that two long non-coding RNAs (lncRNAs) transcribed within this gene are able to regulate the expression of its isoforms that are related to the presence of specific transcription factors particularly in ATRA-exposed leukemic  and neuronal  cell lines . In particular, the GATA3 emerged as the driver of the expression of one of these lncRNAs located within TGM2 gene. Moreover, through the binding to phUPF1, a component of NMD responsible for the active degradation of targets, this lncRNA was found to protect other transcripts from degradation. In addition, based on RNA immunoprecipitation experiments, Franzese et al. found that this lncRNA interacts with RNApol II, RXRα and Max, both regulatory factors of TGM2. Finally, Franzese et al. concluded that TGM2 lncRNAs are engaged in the generation/accumulation of altered variants with dramatically different catalytic activities, especially detected in pathological conditions.κB-interacting long noncoding RNA (NKILA). Excitingly, locked nucleic acid (LNA) technology is beginning to facilitate the translation of these findings into potential interventions. Ground-breaking new work from David Spector’s group at Cold Spring Harbor Labs illustrates a new key player in breast cancer progression: MaTAR25 (mouse)/LINC01271 (human). Like many other lncRNAs, MaTAR25 is enriched in the nucleus and interacts with chromatin; however, Chang et al. [MaTAR25 and its protein binding partners  are recruited to many genomic loci, one of which is TNS1, which encodes for the Tensin protein, a mediator of cell-to-matrix adhesion and migration. Genetic loss-of-function analysis using MaTAR25 knockout 4T1 breast cancer cells demonstrates its necessity for adhesion-associated cellular phenotypes, including migration and invasion in vitro and primary tumor growth and metastasis in vivo. Demonstrating the translational advancements in this field, Chang et al. proceeded to administer anti-sense oligos to knock down MaTAR25 and demonstrate suppressed tumor growth. This pathway is highly relevant to humans as the ortholog LINC01271 is more highly expressed in advanced stages of breast cancer and is highly expressed in lung metastases. Numerous long non-coding RNAs (lncRNAs) implicated in breast cancer progression and metastasis have been well characterized, including HOX antisense intergenic RNA (HOTAIR), Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1), Phosphatase 1 Nuclear-Targeting Subunit (PNUTS) and NF-g et al.  identifi


Eukaryotic RNA polymerase I (Pol I) transcribes ribosomal DNA and generates RNA for ribosome synthesis. Pol I accounts for the majority of cellular transcription activity and dysregulation of Pol I transcription leads to cancers and ribosomopathies. Despite extensive structural studies of yeast Pol I, structure of human Pol I remains unsolved. Here we determined the structures of the human Pol I in the pre-translocation, post-translocation, and backtracked states at near-atomic resolution. The single-subunit peripheral stalk lacks contacts with the DNA-binding clamp and is more flexible than the two-subunit stalk in yeast Pol I. Compared to yeast Pol I, human Pol I possesses a more closed clamp, which makes more contacts with DNA. The Pol I structure in the post-cleavage backtracked state shows that the C-terminal zinc ribbon of RPA12 inserts into an open funnel and facilitates “dinucleotide cleavage” on mismatched DNA–RNA hybrid. Critical disease-associated mutations are mapped on Pol I regions that are involved in catalysis and complex organization. In summary, the structures provide new sights into human Pol I complex organization and efficient proofreading. The human Pol I (hPol I) is located in the nucleolus and synthesizes 47S pre-ribosome RNA4. The 47S pre-ribosome RNA is further processed into mature 18S, 5.8S, and 28S ribosomal RNAs (rRNAs), which compose the RNA components of ribosomes together with 5S RNA synthesized by Pol III5. The Pol I-mediated pre-rRNA transcription is strictly required for ribosome biogenesis2. Dysregulation of Pol I transcription is associated with human diseases and the Pol I transcription machinery is considered to be the drug target for anticancer therapy9.Among the three eukaryotic RNA polymerases 10. The general transcription factors TFIIE and TFIIF are dissociable and serve as regulatory complexes in Pol II, while the TFIIF-/TFIIE-like subunits are evolved as bona fide constitutive subunits, PAF53/PAF49 in Pol I and C37/C53 in Pol III11. Additionally, the yPol I stalk is formed by A14/A43 heterodimer, whereas the functional hPol I stalk consists of only one subunit, RPA43, and the A14 counterpart has not been identified.The hPol I consists of 13 subunits and the yeast Pol I (yPol I) has similar counterparts with an additional yeast-specific subunit, A14. Among the 10 core subunits of the hPol I, five subunits  are shared by the three RNA polymerases  and two subunits (RPAC1 and RPAC2) are shared by Pol I and Pol III, indicating a highly conserved catalytic core14. Recent studies determined yPol I structures in distinct nucleotide-binding states18. It remains incompletely understood how hPol I is assembled and what the differences are between hPol I and yPol I. Here we present cryo-electron microscopy (cryo-EM) structures of hPol I elongation complex (EC) in the pre-translocation, post-translocation, and backtracked states. These structures reveal hPol I-specific structural features and molecular mechanism of backtracking and RNA cleavage of the mismatched DNA–RNA hybrid for proofreading.The structure of yPol I has been elaborately studied in the past decades. The overall architecture of yPol I was reported in early studiespost) was assembled by incubating the purified Pol I and a DNA–RNA hybrid scaffold consisting of 11 mismatched DNA base pairs and 8-nt RNA19. The EC in the pre-translocation state (ECpre) was assembled by adding CMPCPP -methyleno]-triphosphate), a non-hydrolysable nucleotide, to the ECpost. The DNA–RNA scaffold used in the assembly of EC in the backtracked state (ECbt) consists of an additional mismatched DNA–RNA base pair at −1 site (relative to the NTP addition site)  and lacks the yeast counterpart A14  and upstream binding factor (UBF)32. The functional effect of these human-specific structural features  in hPol I requires further investigation.The above structural differences between hPol I and yPol I may reflect distinct functional requirements of Pol I in the two species. For example, the clamp makes more contacts with the entry DNA in hPol I, suggesting a more stabilized hPol I–DNA engagement during transcription elongation, in line with the transcription of more complex and longer rDNA substrate in human cellspost structure reveals characteristic DNA–RNA hybrid and catalytic center in the post-translocation state . The positioning of the NTP substrate in the active site is similar to that in yPol I structure17 , which cleaves the mismatched RNA39. The equivalent factors, RPA12 in Pol I and RPC11 in Pol III, are incorporated into Pol I and Pol III, respectively, and reorganize the active site and mediate cleavage of the mismatched RNA for proofreading40. In Pol I, the N-ribbon of RPA12 resembles that of the Pol II subunit Rpb9 and the C-ribbon resembles that of TFIIS19. Although the backtracking of Pol II has been reported42, the post-cleavage state was not observed in previous studies, in which the negatively-charged residues D and E of TFIIS have been mutated to deactivate its cleavage activity42. The mechanism of backtracking and RNA cleavage in Pol I-mediated transcription remains incompletely understood.RNA polymerases can move backwards on DNA template to remove the RNA 3′ end nucleotides from the active site when encountering transcription barriers, such as mismatched NTP additionbt, we assembled the complex with the nucleotide at the −1 site of the template DNA converted from dA to dT, generating a dT–U mismatch  and Treacher Collins Syndrome (TCS)47 Fig. .Fig. 5Di43. A recent study shows that E593Q-containing Pol I stably binds rDNA chromatin and inhibits wild-type Pol I condensate formation, leading to repression of rRNA synthesis48. Notably, residue E593 is positioned near the conserved magnesium-binding aspartate residues   Fig. . The rep45  consists of ~6.6 kb 35S coding region and a short IGS51. The S. cerevisiae and mammalian Pol I elongate at an average speed of 60 nt/s and 91 nt/s in cells, respectively53. The difference in transcription speed may be related to the observed structural differences. Compared to yPol I, hPol I has a more closed DNA-binding clamp, which may generate more stable binding of the rDNA during transcription elongation and support high transcription processivity.In human cells, the total number of rDNA repeats was estimated to be in the range of ~400 copies, and each rDNA repeat (~43 kb) consists of regulatory elements within an intergenic spacer (IGS) of ~30 kb and the 47S pre-rRNA coding region of ~13 kb54. In all our structures, the stalk subunit RPA43 is flexible relative to the core module, distinct from the stably associated stalk in yPol I. The compositional and conformational differences in the yPol I stalk and hPol I stalk may accommodate the differences in transcription initiation in yeast and human systems. In recently reported structures of yPol I initiation complexes55, Rrn3 binds A43 and participates in the activation of Pol I transcription initiation, while Pol I core module interacts with the core factor (CF) formed by Rrn6, Rrn7, and Rrn11. The yPol I-mediated transcription initiation also requires TATA box-binding protein (TBP), Hmo1 and upstream activation factor56, which have not been structurally determined. The hPol I-mediated transcription initiation involves UBF, RRN3, and SL1 formed by TBP, TAF1A, TAF1B, TAF1C, TAF1D, and TAF1261. The yeast Rrn3 and human RRN3 share high sequence similarity, suggesting similar binding of Rrn3/RRN3 to A43/RPA43 in the yPol I and hPol I. The yeast CF subunits Rrn6, Rrn7, and Rrn11 are distantly related to human SL1 subunits TAF1C, TAF1B, and TAF1A, respectively62. However, the other two SL1 subunits, TAF1D and TAF12, do not exist in the yPol I system.It is known that RPA43 is involved in transcription initiation42. However, if the mismatched RNA backtracks beyond the gating tyrosine at some occasions, transcription arrest occurs and TFIIS is required to bind to reactivate the arrested Pol II. Our study suggests a mechanism of coordinated backtracking and RNA cleavage in Pol I-mediated transcription. The mismatched DNA–RNA possibly facilitates the Pol I to open the cleft and funnel to permit the entry of RPA12, which pushes the gating tyrosine Y687 aside to open the gate and allows the mismatched RNA to pass through. The catalytic center rearranges the nucleotides and exposes the scissile phosphodiester bond between nucleotide −1 and +1 to the negatively-charged RPA12 tip, which may facilitate the cleavage of the phosphodiester bond. Reactivation and continued transcription may occur upon the addition of next NTP. These evidences are consistent with previous report that yeast Pol I transcribes faster than Pol II and pauses less often, and Pol I is more efficient in backtracking recovery than Pol II15.The mechanistic studies of polymerase proofreading have been mainly focused on Pol II. When Pol II encounters mismatched DNA–RNA, RNA fraying occurs and Pol II pauses and backtracks by 1 bp. However, further backtracking is hindered by the gating tyrosine. Transcription elongation continues if the mismatched RNA is cleaved by intrinsic cleavage activity of the polymerase18, indicative of cleaved RNA by automatically backtracked Pol I. This is consistent with a recent study that the stable structure of nascent RNA favors forward nucleotide addition of Pol I and prohibits backtracking63, indicating that Pol I transcription backtracking may be further regulated by transcription-coupled events.The hPol I EC that we assembled with the non-mismatched scaffold for extension assay generated laddered bands  and all plasmids are co-transfected into Expi293F suspension cells using PEI. After being cultured at 37 °C for 72 h, cells were harvested and lysed in lysis buffer  at 4 °C. The supernatant was incubated with IgG-agarose beads (Smart Lifesciences) at 4 °C for 3 h, and beads were extensively washed with wash buffer . Protein was digested using Ulp1 protease overnight to remove tags, and the complex was eluted with elution buffer . The eluted protein was diluted to 100 mM NaCl and loaded on Mono Q . The bound protein was eluted with increasing concentrations of NaCl from 0.1 M to 1 M and Pol I complex was eluted at 360 mM NaCl. Fractions containing Pol I complex were pooled, concentrated using a 100-kDa cut-off centrifugation filter unit (Millipore) to ~2 mg/mL, and then dialyzed against low-salt buffer (similar to Mono Q buffer but containing 150 mM NaCl) overnight. The complex was then flash-frozen in liquid nitrogen and stored at −80 °C.The open reading frames of 13 subunits of hPol I were individually subcloned into a modified pCAG vectorpost, Pol I was incubated with a 46-bp transcription scaffold containing an 11-nt mismatched bubble and an 8-nt RNA  at 4 °C using Slide-a-lyzer mini dialysis device . The ECpre and ECbt were assembled similarly but with a few modifications. The ECpre was assembled by incubating the ECpost with additional 1 mM CMPCPP. The ECbt was assembled uniformly as ECpost, except with −1 site mismatched template DNA . Reactions were stopped by addition of an equal volume of 2× loading buffer  at different times and incubation for 5 min at 95 °C. The FAM-labeled RNA extension products were separated by denaturing gel electrophoresis (0.5 pmol RNA per lane) and visualized with Tanon 4600SF. For RNA cleavage assays, Pol I was incubated with pre-annealed backtrack-scaffold  were applied onto glow-discharged copper grids supported by a continuous thin layer of carbon film for 60 s before being negatively stained by 2% (w/v) uranyl formate solution at room temperature. The grids were prepared in the Ar/O2/O2 mixture for 20 s using a Gatan 950 Solarus plasma cleaning system with a power of 5 W. The ø 55/20 mm blotting paper (TED PELLA) was used for plunge freezing.For cryo-EM grid preparation, samples (4 μL at a concentration of ~1.5 mg/mL) were applied to freshly glow-discharged Quantifoil R1.2/1.3 holey gold grids. After incubation for 5 s at 4 °C and 100% humidity, the grids were blotted for 8.5 s with force 13 in a Thermo Fisher Scientific Vitrobot Mark IV and plunge-frozen in liquid ethane at liquid nitrogen temperature. The grids were prepared in the H65 with a nominal magnification of 130,000× in the NPTEM mode, which yielded a super-resolution pixel size of 0.527 Å on the image plane, and with a defocus value ranging from 1.5 μm to 2.5 μm. Each micrograph stack was dose-fractionated to 32 frames with a total electron dose of ~50 e–/Å266 and a total exposure time of 6.94 s. For the dataset of Pol I ECpre, ECpost, and ECbt samples, 3283, 2074, and 2854 micrographs were collected for further processing, respectively.The cryo-EM grids of Pol I EC were loaded onto a Thermo Fisher Scientific Titan Krios transmission electron microscope and operated at 300 kV for data collection. All the cryo-EM images were automatically recorded by a Gatan K2 Summit direct electron detector in the super-resolution counting mode using Serial-EMpost-crosslinking sample were loaded onto a Thermo Fisher Scientific Arctica transmission electron microscope and operated at 200 kV for data collection. All the cryo-EM images were automatically recorded by a Gatan K3 Summit direct electron detector in the super-resolution counting mode using Serial-EM with a nominal magnification of 36,000× in the TEM mode, which yielded a super-resolution pixel size of 0.55 Å on the image plane, and with a defocus value ranging from 1.5 μm to 2.5 μm. Each micrograph stack was dose-fractionated to 40 frames with a total electron dose of ~50 e–/Å2 and a total exposure time of 3.009 s. For the dataset of ECpost-crosslinking sample, 505 micrographs were collected for further processing.The cryo-EM grids of EC67 and binned 2-fold to a calibrated pixel size of 1.054 Å/pix. The defocus values were estimated by Gctf66 from summed images without dose weighting. Other procedures of cryo-EM data processing were performed with RELION v3.069 and cryoSPARC v269 using the dose-weighted micrographs.For cryo-EM data, drift and beam-induced motion corrections were applied on the super-resolution movie stacks using MotionCor2pre, 1,141,229 particles were picked by automatic particle picking in RELION without reference and subjected to reference-free two-dimensional (2D) classification. In all, 946,031 particles were selected from good 2D classes for three-dimensional (3D) classification in RELION. A total of 382,890 particles were selected from good 3D classes, which were used for the heterogeneous refinement in cryoSPARC and CTF, yielding a reconstruction of Pol I ECpre at 2.89 Å resolution.For the datasets of the Pol I ECpost, 618,806 particles were picked by automatic particle picking in RELION without reference and subjected to reference-free 2D classification. In all, 389,117 particles were selected from good 2D classes for 3D classification in RELION. A total of 282,280 particles were selected from good 3D classes, which were used for the heterogeneous refinement in cryoSPARC and CTF, yielding a reconstruction of Pol I ECpost at 2.81 Å resolution.For the datasets of the Pol I ECbt, 676,465 particles were picked by automatic particle picking in RELION without reference and subjected to reference-free 2D classification. In all, 581,698 particles were selected from good 2D classes for 3D classification in RELION. A total of 152,653 particles were selected from good 3D classes, which were used for the heterogeneous refinement in cryoSPARC and CTF, yielding a reconstruction of Pol I ECbt at 3.01 Å resolution.For the datasets of the Pol I ECpost (crosslinking) complex, 353,482 particles were picked by automatic particle picking in RELION without reference and subjected to reference-free 2D classification. In all, 250,226 particles were selected from good 2D classes for 3D classification in RELION. A total of 127,587 particles were selected from good 3D classes, which were used for the heterogeneous refinement in cryoSPARC and CTF, yielding a reconstruction of ECpost at 3.89 Å resolution.For the datasets of the ECB-factor estimation in cryoSPARC Sharpening Tools. All the visualization and evaluation of the 3D volume map were performed with UCSF Chimera or UCSF ChimeraX70, and the local resolution variations were calculated using cryoSPARC.All reported resolutions are based on the gold-standard (GS) Fourier shell correlation (FSC) = 0.143 criterion. The GSFSC curves were corrected for the effects of a soft mask with high-resolution noise substitution. All cryo-EM maps were sharpened by applying a negative 16 was used as initial structural template, which was docked into the cryo-EM maps by rigid-body fitting using UCSF Chimera70 with the aid of AlphaFold prediction20. The structural models were built in COOT71 and refined in real space using Phenix72 with secondary structure and geometry restraints using the cryo-EM map of the Pol I EC. Overfitting of the model was monitored by refining the model in one of the two half maps from the gold-standard refinement approach and testing the refined model against the other map73. Statistics of the map reconstruction and model refinement can be found in Supplementary Table 74. Maps and model representations in the figures were prepared by PyMOL (https://pymol.org/)75, UCSF Chimera, or UCSF ChimeraX76.The cryo-EM maps of the Pol I ECs were used for model fitting. The structures of yeast Pol I EC (PDB: 5M3F)Supplementary informationSupplementary Video S1Supplementary Video S2