Repurposing RNA sequencing for discovery of RNA modifications in clinical cohorts

The study of RNA modifications in large clinical cohorts can reveal relationships between the epitranscriptome and human diseases, although this is especially challenging. We developed ModTect (https://github.com/ktan8/ModTect), a statistical framework to identify RNA modifications de novo by standard RNA-sequencing with deletion and mis-incorporation signals. We show that ModTect can identify both known (N1-methyladenosine) and previously unknown types of mRNA modifications (N2,N2-dimethylguanosine) at nucleotide-resolution. Applying ModTect to 11,371 patient samples and 934 cell lines across 33 cancer types, we show that the epitranscriptome was dysregulated in patients across multiple cancer types and was additionally associated with cancer progression and survival outcomes. Some types of RNA modification were also more disrupted than others in patients with cancer. Moreover, RNA modifications contribute to multiple types of RNA-DNA sequence differences, which unexpectedly escape detection by Sanger sequencing. ModTect can thus be used to discover associations between RNA modifications and clinical outcomes in patient cohorts.

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Circ-GALNT16 Restrains Colorectal Cancer Progression by Enhancing the SUMOylation of hnRNPK

10.21203/rs.3.rs-501100/v1 ◽

2021 ◽

Author(s):

Chaofan Peng ◽

Yuqian Tan ◽

Peng Yang ◽

Kangpeng Jin ◽

Chuan Zhang ◽

...

Keyword(s):

Colorectal Cancer ◽

Dna Binding ◽

Rna Sequencing ◽

Cancer Progression ◽

Molecular Mechanisms ◽

Transcriptional Level ◽

Multiple Cancer ◽

Binding Ability ◽

Colorectal Cancer Progression

Abstract BackgroundEmerging studies have investigated circRNAs as significant regulation factors in multiple cancer progression. Nevertheless, the biological functions and underlying mechanisms of circRNAs in colorectal cancer progression remain unclear.MethodsA novel circRNA (circ-GALNT16) was identified by microarray and qRT-PCR. A series of phenotype experiments in vitro and vivo were performed to investigate the role of circ-GALNT16 in CRC. FISH, RNA pulldown assay, RIP assay, RNA sequencing, coimmunoprecipitation, and ChIP were constructed to explore the molecular mechanisms of circ-GALNT16 in colorectal cancer.ResultsCirc-GALNT16 was downregulated in colorectal cancer and negatively correlated with poor prognosis. Circ-GALNT16 suppressed the proliferation and metastasis ability of colorectal cancer in vitro and vivo. Mechanistically, circ-GALNT16 could bind to the KH3 domain of heterogeneous nuclear ribonucleoprotein K (hnRNPK), which resulted in the SUMOylation of hnRNPK. Additionally, circ-GALNT16 could enhance the hnRNPK-p53 complex by facilitating the SUMOylation of hnRNPK. Furthermore, RNA sequencing assay identified serpin family E member 1 as the target gene of circ-GALNT16 at the transcriptional level. Rescue assays revealed that circ-GALNT16 regulated the expression of Serpine1 by inhibiting the deSUMOylation of hnRNPK mediated by SUMO specific peptidase 2 and then regulating the sequence-specific DNA binding ability of the hnRNPK-p53 transcriptional complex.ConclusionsCirc-GALNT16 suppressed CRC progression via inhibiting Serpine1 expression through adjusting the sequence-specific DNA binding ability of the SENP2-mediated hnRNPK-p53 transcriptional complex and might work as a biomarker and therapeutic target for CRC.

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The architecture of SARS-CoV-2 transcriptome

10.1101/2020.03.12.988865 ◽

2020 ◽

Cited By ~ 38

Author(s):

Dongwan Kim ◽

Joo-Yeon Lee ◽

Jeong-Sun Yang ◽

Jun Won Kim ◽

V. Narry Kim ◽

...

Keyword(s):

High Resolution ◽

Rna Sequencing ◽

Rna Modification ◽

List Type ◽

Rna Modifications ◽

Subgenomic Rnas ◽

Frequent Motif ◽

High Resolution Map ◽

Functional Investigation ◽

Resolution Map

AbstractSARS-CoV-2 is a betacoronavirus that is responsible for the COVID-19 pandemic. The genome of SARS-CoV-2 was reported recently, but its transcriptomic architecture is unknown. Utilizing two complementary sequencing techniques, we here present a high-resolution map of the SARS-CoV-2 transcriptome and epitranscriptome. DNA nanoball sequencing shows that the transcriptome is highly complex owing to numerous recombination events, both canonical and noncanonical. In addition to the genomic RNA and subgenomic RNAs common in all coronaviruses, SARS-CoV-2 produces a large number of transcripts encoding unknown ORFs with fusion, deletion, and/or frameshift. Using nanopore direct RNA sequencing, we further find at least 41 RNA modification sites on viral transcripts, with the most frequent motif being AAGAA. Modified RNAs have shorter poly(A) tails than unmodified RNAs, suggesting a link between the internal modification and the 3′ tail. Functional investigation of the unknown ORFs and RNA modifications discovered in this study will open new directions to our understanding of the life cycle and pathogenicity of SARS-CoV-2.HighlightsWe provide a high-resolution map of SARS-CoV-2 transcriptome and epitranscriptome using nanopore direct RNA sequencing and DNA nanoball sequencing.The transcriptome is highly complex owing to numerous recombination events, both canonical and noncanonical.In addition to the genomic and subgenomic RNAs common in all coronaviruses, SARS-CoV-2 produces transcripts encoding unknown ORFs.We discover at least 41 potential RNA modification sites with an AAGAA motif.

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Nucleotide resolution profiling of m3C RNA modification by HAC-seq

Nucleic Acids Research ◽

10.1093/nar/gkaa1186 ◽

2020 ◽

Author(s):

Jia Cui ◽

Qi Liu ◽

Erdem Sendinc ◽

Yang Shi ◽

Richard I Gregory

Keyword(s):

Rna Modification ◽

Rna Modifications ◽

Mcf7 Cells ◽

Single Nucleotide ◽

Novel Method ◽

Specific Mapping ◽

Nucleotide Resolution ◽

And Function ◽

Trna Isoacceptors ◽

Single Nucleotide Resolution

Abstract Cellular RNAs are subject to a myriad of different chemical modifications that play important roles in controlling RNA expression and function. Dysregulation of certain RNA modifications, the so-called ‘epitranscriptome’, contributes to human disease. One limitation in studying the functional, physiological, and pathological roles of the epitranscriptome is the availability of methods for the precise mapping of individual RNA modifications throughout the transcriptome. 3-Methylcytidine (m3C) modification of certain tRNAs is well established and was also recently detected in mRNA. However, methods for the specific mapping of m3C throughout the transcriptome are lacking. Here, we developed a m3C-specific technique, Hydrazine-Aniline Cleavage sequencing (HAC-seq), to profile the m3C methylome at single-nucleotide resolution. We applied HAC-seq to analyze ribosomal RNA (rRNA)-depleted total RNAs in human cells. We found that tRNAs are the predominant m3C-modified RNA species, with 17 m3C modification sites on 11 cytoplasmic and 2 mitochondrial tRNA isoacceptors in MCF7 cells. We found no evidence for m3C-modification of mRNA or other non-coding RNAs at comparable levels to tRNAs in these cells. HAC-seq provides a novel method for the unbiased, transcriptome-wide identification of m3C RNA modification at single-nucleotide resolution, and could be widely applied to reveal the m3C methylome in different cells and tissues.

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Integrative analyses of the RNA modification machinery reveal tissue- and cancer-specific signatures

10.1101/830968 ◽

2019 ◽

Cited By ~ 2

Author(s):

Oguzhan Begik ◽

Morghan C. Lucas ◽

Huanle Liu ◽

Jose Miguel Ramirez ◽

John S. Mattick ◽

...

Keyword(s):

Cancer Research ◽

Expression Patterns ◽

Rna Modification ◽

Cancer Type ◽

Evolutionary Analysis ◽

Rna Modifications ◽

Human Samples ◽

Mammalian Tissues ◽

Normal Human ◽

Cancer Types

ABSTRACTBackgroundRNA modifications play central roles in cellular fate and differentiation. These features have placed the epitranscriptome in the forefront of developmental biology and cancer research. However, the machinery responsible for placing, removing and recognizing more than 170 RNA modifications remains largely uncharacterized and poorly annotated, and we currently lack integrative studies that identify which RNA modification–related proteins (RMPs) may be dysregulated in each cancer type.ResultsHere we have performed a comprehensive annotation and evolutionary analysis of human RMPs as well as an integrative analysis of their expression patterns across 32 tissues, 10 species and 13,358 paired tumor-normal human samples. Our analysis reveals an unanticipated heterogeneity of RMP expression patterns across mammalian tissues, with a vast proportion of duplicated enzymes displaying testis-specific expression, suggesting a key role for RNA modifications in sperm formation and possibly intergenerational inheritance. Moreover, through the analysis of paired tumor-normal human samples we uncover many RMPs that are dysregulated in various types of cancer, and whose expression levels are predictive of cancer progression. Surprisingly, we find that several commonly studied RNA modification enzymes such as METTL3 or FTO, are not significantly up-regulated in most cancer types, once the sample is properly scaled and normalized to the full dataset, whereas several less-characterized RMPs, such as LAGE3 and HENMT1, are dysregulated in many cancers.ConclusionsOur analyses reveal an unanticipated heterogeneity in the expression patterns of RMPs across mammalian tissues, and uncover a large proportion of dysregulated RMPs in multiple cancer types. We provide novel targets for future cancer research studies targeting the human epitranscriptome, as well as foundations to understand cell type-specific behaviours that are orchestrated by RNA modifications.

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Mutational interactions define novel cancer subgroups

10.1101/187260 ◽

2017 ◽

Author(s):

Jack Kuipers ◽

Thomas Thurnherr ◽

Giusi Moffa ◽

Polina Suter ◽

Jonas Behr ◽

...

Keyword(s):

Bayesian Network ◽

Cancer Progression ◽

Large Scale ◽

De Novo ◽

Drug Repositioning ◽

Tissue Type ◽

Survival Prediction ◽

Cancer Type ◽

Cancer Data ◽

Cancer Types

Large-scale genomic data can help to uncover the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues of origin highlights differences and similarities which can guide drug repositioning as well as the design of targeted and precise treatments. Here, we developed an improved Bayesian network model for tumour mutational profiles and applied it to 8,198 patient samples across 22 cancer types from TCGA. For each cancer type, we identified the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we found genes which interact both within and across cancer types. To detach cancer classification from the tissue type we performed de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We found 22 novel clusters which significantly improved survival prediction beyond clinical and histopathological information. The models highlight key gene interactions for each cluster that can be used for genomic stratification in clinical trials and for identifying drug targets within strata.

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Quantitative profiling of native RNA modifications and their dynamics using nanopore sequencing

10.1101/2020.07.06.189969 ◽

2020 ◽

Author(s):

Oguzhan Begik ◽

Morghan C Lucas ◽

Leszek P Pryszcz ◽

Jose Miguel Ramirez ◽

Rebeca Medina ◽

...

Keyword(s):

De Novo ◽

Rna Modification ◽

Nanopore Sequencing ◽

Rna Modifications ◽

General Belief ◽

Rna Molecules ◽

Base Calling ◽

Genetic Strains ◽

Quantitative Manner ◽

External Signals

ABSTRACTA broad diversity of modifications decorate RNA molecules. Originally conceived as static components, evidence is accumulating that some RNA modifications may be dynamic, contributing to cellular responses to external signals and environmental circumstances. A major difficulty in studying these modifications, however, is the need of tailored protocols to map each modification type individually. Here, we present a new approach that uses direct RNA nanopore sequencing to identify and quantify RNA modifications present in native RNA molecules. First, we show that each RNA modification type results in a distinct and characteristic base-calling ‘error’ signature, which we validate using a battery of genetic strains lacking either pseudouridine (Y) or 2’-O-methylation (Nm) modifications. We then demonstrate the value of these signatures for de novo prediction of Y modifications transcriptome-wide, confirming known Y-modified sites as well as uncovering novel Y sites in mRNAs, ncRNAs and rRNAs, including a previously unreported Pus4-dependent Y modification in yeast mitochondrial rRNA, which we validate using orthogonal methods. To explore the dynamics of pseudouridylation across environmental stresses, we treat the cells with oxidative, cold and heat stresses, finding that yeast ribosomal rRNA modifications do not change upon environmental exposures, contrary to the general belief. By contrast, our method reveals many novel heat-sensitive Y-modified sites in snRNAs, snoRNAs and mRNAs, in addition to recovering previously reported sites. Finally, we develop a novel software, nanoRMS, which we show can estimate per-site modification stoichiometries from individual RNA molecules by identifying the reads with altered current intensity and trace profiles, and quantify the RNA modification stoichiometry changes between two conditions. Our work demonstrates that Y RNA modifications can be predicted de novo and in a quantitative manner using native RNA nanopore sequencing.

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m6A target microRNAs in serum for cancer detection

Molecular Cancer ◽

10.1186/s12943-021-01477-6 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Bo Zhang ◽

Zhenmei Chen ◽

Baorui Tao ◽

Chenhe Yi ◽

Zhifei Lin ◽

...

Keyword(s):

Cancer Detection ◽

External Validation ◽

High Accuracy ◽

Rna Modification ◽

Support Vector ◽

Cancer Type ◽

Mass Detection ◽

Serum Samples ◽

Multiple Cancer ◽

Cancer Types

AbstractRecent studies have revealed the significant dysregulation of m6A level in peripheral blood in several cancer types and its value in diagnosis. Nonetheless, a biomarker for accurate screening of multiple cancer types has not been established based on the perspective of m6A modification. In this study, we aimed to develop a serum diagnostic signature based on the m6A target miRNAs for the mass detection of cancer. A total of 14965 serum samples with 12 cancer types were included. Based on training cohort (n=7299), we developed the m6A-miRNAs signature using a support vector machine algorithm for cancer detection. The m6A-miRNAs signature showed high accuracy, and its area under the curve (AUC) in the training, internal validation and external validation cohort reached 0.979 (95%CI 0.976 - 0.982), 0.976 (95%CI 0.973 - 0.979) and 0.936 (95%CI 0.922 - 0.951), respectively. In the performance of distinguishing cancer types, the m6A-miRNAs signature showed superior sensitivity in each cancer type and presented a satisfactory AUC in identifying lung cancer, gastric cancer and hepatocellular carcinoma. Additionally, the diagnostic performance of m6A-miRNAs was not interfered by the gender, age and benign disease. In short, this study revealed the value of serum circulating m6A miRNAs in cancer detection and provided a new direction and strategy for the development of novel biomarkers with high accuracy, low cost and less invasiveness for mass cancer screening, such as RNA modification.

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RNA methylation in mammalian development and cancer

Cell Biology and Toxicology ◽

10.1007/s10565-021-09627-8 ◽

2021 ◽

Author(s):

Peizhe Song ◽

Subiding Tayier ◽

Zhihe Cai ◽

Guifang Jia

Keyword(s):

Cell Fate ◽

Cancer Progression ◽

Rna Modification ◽

Biological Processes ◽

Cell Fate Determination ◽

Mammalian Development ◽

Rna Modifications ◽

Temporal And Spatial ◽

Underlying Mechanisms ◽

Cellular Components

AbstractSimilar 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.

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Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning

10.1101/2020.12.20.423675 ◽

2020 ◽

Author(s):

Nadine Körtel ◽

Cornelia Rücklé ◽

You Zhou ◽

Anke Busch ◽

FX Reymond Sutandy ◽

...

Keyword(s):

Machine Learning ◽

Rna Stability ◽

Rna Modification ◽

List Type ◽

Rna Modifications ◽

Uv Crosslinking ◽

Input Material ◽

Machine Learning Model ◽

Eukaryotic Mrnas ◽

Nucleotide Resolution

AbstractN6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing, such as RNA stability and translation. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based approach to map m6A sites in the transcriptome with single-nucleotide resolution. However, due to broad antibody reactivity, reliable identification of m6A sites from miCLIP data remains challenging. Here, we present several experimental and computational innovations, that significantly improve transcriptome-wide detection of m6A sites. Based on the recently developed iCLIP2 protocol, the optimised miCLIP2 results in high-complexity libraries from less input material, which yields a more comprehensive representation of m6A sites. Next, we established a robust computational pipeline to identify true m6A sites from our miCLIP2 data. The analyses are calibrated with data from Mettl3 knockout cells to learn the characteristics of m6A deposition, including a significant number of m6A sites outside of DRACH motifs. In order to make these results universally applicable, we trained a machine learning model, m6Aboost, based on the experimental and RNA sequence features. Importantly, m6Aboost allows prediction of genuine m6A sites in miCLIP data without filtering for DRACH motifs or the need for Mettl3 depletion. Using m6Aboost, we identify thousands of high-confidence m6A sites in different murine and human cell lines, which provide a rich resource for future analysis. Collectively, our combined experimental and computational methodology greatly improves m6A identification.HighlightsmiCLIP2 produces complex libraries to map m6A RNA modificationsMettl3 KO miCLIP2 allows to identify Mettl3-dependent RNA modification sitesMachine learning predicts genuine m6A sites from human and mouse miCLIP2 data without Mettl3 KOm6A modifications frequently occur outside of DRACH motifs and associates with alternative splicing

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Sequence- and structure-specific cytosine-5 mRNA methylation by NSUN6

Nucleic Acids Research ◽

10.1093/nar/gkaa1193 ◽

2020 ◽

Author(s):

Tommaso Selmi ◽

Shobbir Hussain ◽

Sabine Dietmann ◽

Matthias Heiß ◽

Kayla Borland ◽

...

Keyword(s):

Bisulfite Sequencing ◽

Translation Termination ◽

Consensus Sequence ◽

Ribosome Profiling ◽

Untranslated Regions ◽

Rna Modification ◽

Sequence Motif ◽

Cancer Types ◽

Quality Control Mechanism ◽

Nucleotide Resolution

Abstract The highly abundant N6-methyladenosine (m6A) RNA modification affects most aspects of mRNA function, yet the precise function of the rarer 5-methylcytidine (m5C) remains largely unknown. Here, we map m5C in the human transcriptome using methylation-dependent individual-nucleotide resolution cross-linking and immunoprecipitation (miCLIP) combined with RNA bisulfite sequencing. We identify NSUN6 as a methyltransferase with strong substrate specificity towards mRNA. NSUN6 primarily targeted three prime untranslated regions (3′UTR) at the consensus sequence motif CTCCA, located in loops of hairpin structures. Knockout and rescue experiments revealed enhanced mRNA and translation levels when NSUN6-targeted mRNAs were methylated. Ribosome profiling further demonstrated that NSUN6-specific methylation correlated with translation termination. While NSUN6 was dispensable for mouse embryonic development, it was down-regulated in human tumours and high expression of NSUN6 indicated better patient outcome of certain cancer types. In summary, our study identifies NSUN6 as a methyltransferase targeting mRNA, potentially as part of a quality control mechanism involved in translation termination fidelity.

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