Buchmann Institut für Molekulare Lebenswissenschaften (BMLS)
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The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. In recent years, the development of new high-throughput experimental and computational techniques has been a key driving force in discovering the properties of RNA modifications. Machine learning applications, such as for classification, clustering or de novo identification, have been critical in these advances. Nonetheless, various challenges remain before the full potential of machine learning for epitranscriptomics can be leveraged. In this review, we provide a comprehensive survey of machine learning methods to detect RNA modifications using diverse input data sources. We describe strategies to train and test machine learning methods and to encode and interpret features that are relevant for epitranscriptomics. Finally, we identify some of the current challenges and open questions about RNA modification analysis, including the ambiguity in predicting RNA modifications in transcript isoforms or in single nucleotides, or the lack of complete ground truth sets to test RNA modifications. We believe this review will inspire and benefit the rapidly developing field of epitranscriptomics in addressing the current limitations through the effective use of machine learning.
Cyclin CLB2 mRNA localization and protein synthesis link cell cycle progression to bud growth
(2024)
Clb2 is a conserved mitotic B-type cyclin, the levels of which are finely controlled to drive progression through the cell cycle. While it is known that CLB2 transcription and Clb2 protein degradation are important for precise control of its expression, it remains unclear whether the synthesis of Clb2 is also regulated. To address whether and how Clb2 expression levels respond to cell growth changes and adapt cell cycle progression, we combined single-cell and single-molecule imaging methods to measure CLB2 mRNA and protein expression throughout the Saccharomyces cerevisiae cell cycle. We found that the CLB2 mRNA was efficiently localized to the yeast bud as soon as this compartment was formed, but strikingly the Clb2 protein accumulated in the mother nucleus. The CLB2 mRNA localization in the yeast bud by the She2-3 complex did not control protein localization but rather promoted CLB2 translation. Moreover, CLB2 mRNA bud localization and protein synthesis were coupled and dependent on a single secondary structure -a ZIP code-located in the coding sequence. In a CLB2 ZIP code mutant, mRNA localization was impaired and Clb2 protein synthesis decreased, resulting in changes in cell cycle distribution and increased size of daughter cells at birth. Finally, while in WT cells the Clb2 protein concentration followed bud growth, this relationship was impaired in the ZIP code mutant. We propose that S. cerevisiae couples the control of CLB2 mRNA bud localization and protein synthesis to coordinate cell growth and cell cycle progression. This mechanism extends our knowledge of CLB2 expression regulation, and constitutes a novel function for mRNA localization.
MicroRNAs (miRNAs) are critical post-transcriptional regulators in many biological processes. They act by guiding RNA-induced silencing complexes to miRNA response elements (MREs) in target mRNAs, inducing translational inhibition and/or mRNA degradation. Functional MREs are expected to predominantly occur in the 3' untranslated region and involve perfect base-pairing of the miRNA seed. Here, we generate a high-resolution map of miR-181a/b-1 (miR-181) MREs to define the targeting rules of miR-181 in developing murine T-cells. By combining a multi-omics approach with computational high-resolution analyses, we uncover novel miR-181 targets and demonstrate that miR-181 acts predominantly through RNA destabilization. Importantly, we discover an alternative seed match and identify a distinct set of targets with repeat elements in the coding sequence which are targeted by miR-181 and mediate translational inhibition. In conclusion, deep profiling of MREs in primary cells is critical to expand physiologically relevant targetomes and establish context-dependent miRNA targeting rules.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A significant roadblock hindering progress in epitranscriptomics is the identification of more than one modification in individual transcript molecules. We address this with CHEUI (CH3 (methylation) Estimation Using Ionic current). CHEUI predicts N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual molecules from the same sample, the stoichiometry at transcript reference sites, and differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals to achieve high single-molecule, transcript-site, and stoichiometry accuracies in multiple tests using synthetic RNA standards and cell line data. CHEUI’s capability to identify two modification types in the same sample reveals a co-occurrence of m6A and m5C in individual mRNAs in cell line and tissue transcriptomes. CHEUI provides new avenues to discover and study the function of the epitranscriptome.
MicroRNAs (miRNAs) are critical post-transcriptional regulators in many biological processes. They act by guiding RNA-induced silencing complexes to miRNA response elements (MREs) in target mRNAs, inducing translational inhibition and/or mRNA degradation. Functional MREs are expected to predominantly occur in the 3’ untranslated region and involve perfect base-pairing of the miRNA seed. Here, we generate a high-resolution map of miR-181a/b-1 (miR-181) MREs to define the targeting rules of miR-181 in developing murine T-cells. By combining a multi-omics approach with computational high-resolution analyses, we uncover novel miR-181 targets and demonstrate that miR-181 acts predominantly through RNA destabilization. Importantly, we discover an alternative seed match and identify a distinct set of targets with repeat elements in the coding sequence which are targeted by miR-181 and mediate translational inhibition. In conclusion, deep profiling of MREs in primary cells is critical to expand physiologically relevant targetomes and establish context-dependent miRNA targeting rules.
Key Points:
* Deep profiling identifies novel targets of miR-181 associated with global gene regulation.
* miR-181 MREs in repeat elements in the coding sequence act through translational inhibition.
* High-resolution analysis reveals an alternative seed match in functional MREs.
RNA-binding proteins (RBPs) control every RNA metabolic process by multiple protein-RNA and protein-protein interactions. Their roles have largely been analyzed by crude mutations, which abrogate multiple functions at once and likely impact the structural integrity of the large ribonucleoprotein particles (RNPs) these proteins function in. Using UV-induced RNA-protein crosslinking of entire cells, protein complex purification and mass spectrometric analysis, we identified >100 in vivo RNA crosslinks in 16 nuclear mRNP components in Saccharomyces cerevisiae. For functional analysis, we chose Npl3, which displayed crosslinks in its two RNA recognition motifs (RRMs) and in the connecting flexible linker region. Both RRM domains and the linker uniquely contribute to RNA recognition as revealed by NMR and structural analyses. Interestingly, mutations in these regions cause different phenotypes, indicating distinct functions of the different RNA-binding domains. Notably, an npl3-Linker mutation strongly impairs recruitment of several mRNP components to chromatin and incorporation of other mRNP components into nuclear mRNPs, establishing a so far unknown function of Npl3 in nuclear mRNP assembly. Taken together, our integrative analysis uncovers a specific function of the RNA-binding activity of the nuclear mRNP component Npl3. This approach can be readily applied to RBPs in any RNA metabolic process.