Biology subjects

Cun, Y.

Publications and source records attributed to Cun, Y..

5 recordsLinked to original sources

bioRxiv2024

Identification of differentially methylated single-nucleotide m6A sites by incorporating site-specific antibody specificity

Various genome-wide and transcriptome-wide technologies are based on antibodies, however, the specificity of antibodies on different targets has not been characterized or considered in the analyses. The antibody-based MeRIP-seq is the most widely used method to determine the locations of N6-methyladenosine (m6A) on RNAs, especially for differential m6A analyses. However, the antibody specificities in different RNA regions and their resulting technical biases in differential m6A analyses have not been evaluated. Here, we evaluated the m6A antibody specificities using 100 pairs of spike-in RNAs with known m6A levels at single sites. Based on two replicates with different m6A levels on spike-in RNAs, we realized the m6A antibody specificities of the m6A sites on spike-in RNAs were greatly varied and mainly determined by the surrounding sequences of the m6A sites. Moreover, the MeRIP-seq signal fold change is the function of the real difference in m6A levels as well as the m6A antibody specificity. We then trained a machine learning model to predict the m6A antibody specificities of given sequences and predicted the m6A specificities of all RNA sequences surrounding the known m6A motif DRACH throughout the human transcriptome. Finally, we developed a Hierarchical statistic model for Differential Analysis of m6A Sites (HDAMS) by taking advantage of the predicted m6A specificities. We found that HDAMS can accurately determine the differentially methylated single-nucleotide m6A sites and the output more functionally relevant results. Our study not only provides a powerful tool for differential m6A analyses but also provides a methodological framework for other antibody-based studies to incorporate antibody specificities.

bioinformatics↗

bioRxiv2024

Endogenous labeling empowers accurate detection of m6A from single long reads of direct RNA sequencing

Although plenty of machine learning models have been developed to detect m6A RNA modification sites using the electric current signals of ONT direct RNA sequencing (DRS) reads, the landscape of m6A on different RNA isoforms is still a mystery due to their limited capacity to distinguish the m6A on individual long reads and RNA isoforms. The primary challenge in training the model with single-read accuracy is the difficulty of obtaining the training data from individual DRS reads that comprehensively represent the m6A on endogenous RNAs. Here, we endogenously label the methylated m6A sites on single ONT DRS reads by APOBEC1-YTH induced C-to-U mutations, strategically positioned 10-100 nt away from the known m6A sites on the same reads. Adopting a semi-supervised leaning strategy, we obtain 700,438 reliable 5-mer single-read level m6A signals, providing a comprehensive representation of m6A on endogenous RNAs. Leveraging this dataset, we develop m6Aiso, a deep residual neural network model that not only accurately identifies and quantifies known m6A sites but also reveals unknown, subtly methylated m6A sites responsive to METTL3 depletion. Analyzing m6Aiso-determined m6A on single reads and isoforms uncovers distance-dependent linkages of m6A sites along single molecules, as well as differential methylation of identical m6A sites on different isoforms. Moreover, we find wide-spread functionally important dynamic changes of m6A sites on specific isoforms during epithelial-mesenchymal transition (EMT). The pivotal utilization of the endogenous labeling strategy empowers m6Aiso to achieve remarkable precision in pinpointing m6A on individual molecules, underscores its effectiveness in elucidating the intricate dynamics and complexities of m6A across RNA isoforms.

bioinformatics↗

bioRxiv2023

Pan-cancer Analysis Reveals m6A Variation and Cell-specific Regulatory Network in Different Cancer Types

As the most abundant mRNA modification in mRNA, N6-methyladenosine (m6A) plays a crucial role in RNA fate, impacting cellular and physiological processes in various tumor types. However, our understanding of the function and role of the m6A methylome in tumor heterogeneity remains limited. Herein, we collected and analyzed m6A methylomes across nine human tissues from 97 m6A-seq and RNA-seq samples. Our findings demonstrate that m6A exhibits different heterogeneity in most tumor tissues compared to normal tissues, which contributes to the diverse clinical outcomes in different cancer types. We also found that the cancer type-specific m6A level regulated the expression of different cancer-related genes in distinct cancer types. Utilizing a novel and reliable method called "m6A-express", we predicted m6A- regulated genes and revealed that cancer type-specific m6A-regulated genes contributed to the prognosis, tumor origin and infiltration level of immune cells in diverse patient populations. Furthermore, we identified cell-specific m6A regulators that regulate cancer-specific m6A and constructed a regulatory network. Experimental validation was performed, confirming that the cell-specific m6A regulator CAPRIN1 controls the m6A level of TP53. Overall, our work reveals the clinical relevance of m6A in various tumor tissues and explains how such heterogeneity is established. These results further suggest the potential of m6A for cancer precision medicine for patients with different cancer types.

bioinformatics↗

bioRxiv2022

Dynamic Landscapes of tRNA Transcriptomes and Translatomes in Diverse Mouse Tissues

Although the function of tRNA in translational process is well established, it remains controversial whether tRNA abundance is tightly associated with translational efficiency (TE) in mammals. For example, how critically the expression of tRNAs contributes to the establishment of tissue-specific proteomes in mammals has not been well addressed. Here, we measured both tRNA expression using DM-tRNA-seq and ribosome-associated mRNAs in the brain, heart, and testis of RiboTag mice. Remarkable variation in the expression of tRNA isodecoders was observed among the different tissues. When the statistical effect of isodecoder-grouping on reducing variations is considered through permutating the anticodons, we observed an expected reduction in the tissue-variations of anticodon expression, an unexpected smaller variation of anticodon usage bias, and an unexpected larger variation of tRNA isotype expression. Regardless whether or not they share the same anticodons, isotypes encoding the same amino acids are co-expressed across different tissues. Based on the tRNA expression and TE computed from RiboTag-seq, we find that the tRNA adaptation index (tAI) values and TE are significantly correlated in the same tissues but not among tissues; tRNAs and the amino acid compositions of translating peptides are positively correlated in the same tissues but not between tissues. We therefore hypothesize that the tissue-specific expression of tRNAs might be related to post-transcriptional mechanisms, such as aminoacylation, modification, and tRNA-derived small RNAs (tsRNAs). This study provides a resource for tRNA and translation studies to gain novel insights into the dynamics of tRNAs and their role in translational regulation.

cell biology↗

bioRxiv2021

Serine/arginine-rich splicing factor 7 plays oncogenic roles through specific regulation of m6A RNA modification

Serine/Arginine-Rich Splicing Factor 7 (SRSF7), which is previously recognized as a splicing factor, has been revealed to play oncogenic roles in multiple cancers. However, the mechanisms underlying its oncogenic roles have not been well addressed. Here, based on N6-methyladenosine (m6A) co-methylation network analysis across diverse cell lines, we found SRSF7 positively correlated with glioblastoma cell-specific m6A methylation. We then proved SRSF7 is a novel m6A regulator that specifically facilitates the m6A methylation near its binding sites on the mRNAs involved in cell proliferation and migration through recruiting methyltransferase complex. Moreover, SRSF7 promotes the proliferation and migration of glioblastoma cells largely dependent on the m6A methyltransferase. The two single-nucleotide m6A sites on PBK are regulated by SRSF7 and partially mediate the effects of SRSF7 on glioblastoma cells through recognition by IGF2BP2. Together, our discovery revealed a novel role of SRSF7 in regulating m6A and timely confirmed the existence and functional importance of RNA binding protein (RBP) mediated specific regulation of m6A.

molecular biology↗