MicroRNAs preferentially target the genes with high transcriptional regulation complexity
Qinghua Cui, Zhenbao Yu, Youlian Pan, Enrico Purisima, Edwin Wang
Abstract
Over the past few years, microRNAs (miRNAs) have emerged as a new prominent class of gene regulatory factors that negatively regulate expression of approximately one-third of the genes in animal genomes at post-transcriptional level. However, it is still unclear why some genes are regulated by miRNAs but others are not, i.e. what principles govern miRNA regulation in animal genomes. In this study, we systematically analyzed the relationship between transcription factors (TFs) and miRNAs in gene regulation. We found that the genes with more TF-binding sites have a higher probability of being targeted by miRNAs and have more miRNA-binding sites on average. This observation reveals that the genes with higher cis-regulation complexity are more coordinately regulated by TFs at the transcriptional level and by miRNAs at the post-transcriptional level. This is a potentially novel discovery of mechanism for coordinated regulation of gene expression. Gene ontology analysis further demonstrated that such coordinated regulation is more popular in the developmental genes.
Create a lesson
Related papers
Unlocking the Regulatory Genome by ARGUS: An Evidence-Constrained Agentic Framework for Interpreting Single Nucleotide Variants
Pratik Dutta, Matthew B. Obusan, Max Chao et al.
Truecell reproduces R Seurat's single-cell analysis outputs natively in the Python ecosystem
Nuzla Ismail, Shanika Amarasoma, Michelle Galeas-Pena et al.
Beyond the Transcriptome: Chromatin-Informed Prediction of Cell-State-Dependent Perturbation Responses
Jiafa Ruan, Chenyang He, Ruijie Quan et al.
HRPv2: an automated and enhanced method for full-length homology-based R-gene prediction
Giuseppe Andolfo, Thomas Holzweber, Juliane C. Dohm et al.
Tracing model-generated DNA with position-independent watermarking
Kimon Antonios Provatas1, Christos Galanopoulos, Ilias Georgakopoulos-Soares
A Unified Unsupervised Framework for Genome-Wide Association Studies in Heterogeneous Populations
Xiong Shen, Zhenshuang Tang, Yong Liao et al.