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Single-cell analysis of transcription factor regulatory networks reveals molecular basis for subtype-specific dysregulation in acute myeloid leukemia.

ABSTRACT: Highly heterogeneous acute myeloid leukemia (AML) exhibits dysregulated transcriptional programs. Transcription factor (TF) regulatory networks underlying AML subtypes have not been elucidated at single-cell resolution. Here, we comprehensively mapped malignancy-related TFs activated in different AML subtypes by analyzing single-cell RNA sequencing data from AMLs and healthy donors. We first identified six modules of regulatory networks which were prevalently dysregulated in all AML patients. AML subtypes featured with different malignant cellular composition possessed subtype-specific regulatory TFs associated with differentiation suppression or immune modulation. At last, we validated that ERF was crucial for the development of hematopoietic stem/progenitor cells by performing loss- and gain-of-function experiments in zebrafish embryos. Collectively, our work thoroughly documents an abnormal spectrum of transcriptional regulatory networks in AML and reveals subtype-specific dysregulation basis, which provides a prospective view to AML pathogenesis and potential targets for both diagnosis and therapy.

SUBMITTER: Sun R

PROVIDER: S-EPMC9362874 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Single-cell analysis of transcription factor regulatory networks reveals molecular basis for subtype-specific dysregulation in acute myeloid leukemia.

Sun Ruixia R Sun Lina L Xie Xiaowei X Li Xuan X Wu Peng P Wang Lu L Zhu Ping P

Blood science (Baltimore, Md.) 20220401 2

Highly heterogeneous acute myeloid leukemia (AML) exhibits dysregulated transcriptional programs. Transcription factor (TF) regulatory networks underlying AML subtypes have not been elucidated at single-cell resolution. Here, we comprehensively mapped malignancy-related TFs activated in different AML subtypes by analyzing single-cell RNA sequencing data from AMLs and healthy donors. We first identified six modules of regulatory networks which were prevalently dysregulated in all AML patients. AM ...[more]

PMID: 35957668

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Project description:Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species.IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

Dataset Information

Single-cell analysis of transcription factor regulatory networks reveals molecular basis for subtype-specific dysregulation in acute myeloid leukemia.

Publications

Single-cell analysis of transcription factor regulatory networks reveals molecular basis for subtype-specific dysregulation in acute myeloid leukemia.

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