DOI: 10.5061/dryad.s4mw6m9dr
title: Data from: Age-specific induction of mutant p53 drives clonal hematopoiesis and acute myeloid leukemia in adult mice

description: The investigation of the mechanisms behind p53 mutations in acute myeloid
 leukemia (AML) has been limited by the lack of suitable mouse models,
 which historically have resulted in lymphoma rather than leukemia. This
 study introduces two new AML mouse models. One model induces mutant p53
 and Mdm2 haploinsufficiency in early development, showing the role of Mdm2
 in myeloid-biased hematopoiesis and AML predisposition, independent of
 p53. The second model mimics clonal hematopoiesis by inducing mutant p53
 in adult hematopoietic stem cells, demonstrating that the timing of p53
 mutation determines AML versus lymphoma development. In this context,
 age-related changes in hematopoietic stem cells (HSCs), collaborates with
 mutant p53 to predispose towards myeloid transformation rather than
 lymphoma development. Our study unveils new insights into the cooperative
 impact of HSC age, Trp53 mutations and Mdm2 haploinsufficiency on clonal
 hematopoiesis and the development of myeloid malignancies.
description: RNA was extracted utilizing the Direct-Zol RNA Microprep kit
 (Zymo Research, R2060). Barcoded Illumina-compatible stranded total RNA
 libraries were prepared using the TruSeq Stranded Total RNA kit (Illumina)
 as previously described. Library pools were quantified by qPCR and
 sequenced on the HiSeq 4000 sequencer using the 75-bp paired-end format.
 The raw RNA-seq readouts were subsequently mapped to the mouse mm10
 assembly reference genome using TopHat2 and analyzed with DESeq2
 (Bioconductor package).
description: # RNA sequencing of LK cells derived from Vav-cre; Mdm2 fl/+ vs. Vav-cre;
 Mdm2 fl/+;Trp53 fl/fl
 [https://doi.org/10.5061/dryad.s4mw6m9dr](https://doi.org/10.5061/dryad.s4mw6m9dr) The datasets presented include differential gene expression data and results from Ingenuity Pathway Analysis (IPA) for three distinct mouse cohorts in a gene regulation investigation. These groups are designated as Control, Vav-mdm2fl/+, and Vav-mdm2fl/+; Trp53 fl/fl. The purpose of this study is to investigate the regulatory functions of the Mdm2 and Trp53 genes across different genetic modifications in mice. ### Description of the Data and File Structure: #### Data Organization: The datasets are organized into several Excel sheets, each representing a specific aspect of the genetic and pathway analysis conducted in the study: 1. **Differential Gene Expression Sheets:** * Each sheet corresponds to one of the three mouse groups (Control, Vav-mdm2fl/+, Vav-mdm2fl/+; Trp53 fl/fl). * Columns include Gene ID, Gene Name, Log Fold Change, P-value, and FDR (False Discovery Rate). These metrics are crucial for understanding which genes are upregulated or downregulated in each group. 2. **IPA Analysis Sheet:** * This sheet compiles results from the Ingenuity Pathway Analysis, detailing pathways affected by the differential gene expression observed in the mouse groups. * Columns in this sheet include Pathway Name, P-value (Pathway), Z-score (predicts the effect direction of regulation), and the genes involved in each pathway. #### Potential Use of Data: * **Research and Education:** Researchers and students can use this data to understand the impact of genetic modifications on gene expression in mice. The detailed analysis of upregulated and downregulated genes across different groups provides insights into genetic regulatory mechanisms. * **Pharmaceutical Development:** Insights from this data can help in the development of drugs that target specific pathways altered by genetic modifications in these mouse models. #### Relationships Between Data Files: * The gene expression files for each group should be compared to identify common or unique patterns of expression changes across different genetic backgrounds. * The IPA analysis results are directly derived from the differential expression data, providing a pathway-based context that helps in interpreting the biological significance of the gene expression changes. #### Missing Data Codes and Abbreviations: * **NA (Not Available):** Used in cells where data is missing or not applicable. * **Log FC (Log Fold Change):** Represents the logarithm of the fold change in gene expression, indicating the magnitude of upregulation or downregulation. * **FDR (False Discovery Rate):** A statistical method used in multiple hypothesis testing to correct for the chance of false positives. * **P-value:** A statistical measure that helps determine the significance of the results obtained. Lower values indicate more significant differences. * **Z-score (in IPA Sheet):** Indicates whether a pathway is likely activated or inhibited based on the observed gene expression changes. ### Descriptive Details: This dataset is structured to provide a comprehensive overview of the genetic impacts due to specific mutations in the Mdm2 and Trp53 genes in mice. By analyzing these sheets, researchers can identify key genes and pathways influenced by these mutations, facilitating further studies into their biological roles and implications in disease contexts. The clear separation of data into specific groups and analyses (differential expression and IPA) allows for systematic exploration and comparison across different experimental conditions.