Vitiligo is an autoimmune skin disorder primarily characterized by the absence of melanocytes, leading to the development of white patches on the patient's skin. Narrowband Ultraviolet B (NB-UVB) therapy is among the most effective approaches for stimulating the reformation of hyperpigmentation. This treatment utilizes a narrow spectrum of NBUVB wavelengths ranging from 311 to 313 nm to irradiate the affected area, thereby preventing the destruction of migrating and proliferating melanocytes. Nevertheless, the molecular alterations occurring in both the hair follicle and the interfollicular epidermis during NB-UVB treatment remain unknown. In this study, we conducted a comprehensive analysis of the consistency of differentially expressed genes (DEGs) within the enrichment pathways both before and after NB-UVB treatment, utilizing a bioinformatics approach. Furthermore, we employed CYTOHUBBA and Random Forest algorithms to identify and sequence hub genes from the pool of DEGs. Following validation of these hub genes through ROC curve analysis, we proceeded to construct an interaction network between these hub genes, miRNA, and drugs. Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) was used to further verify the difference in the expression of hub genes between the disease group and the control group. Gene Set Enrichment Analysis of DEGs indicated strong associations with vitiligo in most pathways. Subsequently, we conducted Gene Ontology and Metascape enrichment analyses on the overlapping genes from DEGs. We identified key genes (COL11A1, IGFBP7, LOX, NTRK2, SDC2, SEMA4D, and VEGFA) within the Protein-Protein Interaction (PPI) network. We further explored potential drugs that could be used for the clinical treatment of vitiligo through the drug-hub gene interaction network. Finally, the results of RT-qPCR experiments demonstrated that the expression levels of the identified hub genes in both groups were consistent with the bioinformatics analysis results. The hub genes obtained in this study may be a biomarker related to the development of vitiligo pigmentation. Our research not only contributes to a better understanding of the treatment mechanisms of vitiligo but also provides valuable insights for future personalized medical approaches and targeted therapies for vitiligo.

Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

Background: People with type 1 diabetes mellitus (T1DM) are significantly more likely to have osteoporosis (OP). Astragali Radix is a Chinese herbal medicine containing various active ingredients, and several clinical trials have been reported to use it to treat OP and T1DM, respectively. Objective: To evaluate the targets and potential mechanisms of Astragali Radix administration on OP and T1DM. Methods: The targets of Astragali Radix were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The OP and T1DM datasets were downloaded from the Gene Expression Omnibus (GEO) database. The weighted gene correlation network analysis (WGCNA) method was used to identify the co-expression genes associated with OP and T1DM. In addition, the common gene targets of OP and T1DM were screened using two public databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the R tool. After the validation of key genes, molecular docking was performed to visualize small molecule-protein interactions. Results: The compound target network mainly contained 17 compounds and 147 corresponding targets. There were 561 GO items and 154 signaling pathways in KEGG, mainly including the AGE-RAGE signaling pathway in diabetic complications and osteoclast differentiation. The results of molecular docking showed that flavonoids were the top compound of Astragali Radix, which had a high affinity with CDK2, VEGFA, and MYC. Conclusion: Flavonoids in Astragali Radix may regulate multiple signaling pathways through MYC, CDK2, and VEGFA, which may play a therapeutic role in OP and T1DM.

The study aimed to study the differential gene expression and immune cell infiltration in patients with steroid-induced necrosis of the femoral head (SANFH), identify the key genes and immune cells of SANFH, and explore the relationship between immune cells and SANFH. The high-throughput gene chip dataset GSE123568 was downloaded from the GEO database, and the differential gene expression was analyzed with the R language. The STRING database and Cytoscape software were used to analyze the protein interaction network and screen key genes, and enrichment analysis was carried out on key genes. The infiltration of immune cells in SANFH patients was analyzed and verified by immunohistochemistry. EP300, TRAF6, STAT1, JAK1, CASP8, and JAK2 are key genes in the pathogenesis of SANFH, which mainly involve myeloid cell differentiation, cytokine-mediated signaling pathway, tumor necrosis factor-mediated signaling pathway, and cellular response to tumor necrosis factor through JAK-STAT, NOD-like receptor, toll-like receptor, and other signaling pathways, leading to the occurrence of diseases; immune infiltration and immunohistochemical results have shown the expression of memory B cells and activated dendritic cells as reduced in SANFH patients, while in the same SANFH samples, M1 macrophages have been positively correlated with monocytes, and neutrophils have been negatively correlated with monocytes expression. EP300, TRAF6, STAT1, JAK1, CASP8, and JAK2 have exhibited significant differences in SANFH (spontaneous osteonecrosis of the femoral head). Memory B cells, activated dendritic cells, M1 macrophages, monocytes, and neutrophils have shown abnormal expression in SANFH.

Non-small-cell lung cancer (NSCLC) is one of the most prevalent malignancies and poses a significant threat to human health. Qing-Jin-Hua-Tan (QJHT) decoction is a classical herbal remedy that has demonstrated therapeutic effects in various diseases, including NSCLC, and can improve the quality of life of patients with respiratory conditions. However, the mechanism underlying the effect of the QJHT decoction on NSCLC remains unclear and requires further investigation. We collected NSCLC-related gene datasets from the GEO database and performed differential gene analysis, followed by using WGCNA to identify the core set of genes associated with NSCLC development. The TCMSP and HERB databases were searched to identify the active ingredients and drug targets, and the core gene target datasets related to NSCLC were merged to identify the intersecting targets of drugs and diseases for GO and KEGG pathway enrichment analysis. We then constructed a protein-protein interaction (PPI) network map of drug diseases using the MCODE algorithm and identified key genes using topology analysis. The disease-gene matrix underwent immunoinfiltration analysis, and we analyzed the association between intersecting targets and immunoinfiltration. We obtained the GSE33532 dataset that met the screening criteria, and a total of 2211 differential genes were identified using differential gene analysis. We performed GSEA analysis and WGCNA analysis for a crossover with differential genes, resulting in 891 key targets for NSCLC. The drug database was screened to obtain 217 active ingredients and 339 drug targets of QJHT. By constructing a PPI network, the active ingredients of QJHT decoction were intersected with the targets of NSCLC, resulting in 31 intersected genes. Enrichment analysis of the intersection targets showed that 1112 biological processes, 18 molecular functions, and 77 cellular compositions were enriched in GO functions, and 36 signaling pathways were enriched in KEGG pathways. Based on immune-infiltrating cell analysis, we found that the intersection targets were significantly associated with multiple infiltrating immune cells. Our analysis using network pharmacology and mining of the GEO database revealed that QJHT decoction can potentially treat NSCLC through multi-target and multi-signaling pathways, while also regulating multiple immune cells.

Danshen Chuanxiong Injection (DCI) has demonstrated significant clinical efficacy in the treatment of acute pancreatitis (AP); however, the precise molecular mechanisms underlying its therapeutic effects remain incompletely understood. In this study, we employed network pharmacology analysis to comprehensively investigate the active components, potential targets, and signaling pathways involved in DCI-mediated treatment of AP. We utilized the mouse pancreatic acinar cell line 266-6 to establish an cholecystokinin (CCK)-induced AP cell injury model and evaluated cell viability using the Cell counting kit-8 assay. Western blotting and quantitative PCR were employed to determine the expression levels of key target proteins and genes. Network pharmacology analysis identified a total of 144 active components and 430 potential targets within DCI. By integrating data from public databases, we identified 762 AP-related genes. Among these, we identified 93 potential targets that may be involved in the therapeutic effects of DCI for AP. These targets were significantly enriched in biological processes such as oxidative stress, regulation of cytokine production, leukocyte migration, and the TNF signaling pathway. Molecular docking studies revealed a high binding affinity between the active components and the key targets AKT1 and NFKBA, indicative of potential interaction. Additionally, CCK-induced acinar cell injury led to upregulation of AKT1, NFKBA, and P53 proteins, as well as TNF, IL6, and MMP9 genes. Conversely, treatment with DCI dose-dependently attenuated CCK-induced acinar cell injury and restored the expression levels of the aforementioned proteins and genes. Overall, this study provides a comprehensive understanding of the molecular mechanisms underlying the therapeutic effects of DCI in the treatment of AP. Our findings confirm the protective effect of DCI against CCK-induced acinar cell injury and its regulation of key targets.

Wetland microbial communities play a vital role in ecosystem functioning, particularly in the intricate processes of carbon cycling. This study employed metagenomic sequencing to investigate the diversity, composition, structural differences, carbon cycling functional gene, and microbial species of soil microbial communities in five distinct soil types of the Yalu River estuary wetland, including shoal soil, bog soil, paddy soil, meadow soil, and brown forest soil. We further explored the influence of environmental factors on both the microbial community structure and carbon cycling functional genes. Our results revealed a bacterial-dominated soil microbial community, constituting about 97.6%. Archaea and fungi represented relatively minor fractions, at 1.9% and 0.4%, respectively. While no significant differences were observed in Chao1 indices between bacterial and fungal communities, the Shannon index revealed notable differences. Both Chao1 and Shannon indices exhibited significant variations within the archaeal communities. The dominant bacterial phyla were Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes, and Nitrospirae. Thaumarchaeota, Crenarchaeota, and Euryarchaeota formed the major archaeal phyla, while Ascomycota, Mucoromycota, and Basidiomycota were the dominant fungal phyla. Non-metric multidimensional scaling (NMDS) analysis based on Bray-Curtis distance revealed notable differences in the bacterial, archaeal, and fungal community structures across the samples. Redundancy analysis (RDA) identified key environmental factors for the major phyla. Soil pH, soil organic carbon (SOC), electrical conductivity (EC), and total phosphorus (TP) were the main influencing factors for bacteria, while soil TP, EC, total sulfur (TS), and SOC were the primary drivers for archaeal phyla. Soil total nitrogen (TN) and EC were the main influencing factors for fungal phyla. Analysis of key carbon cycling pathway genes utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and clustering heatmap revealed some variations in functional gene composition across different soil types. Mantel test indicated that pH, TN, and SOC were the primary environmental factors influencing microbial functional genes associated with soil carbon cycling. Stratified bar chart analysis further demonstrated that the major contributors to carbon cycling originated from corresponding dominatnt phyla and genera of Proteobacteria, Thaumarchaeota, Actinomycetota, Euryarchaeota, and Bacteroidota. The species and relative abundance of microorganisms associated with carbon cycling pathways varied among the samples. These findings provide a crucial reference for informing the conservation and sustainable management of wetland ecosystems in the Yalu River estuary.

Background: Preeclampsia (PE) is a severe pregnancy complication associated with autophagy. Objective: This research sought to uncover autophagy-related genes in pre-eclampsia through bioinformatics and machine learning. Methods: GSE75010 from the GEO series was subjected to WGCNA to identify key modular genes in PE. Autophagy genes retrieved from the THANATOS overlapped with the modular genes to yield PErelated autophagy genes. Furthermore, the crucial step involved the utilization of two machine learning algorithms (LASSO and SVM-RFE) for dimensionality reduction. The candidate gene was further verified by quantitative reverse transcription polymerase chain reaction, western blot, and immunohistochemistry. Preliminary experiments were conducted on HTR-8/SVneo cell lines to explore the role of candidate genes in autophagy regulation. Results: WGCNA identified 291 genes from 5 hubs, and after overlapping with 1087 autophagy-related genes obtained from THANATOS, 42 PE-related ARGs were identified. ANXA6 was recognized as a potential target through SVM-RFE and LASSO analyses. The mRNA and protein expression of ANXA6 were verified in placenta samples. In HTR8/SVneo cells, modulating ANXA6 expression altered autophagy levels. Knocking down ANXA6 resulted in an anti-autophagy effect, which was reversed by treatment with CAL101, an inhibitor of PI3K, Akt, and mTOR. Conclusion: We observed that ANXA6 may serve as a possible PE action target and that autophagy may be crucial to the pathogenesis of PE.

Cancer stem cells (CSCs) contribute to metastasis and drug resistance to immunotherapy in lung adenocarcinoma (LUAD), so the stemness evaluation of cancer cells is of great significance. The single-cell RNA sequencing (scRNA-seq) data of the GSE149655 dataset were collected and analyzed. Malignant cells were distinguished by CopyKAT. CytoTRACE score of marker genes in malignant cells was counted by CytoTRACE to construct the stemness score formula. Sample stemness score in TCGA was determined by the formula and divided into high-, medium- and low-stemness score groups. LASSO and COX regression analyses were carried out to screen the key genes related to the prognosis of LUAD from the differentially expressed genes (DEGs) in high- and low-stemness score groups and a risk score model was constructed. Seven types of cells were identified from a total of 4 samples, and 193 marker genes of 3455 malignant cells were identified. There were 1098 DEGs between low- and high-stemness score groups of TCGA, of which CPS1, CENPK, GJB3, and TPSB2 constituted gene signatures. The 4-gene signature could independently evaluate LUAD survival in the training and validation sets and showed an acceptable area under the receiver operator characteristic (ROC) curves (AUCs). This study provides insights into the cellular heterogeneity of LUAD and develops a new cancer stemness evaluation indicator and a 4-gene signature as a potential tool for evaluating the response of LUAD to immune checkpoint blockade (ICB) therapy or antineoplastic therapy.

This research intended to predict the active ingredients and key target genes of Indigo Naturalis in treating human chronic myeloid leukemia (CML) using network pharmacology and conduct the invitro verification. The active components of Indigo Naturalis and the corresponding targets and leukemia-associated genes were gathered through public databases. The core targets and pathways of Indigo Naturalis were predicted through protein-protein interaction (PPI) network, gene ontology (GO) function, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Next, after intersecting with leukemia-related genes, the direct core target gene of Indigo Naturalis active components was identified. Subsequently, HL-60 cells were stimulated with indirubin (IND) and then examined for cell proliferation using CCK-8 assay and cell cycle, cell apoptosis, and mitochondrial membrane potential using flow cytometry. The content of apoptosis-associated proteins (Cleaved Caspase 9, Cleaved Caspase 7, Cleaved Caspase 3, and Cleaved parp) were detected using Western blot, HSP90AA1 protein, and PI3K/Akt signaling (PI3K, p-PI3K, Akt, and p-Akt) within HL-60 cells. A total of 9 active components of Indigo Naturalis were screened. The top 10 core target genes (TNF, PTGS2, RELA, MAPK14, IFNG, PPARG, NOS2, IKBKB, HSP90AA1, and NOS3) of Indigo Naturalis active components within the PPI network were identified. According to the KEGG enrichment analysis, these targets were associated with leukemia-related pathways (such as acute myeloid leukemia and CML). After intersecting with leukemia-related genes, it was found that IND participated in the most pairs of target information and was at the core of the target network; HSP90AA1 was the direct core gene of IND. Furthermore, the in-vitro cell experiments verified that IND could inhibit the proliferation, elicit G2/M-phase cell cycle arrest, enhance the apoptosis of HL-60 cells, reduce mitochondrial membrane potential, and promote apoptosis-related protein levels. Under IND treatment, HSP90AA1 overexpression notably promoted cell proliferation and inhibited apoptosis. Additionally, IND exerted tumor suppressor effects on leukemia cells by inhibiting HSP90AA1 expression. IND, an active component of Indigo Naturalis, could inhibit CML progression, which may be achieved via inhibiting HSP90AA1 and PI3K/Akt signaling expression levels.

Exploring the ferroptosis-related gene lipocalin 2 as a potential biomarker for sepsis-induced acute respiratory distress syndrome based on machine learning

Tuz Lake, a thalassohaline lake with a salt rate of 32%, is a unique habitat for a halophilic microbiome. Culture-dependent and independent methods have been applied to identify prokaryotic microbial diversity in Tuz Lake. However, the key processes and genes involved in biogeochemical cycles in Tuz Lake have not been investigated seasonally. The aim of the study is to investigate seasonal gene profiling in Tuz Lake associated with biogeochemical cycling and thereby reveal more about the ecosystem dynamics of this extreme environment. Therefore, the PICRUSt2 tool was applied to analyze the metabolic function of archaeal and bacterial diversity in Tuz Lake. As a result of metabolic functions based on 16S rDNA amplicon sequencing data, it was observed that methane production by H2 and CO2 by anaerobic archaea in Tuz Lake was the predominant methanogenesis pathway. It was determined that sulfur oxidation was the dominant sulfur metabolism, while the reductive citric acid cycle was the dominant carbon fixation pathway.

Objectives: To analyze the survival benefit of intrahepatic cholangiocarcinoma (ICC) radical resection based on single cell omics. Methods: This is a retrospective case-series study. ICC single-cell sequencing was integrated from four data sets in the Gene Expression Omnibus Database, with a total of 46 patients undergoing radical resection, to explore the characteristics of the microenvironment. Microarray data of 100 ICC cases was analyzed in the EMBI database with survival data. The infiltration abundance of each epithelial cell cluster was calculated in each microarray data sample using the ssGSEA algorithm. The key epithelial cell cluster associated with poor patient outcomes was explored. The clinical value of key marker genes in this subgroup was studied. Prognostic marker genes were selected using the univariate and multivariate Cox proportional hazards(CoxPH) model. The The CoxPH model was constructed by the target genes and a nomogram was drawn. Kaplan-Meier survival analysis was used to verify the relationship between score and prognosis of patients. The predictive power of the model was evaluated by receiver operating characteristic(ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Epithelial cell clusters infiltrated almost exclusively in tumor tissue. The MT2A+ epithelial cell subset was associated with a poorer prognosis for patients with a high invasion abundance and patients characterized by infiltration of this group were defined as antioxidant. After screening marker genes in this cluster using a univariate and multivariate CoxPH model, the following genes were found to be independent prognostic factors: FILPIL, NFKBIA, PEG10, SERPINB5. The CoxPH model was constructed using the four gene expression levels, and the survival rate of patients in the high-risk group was significantly lower than those in the low-risk group (all P<0.05). The constructed nomogram had good discrimination and validity. The ROC curve showed that the predicted area under the curve was 0.779, 0.844 and 0.845 at 1, 3 and 5 years, respectively. Compared to clinical indicators, the model had better predictive power using the calibration curve and the DCA test. Conclusions: The MT2A+ epithelial cell group may be associated with the prognosis of patients with ICC, and the concept of ICC tissue typing of antioxidant and non-antioxidant types is proposed. The type of antioxidant may predict the poor prognosis of the patients, and postoperative adjuvant therapy and other means could be considered to improve the survival of the patients.

Angiogenesis involves the development of new blood vessels. Biochemical signals start this process in the body, which is followed by migration, growth, and differentiation of endothelial cells that line the inside wall of blood vessels. This process is vital for the growth of cancer cells and tumors. We started our analysis by composing a list of genes that have a validated impact in humans with respect to angiogenesis-related phenotypes. Here, we have investigated the expression patterns of angiogenesis-related genes in the context of previously published single-cell RNA-Seq data from prostate and breast cancer samples. Using a protein-protein interaction network, we showed how different modules of angiogenesis-related genes are overexpressed in different cell types. In our results, genes, such as ACKR1, AQP1, and EGR1, showed a strong cell type-dependent overexpression pattern in the two investigated cancer types, which can potentially be helpful in the diagnosis and follow-up of patients with prostate and breast cancer. Our work demonstrates how different biological processes in distinct cell types contribute to the angiogenesis process, which can provide clues regarding the potential application of targeted inhibition of the angiogenesis process.

BackgroundDrought severely limits sunflower production especially at the seedling stage. To investigate the response mechanism of sunflowers to drought stress, we utilized two genotypes of sunflower materials with different drought resistances as test materials. The physiological responses were investigated under well-watered (0 h) and drought-stressed conditions (24 h, 48 h, and 72 h).ResultsANOVA revealed the greatest differences in physiological indices between 72 h of drought stress and 0 h of drought stress. Transcriptome analysis was performed after 72 h of drought stress. At 0 h, there were 7482 and 5627 differentially expressed genes (DEGs) in the leaves of K55 and K58, respectively, and 2150 and 2527 DEGs in the roots of K55 and K58, respectively. A total of 870 transcription factors (TFs) were identified among theDEGs, among which the high-abundance TF families included AP2/ERF, MYB, bHLH,and WRKY. Five modules were screened using weighted gene coexpressionnetwork analysis (WGCNA), three and two of which were positively and negatively, respectively, related to physiological traits. KEGG analysis revealedthat under drought stress, “photosynthesis”, “carotenoid biosynthesis”, “starch and sucrose metabolism”, “ribosome”, “carotenoid biosynthesis”, “starch and sucrose metabolism”, “protein phosphorylation” and “phytohormone signaling” are six important metabolic pathways involved in the response of sunflower to drought stress. Cytoscape software was used to visualize the three key modules, and the hub genes were screened. Finally, a total of 99 important candidate genes that may be associated with the drought response in sunflower plants were obtained, and the homology of these genes was compared with that in Arabidopsis thaliana.ConclusionsTaken together, our findings could lead to a better understanding of drought tolerance in sunflowers and facilitate the selection of drought-tolerant sunflower varieties.

Prognostic value and potential function of a novel heme-related LncRNAs signature in gastric cancer

The accessible chromatin landscape of lipopolysaccharide-induced systemic inflammatory response identifying epigenome signatures and transcription regulatory networks in chickens

SH3GL2 and MMP17 as lung adenocarcinoma biomarkers: a machine-learning based approach

Prostate cancer (PC) poses significant health risks to men globally. Enhancing our understanding of prostate cancer biology is crucial for facilitating early diagnosis and effective treatment strategies. A multitude of high-throughput sequencing studies, encompassing whole genome resequencing, transcriptome sequencing, and genome-wide association studies, have unearthed vital point mutations, structural variations, and epigenomic alterations linked to prostate cancer. These findings have significantly enriched our knowledge of the genomic framework for prostate cancer. Still, these investigations have predominantly centered on the one- or two-dimensional landscape of the genome. Research in three-dimensional genomics underscores the critical role of the genome's three-dimensional spatial structure in maintaining normal cellular functions. Additionally, it demonstrates that dysregulation of key genes in numerous cancers relates to the chromatin's spatial organization across various levels. This article explores the intricate three-dimensional architecture of chromosomes. It outlines the progressive development of techniques used in three-dimensional genomic research and synthesizes the application of these techniques in the study of prostate cancer biology. Furthermore, it proposes potential therapeutic strategies for prostate cancer.

Abstract CBFB::MYH11 acute myeloid leukemia (AML) is a subset of Core Binding Factor AML initiated by an oncofusion in which Core Binding Factor Beta (CBFB) is fused to the C-terminus of smooth muscle myosin heavy chain 11 (MYH11). CBFB::MYH11 AML has a high cure rate with conventional anthracycline/cytarabine induction and high-dose cytarabine consolidation in patients under the age of 60, and remains chemosensitive after multiple relapses; the mechanisms underlying chemosensitivity are unknown. TP53 is a key regulator of DNA damage response and apoptosis, and TP53-mutated AML is often chemoresistant. Large sequencing studies have revealed only rare co-occurrence of TP53 mutations with CBFB::MYH11. We initially used CBFB::MYH11 as a negative control for an unrelated study of TP53 function and unexpectedly found striking synergy. While expression of CBFB::MYH11 in WT murine hematopoietic stem and progenitor cells (HSPCs) induced a low-penetrance (~20%), long latency (~6 months) myelomonocytic AML, expression of CBFB::MYH11 in Trp53-/- HSPCs induced AML with 100% penetrance and a median latency of 8 weeks. CBFB::MYH11-induced AMLs arising in Trp53-/- cells were similar by morphology and flow cytometry phenotype. We validated these findings in a second model, using a conditional Trp53flox/flox x Vav1-Cre model in which Trp53 is inactivated in HSPCs, with nearly identical results. Trp53-/- cells transduced with Empty Vector did not develop AML, but did develop T cell lymphoma with a median latency of 16 weeks, similar to prior studies. Trp53 deficiency also cooperated with RUNX1::RUNX1T1, the other Core Binding Factor oncofusion, to induce a high-penetrance (100%) and short latency AML (median latency 10 weeks); consistent with prior data, RUNX1::RUNX1T1 expression alone in Trp53 WT cells was insufficient to induce AML. To determine whether Trp53 influences the chemosensitivity of CBFB::MYH11 expressing cells, we performed cytarabine dose-response experiments. While CBFB::MYH11-transduced WT HSPCs were very sensitive to cytarabine (relative to EV), sensitivity was reduced in CBFB::MYH11-transduced Trp53-/- cells, suggesting that CBFB::MYH11-induced chemosensitivity is, at least in part, mediated by the TP53 pathway. To determine whether CBFB::MYH11 expression results in upregulation of key TP53 pathway genes, we transduced WT HSPCs with EV or CBFB::MYH11, transplanted cells into WT mice, harvested bone marrow at 3 months, and performed RNA-Seq. Key TP53 pathway genes were upregulated, including Bbc3/Puma, Bax, Cdkn1a/p21, and Trp53 (all 2.6-4.6-fold, p≤0.05). These data suggest that the chemosensitivity and the favorable prognosis of CBFB::MYH11 AML may be due to the direct activation of the TP53 pathway by CBFB::MYH11. Similar studies are underway with RUNX1::RUNX1T1-transduced cells to determine whether TP53 is relevant for the chemosensitivity of that CBF AML as well. Citation Format: Ryan B. Day, Julia A. Hickman, Casey D. Katerndahl, Sai Mukund Ramakrishnan, Christopher A. Miller, Timothy J. Ley. CBFB::MYH11 expression activates Trp53 in myeloid cells, which inhibits cell growth and sensitizes cells to cytarabine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5589.