Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach.

OBJECTIVES: This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent complication in critically ill sepsis patients. METHODS: GWAS data was analyzed for genetic association between AKI and sepsis. Then, we systematically applied three distinct machine learning algorithms (LASSO, SVM-RFE, RF) to rigorously identify and validate signature genes of SA-AKI, assessing their diagnostic and prognostic value through ROC curves and survival analysis. The study also examined the functional and immunological aspects of these genes, potential drug targets, and ceRNA networks. A mouse model of sepsis was created to test the reliability of these signature genes. RESULTS: LDSC confirmed a positive genetic correlation between AKI and sepsis, although no significant shared loci were found. Bidirectional MR analysis indicated mutual increased risks of AKI and sepsis. Then, 311 key genes common to sepsis and AKI were identified, with 42 significantly linked to sepsis prognosis. Six genes, selected through LASSO, SVM-RFE, and RF algorithms, showed excellent predictive performance for sepsis, AKI, and SA-AKI. The models demonstrated near-perfect AUCs in both training and testing datasets, and a perfect AUC in a sepsis mouse model. Significant differences in immune cells, immune-related pathways, HLA, and checkpoint genes were found between high- and low-risk groups. The study identified 62 potential drug treatments for sepsis and AKI and constructed a ceRNA network. CONCLUSIONS: The identified signature genes hold potential clinical applications, including prognostic evaluation and targeted therapeutic strategies for sepsis and AKI. However, further research is needed to confirm these findings.

Adaptive block imaging based on compressive sensing in AFM.

Atomic force microscopy (AFM) is a kind of high-precision instrument to measure the surface morphology of various conductive or nonconductive samples. However, obtaining a high-resolution image with standard AFM scanning requires more time. Using block compressive sensing (BCS) is an effective approach to achieve rapid AFM imaging. But, the routine BCS-AFM imaging is difficult to balance the image quality of each local area. It is easy to lead to excessive sampling in some flat areas, resulting in time-consuming. At the same time, there is a lack of sampling in some areas with significant details, resulting in poor imaging quality. Thus, an innovative adaptive BCS-AFM imaging method is proposed. The overlapped block is used to eliminate blocking artifacts. Characteristic parameters (GTV, Lu, and SD) are used to predict the local morphological characteristics of the samples. Back propagation neural network is employed to acquire the appropriate sampling rate of each sub-block. Sampling points are obtained by pre-scanning and adaptive supplementary scanning. Afterward, all sub-block images are reconstructed using the TVAL3 algorithm. Each sample is capable of achieving uniform, excellent image quality. Image visual effects and evaluation indicators (PSNR and SSIM) are employed for the purpose of evaluating and analyzing the imaging effects of samples. Compared with two nonadaptive and two other adaptive imaging schemes, our proposed scheme has the characteristics of a high degree of automation, uniformly high-quality imaging, and rapid imaging speed. HIGHLIGHTS: The proposed adaptive BCS method can address the issues of uneven image quality and slow imaging speed in AFM. The appropriate sampling rate of each sub-block of the sample can be obtained by BP neural network. The introduction of GTV, Lu, and SD can effectively reveal the morphological features of AFM images. Seven samples with different morphology are used to test the performance of the proposed adaptive algorithm. Practical experiments are carried out with two samples to verify the feasibility of the proposed adaptive algorithm.

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing.

We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcriptome sequencing and bulk RNA-seq. Using single-cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage-related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 × 3 convolutional neural network created an additional diagnostic model, while molecular docking identified potential drugs. The diagnostic model based on the 10 selected genes achieved excellent performance, with an AUC of 1 in both training and validation datasets. The heatmap, combining cell abundance and gene expression, provided insights into SAH cellular composition. The convolutional neural network model exhibited a sensitivity and specificity of 1 in both datasets. Additionally, CD14, GPNMB, SPP1 and PRDX5 were specifically expressed in SAH-associated macrophages, highlighting its potential as a therapeutic target. Network pharmacology analysis identified some targeting drugs for SAH treatment. Our study characterised SAH macrophage subpopulations and identified key associated genes. We developed a robust diagnostic model and recognised CD14, GPNMB, SPP1 and PRDX5 as potential therapeutic targets. Further experiments and clinical investigations are needed to validate these findings and explore the clinical implications of targets in SAH treatment.

Dynamic Electrostatic Interfacial Engineering for Block Copolymer Microparticles with Reversible Structures.

Responsive nanoparticle surfactants (NPSs) can dynamically and reversibly modulate the interfacial interactions between incompatible components, which are essential in the interfacial catalysis, corrosion, and self-assembly of block copolymers (BCPs). However, NPSs with stimuli-responsive behavior often involve tedious chemical synthesis and surface modifications. Herein, we propose a strategy to in situ construct a kind of dynamic and reversible NPSs by the interfacial electrostatic interaction between the negatively charged nanoparticles (NPs) and the positively charged homopolymers. The NPSs assembled at the oil/water interface reduce the interfacial tension and direct the confined assembly of BCP. Meanwhile, the dynamic NPSs can be disassembled by increasing the pH value or introducing competitive electrostatic attractions, which can dynamically and reversibly change the interfacial properties as well as the alignment of polymer chains, enabling BCP microparticles with reversibly switchable lamellar and cylindrical structures. Furthermore, by the introduction of aggregation-induced emission luminogens as tails to the NPSs, the reversible transformation of BCP microparticles can be visualized by fluorescence emission, which is dependent on the nanostructures of microparticles. This work establishes a concept for dynamically manipulating interfacial interactions and reversibly switching BCP microparticles without time-consuming NPS synthesis, showing promising applications in the fabrication of smart materials with switchable structures and properties.

Therapeutic effects of orexin-A in sepsis-associated encephalopathy in mice.

BACKGROUND: Sepsis-associated encephalopathy (SAE) causes acute and long-term cognitive deficits. However, information on the prevention and treatment of cognitive dysfunction after sepsis is limited. The neuropeptide orexin-A (OXA) has been shown to play a protective role against neurological diseases by modulating the inflammatory response through the activation of OXR1 and OXR2 receptors. However, the role of OXA in mediating the neuroprotective effects of SAE has not yet been reported. METHODS: A mouse model of SAE was induced using cecal ligation perforation (CLP) and treated via intranasal administration of exogenous OXA after surgery. Mouse survival, in addition to cognitive and anxiety behaviors, were assessed. Changes in neurons, cerebral edema, blood-brain barrier (BBB) permeability, and brain ultrastructure were monitored. Levels of pro-inflammatory factors (IL-1ß, TNF-α) and microglial activation were also measured. The underlying molecular mechanisms were investigated by proteomics analysis and western blotting. RESULTS: Intranasal OXA treatment reduced mortality, ameliorated cognitive and emotional deficits, and attenuated cerebral edema, BBB disruption, and ultrastructural brain damage in mice. In addition, OXA significantly reduced the expression of the pro-inflammatory factors IL-1ß and TNF-α, and inhibited microglial activation. In addition, OXA downregulated the expression of the Rras and RAS proteins, and reduced the phosphorylation of P-38 and JNK, thus inhibiting activation of the MAPK pathway. JNJ-10,397,049 (an OXR2 blocker) reversed the effect of OXA, whereas SB-334,867 (an OXR1 blocker) did not. CONCLUSION: This study demonstrated that the intranasal administration of moderate amounts of OXA protects the BBB and inhibits the activation of the OXR2/RAS/MAPK pathway to attenuate the outcome of SAE, suggesting that OXA may be a promising therapeutic approach for the management of SAE.

Orexin-A mediates glioblastoma proliferation inhibition by increasing ferroptosis triggered by unstable iron pools and GPX4 depletion.

Glioblastoma (GBM) represents a prevalent form of primary malignant tumours in the central nervous system, but the options for effective treatment are extremely limited. Ferroptosis, as the most enriched programmed cell death process in glioma, makes a critical difference in glioma progression. Consequently, inducing ferroptosis has become an appealing strategy for tackling gliomas. Through the utilization of multi-omics sequencing data analysis, flow cytometry, MDA detection and transmission electron microscopy, the impact of orexin-A on ferroptosis in GBM was assessed. In this report, we provide the first evidence that orexin-A exerts inhibitory effects on GBM proliferation via the induction of ferroptosis. This induction is achieved by instigating an unsustainable increase in iron levels and depletion of GPX4. Moreover, the regulation of TFRC, FTH1 and GPX4 expression through the targeting of NFE2L2 appears to be one of the potential mechanisms underlying orexin-A-induced ferroptosis.

Machine learning unveils immune-related signature in multicenter glioma studies.

In glioma molecular subtyping, existing biomarkers are limited, prompting the development of new ones. We present a multicenter study-derived consensus immune-related and prognostic gene signature (CIPS) using an optimal risk score model and 101 algorithms. CIPS, an independent risk factor, showed stable and powerful predictive performance for overall and progression-free survival, surpassing traditional clinical variables. The risk score correlated significantly with the immune microenvironment, indicating potential sensitivity to immunotherapy. High-risk groups exhibited distinct chemotherapy drug sensitivity. Seven signature genes, including IGFBP2 and TNFRSF12A, were validated by qRT-PCR, with higher expression in tumors and prognostic relevance. TNFRSF12A, upregulated in GBM, demonstrated inhibitory effects on glioma cell proliferation, migration, and invasion. CIPS emerges as a robust tool for enhancing individual glioma patient outcomes, while IGFBP2 and TNFRSF12A pose as promising tumor markers and therapeutic targets.

Pan-cancer analysis revealed prognosis value and immunological relevance of RAMPs.

Whether receptor activity-modifying proteins (RAMPs) play a key role in human cancer prognosis and immunity remains unknown. We used data from the public databases, The Cancer Genome Atlas, Therapeutically Applicable Research to Generate Effective Treatments, and the Genotype-Tissue Expression project. We utilized bioinformatics methods, R software, and a variety of online databases to analyze RAMPs. In general, RAMPs were significantly and differentially expressed in multiple tumors, and RAMP expression was closely associated with prognosis, immune checkpoints, RNA-editing genes, tumor mutational burden, microsatellite instability, ploidy, and stemness indices. In addition, the expression of RAMPs is strongly correlated with tumor-infiltrating lymphocytes in human cancers. Moreover, the RAMP co-expression network is largely involved in many immune-related biological processes. Quantitative reverse transcription polymerase chain reaction and Western blot proved that RAMP3 was highly expressed in glioma, and RAMP3 promoted tumor proliferation and migration. RAMPs exhibit potential as prognostic and immune-related biomarkers in human cancers. Moreover, RAMPs can be potentially developed as therapeutic targets or used to enhance the efficacy of immunotherapy.

Hierarchical Porous Nonprecious High-entropy Alloys for Ultralow Overpotential in Hydrogen Evolution Reaction.

Water electrolysis is considered the cleanest method for hydrogen production. However, the widespread popularization of water splitting is limited by the high cost and scarce resources of efficient platinum group metals. Hence, it is imperative to develop an economical and high-performance electrocatalyst to improve the efficiency of hydrogen evolution reaction (HER). In this study, a hierarchical porous sandwich structure is fabricated through dealloying FeCoNiCuAl2 Mn high-entropy alloy (HEA). This free-standing electrocatalyst shows outstanding HER performance with a very small overpotential of 9.7 mV at 10 mA cm-2 and a low Tafel slope of 56.9 mV dec-1 in 1 M KOH solution, outperforming commercial Pt/C. Furthermore, this electrocatalytic system recorded excellent reaction stability over 100 h with a constant current density of 100 mA cm-2 . The enhanced electrochemical activity in high-entropy alloys results from the cocktail effect, which is detected by density functional theory (DFT) calculation. Additionally, micron- and nano-sized pores formed during etching boost mass transfer, ensuring sustained electrocatalyst performance even at high current densities. This work provides a new insight for development in the commercial electrocatalysts for water splitting.

CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With Modality-Correlated Cross-Attention for Brain Tumor Segmentation.

Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain tumor diagnosis, cancer management and research purposes. With the great success of the ten-year BraTS challenges as well as the advances of CNN and Transformer algorithms, a lot of outstanding BTS models have been proposed to tackle the difficulties of BTS in different technical aspects. However, existing studies hardly consider how to fuse the multi-modality images in a reasonable manner. In this paper, we leverage the clinical knowledge of how radiologists diagnose brain tumors from multiple MRI modalities and propose a clinical knowledge-driven brain tumor segmentation model, called CKD-TransBTS. Instead of directly concatenating all the modalities, we re-organize the input modalities by separating them into two groups according to the imaging principle of MRI. A dual-branch hybrid encoder with the proposed modality-correlated cross-attention block (MCCA) is designed to extract the multi-modality image features. The proposed model inherits the strengths from both Transformer and CNN with the local feature representation ability for precise lesion boundaries and long-range feature extraction for 3D volumetric images. To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder. We compare the proposed model with six CNN-based models and six transformer-based models on the BraTS 2021 challenge dataset. Extensive experiments demonstrate that the proposed model achieves state-of-the-art brain tumor segmentation performance compared with all the competitors.

The impact of histological variants in patients with upper tract urothelial carcinoma treated with radical nephroureterectomy.

OBJECTIVE: To investigate the impact of histological variants (HV) in patients with upper tract urothelial carcinoma (UTUC) and analyze the potential association between HV and postoperative bladder recurrence. MATERIALS AND METHODS: The medical records of UTUC patients treated with RNU at our center from January 2012 to December 2019 were retrospectively analyzed. Patients were grouped according to the types of HV. Clinicopathological features and prognostic factors were compared among groups. RESULTS: A total of 629 patients were included in the study: 458 (73%) patients had pure urothelial carcinoma (PUC) and 171 (27%) patients had UTUC with HV. Squamous differentiation was the most common type (124 cases, 19%), followed by glandular differentiation (29 cases, 5.0%). Patients with HV had a higher proportion of T3 and T4 pathologic stages (P < 0.001) as well as high-grade disease (P = 0.002). In the univariate analysis, squamous differentiation and glandular differentiation were significantly associated with worse cancer-specific survival (CSS) (HR 2.22, 95% CI 1.62-3.04, P < 0.001; HR 1.90, 95% CI 1.13-3.20, P = 0.016). However, the multivariate analysis showed that this association became non-significant. We found that HV were associated with recurrent muscle-invasive bladder cancer (MIBC) after RNU and all patients had T2 and T3 initial tumor stages (P = 0.008, P < 0.001). CONCLUSION: We found that UTUC patients with HV were associated with biologically aggressive disease and recurrent MIBC after RNU. The detection of bladder recurrence following surgery needs to be given more attention in advanced UTUC patients with HV.

Automatic classification of arrhythmias using multi-branch convolutional neural networks based on channel-based attention and bidirectional LSTM.

Cardiac arrhythmia is an abnormal rhythm of the heartbeat and can be life-threatening Electrocardiogram (ECG) is a technology that uses an electrocardiograph machine to record a graph of the changes in electrical activity produced by the heart at each cardiac cycle. ECG can generally be used to check whether the examinee has arrhythmia, ion channel disease, cardiomyopathy, electrolyte disorder and other diseases. To reduce the workload of doctors and improve the accuracy of ECG signal recognition, a novel and lightweight automatic ECG classification method based on Convolutional Neural Network (CNN) is proposed. The multi-branch network with different receptive fields is used to extract the multi-spatial deep features of heartbeats. The Channel Attention Module (CAM) and Bidirectional Long Short-Term Memory neural network (BLSTM) module are used to filter redundant ECG features. CAM and BLSTM are beneficial for distinguishing different categories of heartbeats. In the experiments, a four-fold cross-validation technique is used to improve the generalization capability of the network, and it shows good performance on the testing set. This method divides heartbeats into five categories according to the American Advancement of Medical Instrumentation (AAMI) criteria, which is validated in the MIT-BIH arrhythmia database. The sensitivity of this method to Ventricular Ectopic Beat (VEB) is 98.5% and the F1 score is 98.2%. The precision of the Supraventricular Ectopic Beat (SVEB) is 91.1%, and the corresponding F1 score is 90.8%. The proposed method has high classification performance and a lightweight feature. In a word, it has broad application prospects in clinical medicine and health testing.

Pax transactivation domain-interacting protein is required for preserving hematopoietic stem cell quiescence via regulating lysosomal activity.

Hematopoietic stem cells (HSC) maintain lifetime whole blood hematopoiesis through self-renewal and differentiation. In order to sustain HSC stemness, most HSC reside in a quiescence state, which is affected by diverse cellular stress and intracellular signal transduction. How HSC accommodate those challenges to preserve lifetime capacity remains elusive. Here we show that Pax transactivation domain-interacting protein (PTIP) is required for preserving HSC quiescence via regulating lysosomal activity. Using a genetic knockout mouse model to specifically delete Ptip in HSC, we find that loss of Ptip promotes HSC exiting quiescence, and results in functional exhaustion of HSC. Mechanistically, Ptip loss increases lysosomal degradative activity of HSC. Restraining lysosomal activity restores the quiescence and repopulation potency of Ptip-/- HSC. Additionally, PTIP interacts with SMAD2/3 and mediates transforming growth factor-ß signaling-induced HSC quiescence. Overall, our work uncovers a key role of PTIP in sustaining HSC quiescence via regulating lysosomal activity.

Multiomics integration reveals the effect of Orexin A on glioblastoma.

Objectives: This study involved a multi-omics analysis of glioblastoma (GBM) samples to elaborate the potential mechanism of drug treatment. Methods: The GBM cells treated with or without orexin A were acquired from sequencing analysis. Differentially expressed genes/proteins/metabolites (DEGs/ DEPs/ DEMs) were screened. Next, combination analyses were conducted to investigate the common pathways and correlations between the two groups. Lastly, transcriptome-proteome-metabolome association analysis was carried out to determine the common pathways, and the genes in these pathways were analyzed through Kaplan-Meier (K-M) survival analysis in public databases. Cell and animal experiments were performed to investigate the anti-glioma activity of orexin A. Results: A total of 1,527 DEGs, 52 DEPs, and 153 DEMs were found. Moreover, the combination analyses revealed that 6, 4, and 1 common pathways were present in the transcriptome-proteome, proteome-metabolome, and transcriptome-metabolome, respectively. Certain correlations were observed between the two data sets. Finally, 11 common pathways were discovered in association analysis, and 138 common genes were screened out in these common pathways. Six genes showed significant differences in terms of survival in both TCGA and CGGA. In addition, orexin A inhibited the proliferation, migration, and invasion of glioma in vitro and in vivo. Conclusion: Eleven common KEGG pathways with six common genes were found among different omics participations, revealing the underlying mechanisms in different omics and providing theoretical basis and reference for multi-omics research on drug treatment.

Mast cell activation mediates blood-brain barrier impairment and cognitive dysfunction in septic mice in a histamine-dependent pathway.

Introduction: Sepsis-associated encephalopathy (SAE) is a diffuse cerebral dysfunction resulting from a systemic inflammatory response to infection; however, its pathophysiology remains unclear. Sepsis-induced neuroinflammation and blood-brain barrier (BBB) disruption are crucial factors in brain function disturbance in SAE. Mast cells (MCs) activation plays an important role in several neuroinflammation models; however, its role in SAE has not been comprehensively investigated. Methods: We first established a SAE model by cecal ligation puncture (CLP) surgery and checked the activation of MCs. MCs activation was checked using immumohistochemical staining and Toluidine Blue staining. We administrated cromolyn (10mg/ml), a MC stabilizer, to rescue the septic mice. Brain cytokines levels were measured using biochemical assays. BBB disruption was assessed by measuring levels of key tight-junction (TJ) proteins. Cognitive function of mice was analyzed by Y maze and open field test. Transwell cultures of brain microvascular endothelial cells (BMVECs) co-cultured with MCs were used to assess the interaction of BMVECs and MCs. Results: Results showed that MCs were overactivated in the hippocampus of CLP-induced SAE mice. Cromolyn intracerebroventricular (i.c.v) injection substantially inhibited the MCs activation and neuroinflammation responses, ameliorated BBB impairment, improved the survival rate and alleviated cognitive dysfunction in septic mice. In vitro experiments, we revealed that MCs activation increased the sensitivity of BMVECs against to lipopolysaccharide (LPS) challenge. Furthermore, we found that the histamine/histamine 1 receptor (H1R) mediated the interaction between MCs and BMVECs, and amplifies the LPS-induced inflammatory responses in BMVECs by modulating the TLR2/4-MAPK signaling pathway. Conclusions: MCs activation could mediate BBB impairment and cognitive dysfunction in septic mice in a histamine-dependent pathway.

Identifying miRNA-Gene Common and Specific Regulatory Modules for Cancer Subtyping by a High-Order Graph Matching Model.

Identifying regulatory modules between miRNAs and genes is crucial in cancer research. It promotes a comprehensive understanding of the molecular mechanisms of cancer. The genomic data collected from subjects usually relate to different cancer statuses, such as different TNM Classifications of Malignant Tumors (TNM) or histological subtypes. Simple integrated analyses generally identify the core of the tumorigenesis (common modules) but miss the subtype-specific regulatory mechanisms (specific modules). In contrast, separate analyses can only report the differences and ignore important common modules. Therefore, there is an urgent need to develop a novel method to jointly analyze miRNA and gene data of different cancer statuses to identify common and specific modules. To that end, we developed a High-Order Graph Matching model to identify Common and Specific modules (HOGMCS) between miRNA and gene data of different cancer statuses. We first demonstrate the superiority of HOGMCS through a comparison with four state-of-the-art techniques using a set of simulated data. Then, we apply HOGMCS on stomach adenocarcinoma data with four TNM stages and two histological types, and breast invasive carcinoma data with four PAM50 subtypes. The experimental results demonstrate that HOGMCS can accurately extract common and subtype-specific miRNA-gene regulatory modules, where many identified miRNA-gene interactions have been confirmed in several public databases.

YBX1 regulates the survival of chronic myeloid leukemia stem cells by modulating m6A-mediated YWHAZ stability.

PURPOSE: Chronic myeloid leukemia (CML) is a myeloproliferative disease derived from hematopoietic stem cells (HSCs) that harbor Philadelphia chromosome (Ph chromosome). In clinic, leukemia stem cells (LSCs) in CML are insensitive to the treatment with tyrosine kinase inhibitors, and are responsible for disease relapse. However, the molecular mechanisms for maintaining LSCs survival remain elusive. METHODS: CML patient-derived cell lines and BCR-ABL-induced CML mouse model were used to explore the role of YBX1 in regulating the survival of CML LSCs. Bone marrow transduction and transplantation, and colony-forming unit assay were used to investigate LSC function. The underlying mechanism of how YBX1 regulates LSCs survival were assessed using flow cytometry, RNA sequencing, western blot, RNA decay assay, co-immunoprecipitation and RNA immunoprecipitation. RESULTS: Here we show that RNA-binding protein YBX1 plays an important role in regulating survival of CML LSCs. We find that YBX1 expression is significantly increased in CML cells, and confirm that YBX1 is required for maintaining survival of LSCs. Deletion of YBX1 impairs the propagation of CML through blocking cell proliferation and inducing apoptosis of LSCs. Mechanistically, we find that YBX1 regulates expression of apoptotic associated genes. YBX1 cooperates with RNA m6A reader IGF2BPs to stabilize YWHAZ transcript in an m6A-dependent manner, and loss of YBX1 decreases YWHAZ expression by accelerating mRNA decay. Restoration of YWHAZ efficiently rescues the defects of YBX1-deficient CML cells. CONCLUSION: Our findings reveal a critical role of YBX1 in maintaining survival of CML LSCs, which provides a rationale for targeting YBX1 in CML treatment.

Decoding m6A RNA methylome identifies PRMT6-regulated lipid transport promoting AML stem cell maintenance.

N6-methyladenosine (m6A) is a common chemical modification for mammalian mRNA and exhibits high dynamics in various biological processes. However, dynamics of m6A RNA methylome during leukemogenesis remains unknown. Here, we delineate a comprehensive m6A landscape during acute myeloid leukemia (AML) development and identify PRMT6 as a key for maintaining AML stem cells. We observe an obvious change in m6A methylome during leukemogenesis and find that protein arginine methyltransferase PRMT6 and m6A reader IGF2BP2 maintain the function of human and murine leukemia stem cells (LSCs). Genetic deletion or pharmacological inhibition of PRMT6 damages AML development and LSC function. Mechanistically, IGF2BP2 stabilizes PRMT6 mRNA via m6A-mediated manner, which catalyzes H3R2me2a and suppresses lipid transporter MFSD2A expression. PRMT6 loss upregulates MFSD2A expression that increases docosahexaenoic acid levels and impairs LSC maintenance. Collectively, our findings reveal a critical role of PRMT6-MFSD2A signaling axis in AML development and provide a therapeutic strategy for targeting LSCs.

The Role of DACT Family Members in Tumorigenesis and Tumor Progression.

Disheveled-associated antagonist of ß-catenin (DACT), which ubiquitously expressed in human tissue, is critical for regulating cell proliferation and several developmental processes in different cellular contexts. In addition, DACT is essential for some other cellular processes, such as cell apoptosis, migration and differentiation. Given the importance of DACT in these cellular processes, many scientists are gradually interested in studying the role of DACT in tumorigenesis and cancer progression. This review article focuses on the latest research regarding the essential functions and potential DACT mechanisms in the occurrence and progression of tumors. Our study indicates that DACT may act as a tumor biomarker for cancer diagnosis and prognosis, as well as a promising therapeutic target in cancers.