Vericiguat improves cardiac function and microcirculation of a male patient with Fabry disease: A case report.

Fabry disease (FD) is a rare X chromosome-linked disorder and can be easily misdiagnosed. Here, we report the case of a 69-year-old male patient with FD who developed heart failure and showed extremely high pulmonary artery pressure. His initial symptom was recurrent atrial fibrillation. The left and right atrial inner diameters were large, and the ventricular wall was thick. Gene analysis which showed GLA c.215T>C p.Met72Thr mutation and single photon emission computed tomography indicated the diagnosis of FD with coronary microvascular dysfunction. The patient was prescribed anti-heart failure drugs, including vericiguat. Following the treatment, his heart function and microvascular perfusion significantly improved, which might be due to the beneficial effects of vericiguat.

Prediction of anti-cancer drug synergy based on cross-matching network and cancer molecular subtypes.

At present, anti-cancer drug synergy therapy is one of the most important methods to overcome drug resistance and reduce drug toxicity in cancer treatment. High-throughput screening through deep learning can effectively improve the efficiency of discovering synergistic drugs. Nowadays, most of the existing deep learning algorithms for anti-cancer drug synergy prediction use deep neural networks and can only implicitly perform feature interaction. This study proposes a deep learning algorithm, named MolCross, which combines implicit feature interaction with explicit features to improve the accuracy of prediction of the anti-cancer drug synergy score. MolCross uses a deep autoencoder to extract features from high-dimensional input, uses the drug-specific subnetworks and cross-network to perform implicit feature interaction and explicit feature interaction respectively, and finally uses a synergy prediction network to combine the two feature interaction methods to obtain the final prediction results. We adopted a five-fold cross validation and compared MolCross with other four anti-cancer drug synergy prediction models. The results show that MolCross has better prediction performance than other models. MolCross also has good performance in terms of cross-cell line and cross-tissue type. Existing studies have demonstrated that cancer molecular subtypes have different sensitivities to targeted therapy. In this study, the features of cancer molecular subtype were introduced in the model using an embedding layer in MolCross to explore the effect of cancer molecular subtype on anti-cancer drug synergy. We also found that the cancer molecular subtype is one of the main factors affecting the synergy between drugs.

Gene signatures to therapeutics: Assessing the potential of ivermectin against t(4;14) multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow. The translocation, (t)(4;14), results in high-risk MM with limited treatment alternatives. Thus, there is an urgent need for identification and validation of potential treatments for this MM subtype. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets. AIM: To elucidate the molecular basis and search for potential effective drugs of t(4;14) MM subtype by employing a comprehensive approach. METHODS: The transcriptional signature of t(4;14) MM was sourced from the Gene Expression Omnibus. Two datasets, GSE16558 and GSE116294, which included 17 and 15 t(4;14) MM bone marrow samples, and five and four normal bone marrow samples, respectively. After the differentially expressed genes were identified, the Cytohubba tool was used to screen for hub genes. Then, the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Using the STRING database and Cytoscape, protein-protein interaction networks and core targets were identified. Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis, respectively. RESULTS: In this study, a total of 258 differentially expressed genes with enriched functions in cancer pathways, namely cytokine receptor interactions, nuclear factor (NF)-κB signaling pathway, lipid metabolism, atherosclerosis, and Hippo signaling pathway, were identified. Ten hub genes (cd45, vcam1, ccl3, cd56, app, cd48, btk, ccr2, cybb, and cxcl12) were identified. Nine drugs, including ivermectin, deforolimus, and isoliquiritigenin, were predicted by the Connectivity Map database to have potential therapeutic effects on t (4;14) MM. In molecular docking, ivermectin showed strong binding affinity to all 10 identified targets, especially cd45 and cybb. Ivermectin inhibited t(4;14) MM cell growth via the NF-κB pathway and induced MM cell apoptosis in vitro. Furthermore, ivermectin increased reactive oxygen species accumulation and altered the mitochondrial membrane potential in t(4;14) MM cells. CONCLUSION: Collectively, the findings offer valuable molecular insights for biomarker validation and potential drug development in t(4;14) MM diagnosis and treatment, with ivermectin emerging as a potential therapeutic alternative.

Prediction of cancer recurrence based on compact graphs of whole slide images.

Cancer recurrence is one of the primary causes of patient mortality following treatment, indicating increased aggressiveness of cancer cells and difficulties in achieving a cure. A critical step to improve patients' survival is accurately predicting recurrence status and giving appropriate treatment. Whole Slide Images (WSIs) are a common type of image data in the field of digital pathology, containing high-resolution tissue information. Furthermore, WSIs of primary tumors contain microenvironmental information directly associated with the growth of tumor cells. To effectively utilize this microenvironmental information. Firstly, we represented microenvironmental features of histopathological images as compact graphs. Secondly, this work aims to develop an enhanced lightweight graph neural network called the Adaptive Graph Clustering Network (AGCNet) for predicting cancer recurrence. Experiments are conducted on three cancer datasets from The Cancer Genome Atlas (TCGA), and AGCNet achieved an accuracy of 81.81% in BLCA, 69.66% in PAAD, and 81.96% in STAD. These results indicated that AGCNet is an effective model for predicting cancer recurrence and is expected to be applied in clinical applications.

ConPep: Prediction of peptide contact maps with pre-trained biological language model and multi-view feature extracting strategy.

The accurate prediction of peptide contact maps remains a challenging task due to the difficulty in obtaining the interactive information between residues on short sequences. To address this challenge, we propose ConPep, a deep learning framework designed for predicting the contact map of peptides based on sequences only. To sufficiently incorporate the sequential semantic information between residues in peptide sequences, we use a pre-trained biological language model and transfer prior knowledge from large scale databases. Additionally, to extract and integrate sequential local information and residue-based global correlations, our model incorporates Bidirectional Gated Recurrent Unit and attention mechanisms. They can obtain multi-view features and thus enhance the accuracy and robustness of our prediction. Comparative results on independent tests demonstrate that our proposed method significantly outperforms state-of-the-art methods even with short peptides. Notably, our method exhibits superior performance at the sequence level, suggesting the robust ability of our model compared with the multiple sequence alignment (MSA) analysis-based methods. We expect it can be meaningful research for facilitating the wide use of our method.

Distinct mutational processes shape selection of MHC class I and class II mutations across primary and metastatic tumors.

Disruption of antigen presentation via loss of major histocompatibility complex (MHC) expression is a strategy whereby cancer cells escape immune surveillance and develop resistance to immunotherapy. Here, we develop the personalized genomics algorithm Hapster and accurately call somatic mutations within the MHC genes of 10,001 primary and 2,199 metastatic tumors, creating a catalog of 1,663 non-synonymous mutations that provide key insights into MHC mutagenesis. We find that MHC class I genes are among the most frequently mutated genes in both primary and metastatic tumors, while MHC class II mutations are more restricted. Recurrent deleterious mutations are found within haplotype- and cancer-type-specific hotspots associated with distinct mutational processes. Functional classification of MHC residues reveals significant positive selection for mutations disruptive to the B2M, peptide, and T cell binding interfaces, as well as to MHC chaperones.

Identification of immunity-related lncRNAs and construction of a ceRNA network of potential prognostic biomarkers in acute myeloid leukemia.

Objective: Using bioinformatics analyses, this study aimed to identify lncRNAs related to the immune status of acute myeloid leukemia (AML) patients and ascertain the potential impact in immunity-related competing endogenous RNA (ceRNA) networks on AML prognosis. Methods: AML-related RNA-seq FPKM data, AML-related miRNA expression microarray data, and gene sets associated with immunity-related pathways were, respectively, obtained from the TCGA, GEO, and ImmReg databases. An immunity-related ceRNA network was then constructed according to the predicted interactions between AML-related mRNAs, lncRNAs, and miRNAs. After performing LASSO and multivariate Cox regression analyses, lncRNAs in the ceRNA network were used to establish an AML prognostic model. According to mutual regulatory relationships and consistent trends of expression among candidate ceRNAs, two ceRNA subnetworks related to the AML prognostic model were determined. Finally, the correlation between the expression levels of mRNAs, lncRNAs, and miRNAs in each ceRNA subnetwork and immune cell infiltration (assessed by combining the ESTIMATE and CIBERSORT methods and ssGSEA) was analyzed. Results: A total of 424 immunity-related differentially expressed (IR-DE) mRNAs (IR-DEmRNAs), 191 IR-DElncRNAs, and 69 IR-DEmiRNAs were obtained, and a ceRNA network of 20 IR-DElncRNAs, 6 IR-DEmRNAs, and 3 IR-DEmiRNAs was established. Univariate Cox regression analysis was conducted on 20 IR-DElncRNAs, and 7 of these were identified to be significantly correlated with the overall survival (OS) time in AML patients. Then, two IR-DElncRNAs (MEG3 and HCP5) were screened as independent OS-related factors by LASSO and multivariable Cox regression analyses, and a prognostic model was constructed to evaluate the survival risk in AML patients. Survival analyses indicated that the OS of patients was often poor in the high-risk group. Additionally, from this model, two ceRNA regulatory pathways, namely, MEG3/miR-125a-5p/SEMA4C and HCP5/miR-125b-5p/IL6R, which were potentially involved in the immune regulation of AML prognosis were identified. Conclusion: lncRNAs HCP5 and MEG3 may act as key ceRNAs in the pathogenesis in AML by regulating immune cell representation as part of the regulatory lncRNA-miRNA-mRNA axes. The candidate mRNAs, lncRNAs, and miRNAs included in the ceRNA network identified here may serve as useful prognostic biomarkers and immunotherapeutic targets for AML.

Targeted RASSF1A expression inhibits proliferation of HER2­positive breast cancer cells in vitro.

Human epidermal growth factor receptor 2-positive (HER2+) breast cancer, which accounts for 15-20% of all breast cancer, is associated with tumor recurrence and poor prognosis. RAS association domain family protein 1 subtype A (RASSF1A) is a tumor suppressor that is silenced in a variety of human cancers. The present study aimed to investigate the role of RASSF1A in HER2+ breast cancer and the therapeutic potential of RASSF1A-based targeted gene therapy for this malignancy. RASSF1A expression in human HER2+ breast cancer tissues and cell lines was evaluated by reverse transcription PCR and western blot analysis. The associations between tumorous RASSF1A level and tumor grade, TNM stage, tumor size, lymph node metastasis and five-year survival were examined. HER2+ and HER2-negative (HER2-) breast cancer cells were transfected with a lentiviral vector (LV-5HH-RASSF1A) that could express RASSF1A under the control of five copies of the hypoxia-responsive element (5HRE) and one copy of the HER2 promoter (HER2p). Cell proliferation was evaluated by the MTT and colony formation assays. It was found that tumorous RASSF1A level was negatively associated with tumor grade (P=0.014), TNM stage (P=0.0056), tumor size (P=0.014) and lymph node metastasis (P=0.029) and positively associated with five-year survival (P=0.038) in HER2+ breast cancer patients. Lentiviral transfection of HER2+ breast cancer cells resulted in increased RASSF1A expression and decreased cell proliferation, especially under hypoxic conditions. However, lentiviral transfection of HER2-breast cancer cells did not affect RASSF1A expression. In conclusion, these findings verified the clinical significance of RASSF1A as a tumor suppressor in HER2+ breast cancer and supported LV-5HH-RASSF1A as a potential targeted gene therapy for this malignancy.

Single cell landscape of parietal epithelial cells in healthy and diseased states.

The biology and diversity of glomerular parietal epithelial cells (PECs) are important for understanding podocyte regeneration and crescent formation. Although protein markers have revealed the morphological heterogeneity of PECs, the molecular characteristics of PEC subpopulations remain largely unknown. Here, we performed a comprehensive analysis of PECs using single-cell RNA sequencing (scRNA-seq) data. Our analysis identified five distinct PEC subpopulations: PEC-A1, PEC-A2, PEC-A3, PEC-A4 and PEC-B. Among these subpopulations, PEC- A1 and PEC-A2 were characterized as podocyte progenitors while PEC-A4 represented tubular progenitors. Further dynamic signaling network analysis indicated that activation of PEC-A4 and the proliferation of PEC-A3 played pivotal roles in crescent formation. Analyses suggested that upstream signals released by podocytes, immune cells, endothelial cells and mesangial cells serve as pathogenic signals and may be promising intervention targets in crescentic glomerulonephritis. Pharmacological blockade of two such pathogenic signaling targets, proteins Mif and Csf1r, reduced hyperplasia of the PECs and crescent formation in anti-glomerular basement membrane glomerulonephritis murine models. Thus, our study demonstrates that scRNA-seq-based analysis provided valuable insights into the pathology and therapeutic strategies for crescentic glomerulonephritis.

Mechanism study of the gel-forming ability of heat-induced gel from Peruvian hake (Merluccius gayi peruanus) surimi.

The commercial value of Peruvian hake (Merluccius gayi peruanus) meat is low because of its soft texture. This study investigated the major factor contributing to the gel-forming ability of Peruvian hake surimi by comparing the effects of endogenous protease activity and parasitic infection. Heat-induced gels could not be obtained at 50 °C-90 °C. Surimi with severe parasitic infection showed a stronger gel-forming ability. The endogenous protease activities were the main factor influencing the Peruvian hake meat proteolysis and contributed to the low gel-forming ability, rather than parasitic infection. Specifically, endogenous cysteine proteases played an essential role in protein degradation and low gel-forming ability. Moreover, endogenous transglutaminase was also shown to be involved in the gel-forming ability upon heating at 40 °C. These results suggested that Peruvian hake meat could be used as a raw material of frozen surimi for fish gel by inhibiting the activity of endogenous proteases.

Preparation of a miR-155-activating nucleic acid nanoflower to study the molecular mechanism of miR-155 in inflammation.

At present, the molecular mechanisms underlying inflammation remain unclear. In recent years, research on inflammation has focused on stimulating cell inflammation by using exogenous pro-inflammatory substances such as lipopolysaccharide (LPS) or inflammatory factors. To investigate the molecular mechanism of inflammation from a new perspective, we designed a nucleic acid nanoflowers (NFs) complex to directly activate inflammatory genes to study the inflammatory response without the need for external microbial factors to trigger an inflammatory response. An RNAa-type target gene-activated NFs was designed. Human umbilical vein endothelial cells (HUVECs) were transfected with NFs carrying small activating RNA (saRNAs) to directly co-activate microRNA (miR)-155 and SHIP1 genes. After RNA activation (RNAa)-type NFs were transferred into HUVECs, the expression of miR-155 and pro-inflammatory and cancer-related factors increased, anti-inflammatory factors were reduced, cell proliferation increased, and cell migration was promoted. IL-1ß protein levels were decreased and SHIP1 expression was downregulated. When miR-155 and its target SHIP1 were both activated, the expression of both was unaltered, maintaining cell homeostasis. This points towards miR-155 overexpression can trigger inflammation, and that miR-155 and its target genes act as a molecular switch role in the development of inflammation.

Corrigendum: Comprehensive Analysis of N6-Methylandenosine-Related Long Non-Coding RNAs Signature in Prognosis and Tumor Microenvironment of Bladder Cancer.

[This corrects the article DOI: 10.3389/fonc.2022.774307.].

Comprehensive Analysis of N6-Methylandenosine-Related Long Non-Coding RNAs Signature in Prognosis and Tumor Microenvironment of Bladder Cancer.

To investigate the role of N6-methyladenosine (m6A)- related long non-coding RNAs (lncRNAs) in bladder cancer (BC). 50 m6A-related lncRNAs were screened out and were correlated with prognosis from BC samples in The Cancer Genome Atlas (TCGA). The lncRNAs were subdivided into cluster 1 and cluster 2 with consensus cluster analysis, and it was found that lncRNAs in cluster 2 were associated with poor prognosis and increased PD-L1 expression. Gene set enrichment analysis (GSEA) revealed tumor-related pathways in cluster 2. Through least absolute shrinkage and selection operator (LASSO) Cox regression analysis, univariate and multivariate Cox regression, and ROC analyses, 14 prognostic lncRNAs were selected and used to construct the m6A-related lncRNA prognostic signature (m6A-LPS), furthermore, that m6A-LPS was as a valuable independent prognostic factor. Interestingly, the m6A-LPS risk score was positively correlated with the immune score, PD-L1 expression, and the infiltration of immune cell subtypes in BC. SNHG16, a member of the high-risk group based on m6A-LPS, was highly expressed in BC tissues and cell lines and interfered with siRNA resulted in suppressed proliferation, migration, and invasion in vitro. Our study illustrates the role of m6A-related lncRNAs in BC. The m6A-LPS may be an important regulatory target of the tumor microenvironment (TME) in BC.

Renal denervation ameliorates cardiac metabolic remodeling in diabetic cardiomyopathy rats by suppressing renal SGLT2 expression.

This study aimed to investigate the effects of renal denervation (RDN) on diabetic cardiomyopathy (DCM) and explore the related mechanisms. Male Sprague-Dawley rats were fed high-fat chow and injected with low-dose streptozotocin to establish a DCM model. Six rats served as controls. The surviving rats were divided into three groups: control group, DCM group and DCM + RDN group. RDN surgery was performed in the fifth week. At the end of the experiment, all rats were subjected to 18F-FDG PET/CT and metabolic cage studies. Cardiac function and structure were evaluated by echocardiography and histology. Myocardial substrate metabolism and mitochondrial function were assessed by multiple methods. In the 13th week, the DCM rats exhibited cardiac hypertrophy and interstitial fibrosis accompanied by diastolic dysfunction. RDN ameliorated DCM-induced cardiac dysfunction (E/A ratio: RDN 1.07 ± 0.18 vs. DCM 0.93 ± 0.12, P < 0.05; E/E' ratio: RDN 10.74 ± 2.48 vs. DCM 13.25 ± 1.99, P < 0.05) and pathological remodeling (collagen volume fraction: RDN 5.05 ± 2.05% vs. DCM 10.62 ± 2.68%, P < 0.05). Abnormal myocardial metabolism in DCM rats was characterized by suppressed glucose metabolism and elevated lipid metabolism. RDN increased myocardial glucose uptake and oxidation while reducing the absorption and utilization of fatty acids. Meanwhile, DCM decreased mitochondrial ATP content, depolarized the membrane potential and inhibited the activity of respiratory chain complexes, but RDN attenuated this mitochondrial damage (ATP: RDN 30.98 ± 7.33 µmol/gprot vs. DCM 22.89 ± 5.90 µmol/gprot, P < 0.05; complexes I, III and IV activity: RDN vs. DCM, P < 0.05). Furthermore, both SGLT2 inhibitor and the combination treatment produced similar effects as RDN alone. Thus, RDN prevented DCM-induced cardiac dysfunction and pathological remodeling, which is related to the improvement of metabolic disorders and mitochondrial dysfunction.

Case report of retroperitoneal ectopic pancreas with adrenal adenoma.

Background: Ectopic pancreas is a congenital anomaly in which pancreatic tissue is anatomically separated from the main gland and without vascular or ductal continuity. A case of retroperitoneal ectopic pancreas with adrenal adenoma has never yet been reported. Case Presentation: A 54-year-old man presented three masses in the left retroperitoneum, and two of them were resected. The pathologic findings were a retroperitoneal ectopic pancreas with adrenal adenoma. Conclusion: We report an extremely rare case of a retroperitoneal ectopic pancreas and its characterization with dynamic gadolinium-enhanced magnetic resonance imaging (MRI).

Identification of risk factors for postoperative delirium in elderly patients with hip fractures by a risk stratification index model: A retrospective study.

INTRODUCTION: Postoperative delirium is one of the most common and dangerous psychiatric complications after hip surgery. The aim of this study was to investigate the incidence of postoperative delirium in elderly patients after hip fracture surgery and to identify risk factors for such, as part of developing a risk stratification index (RSI) system to predict a patient's risk of postoperative delirium. METHODS: Elderly patients (aged 65 years or older) with hip fractures who had received surgical treatment in our hospital between March 2018 and December 2019 were retrospectively included. Clinical data were collected, and multivariate logistic regression analysis was performed to investigate the relevant risk factors of postoperative delirium. An RSI system was developed based on factors identified in the regression analysis. RESULTS: Of 272 patients included, 52 (19.12%) experienced postoperative delirium. Drinking history (> 3/ week), the perioperative lactic acid level (Lac > 2 mmol/L), postoperative visual analog score (VAS) > 3, American Society of Anesthesiologists (ASA) physical status > II, application of the bispectral index, and preoperative diabetes were independent risk factors of postoperative delirium. When RSI ≥ 5, the rate of postoperative delirium significantly increased (p < .05). CONCLUSION: The RSI system developed here can safely guide postoperative outcomes of elderly patients with hip fractures, and RSI ≥ 5 may be able to predict the onset of postoperative delirium.

MicroRNA-22 inhibition promotes the development of atherosclerosis via targeting interferon regulator factor 5.

Atherosclerosis is generally accepted as a chronic inflammatory disease and is the most important pathological process underlying the cardiovascular diseases. MiR-22 exerts an important role in tumorgenesis, obesity and NAFLD development, as well as cardiovascular diseases. However, a certain role of miR-22 in the pathogenesis of atherosclerosis remains undetermined. Here, we showed that miR-22 exhibited a negative association with the deteriorated atherosclerotic plaque and showed significant downregulated expression in macrophages. Next, treatment of ApoE deficiency (ApoE-/-) mice with miR-22 inhibitors which were then subjected to high fat diet (HFD) for 12 weeks were performed to investigate the function of miR-22 on atherogenesis. The results exhibited that miR-22 inhibition dramatically promoted atherosclerotic plaques but attenuated plaque stabilization which were accompanied by decreased smooth muscle cell and collagen content, but increased macrophage infiltration and lipid accumulation. More importantly, the in vivo and in vitro experiments suggested that miR-22 inhibition accelerated inflammatory response and foam cell formation. Mechanistically, we demonstrated interferon regulator factor 5 (IRF5) was an important target of miR-22 and it was required for the regulation of inflammation mediated by miR-22 inhibition. Collectively, these evidences revealed that miR-22 inhibition promoted the atherosclerosis progression through activation of IRF5.

Clinical applications of mesenchymal stromal cell-based therapies for pulmonary diseases: An Update and Concise Review.

Lung disorders are a leading cause of morbidity and death worldwide. For many disease conditions, no effective and curative treatment options are available. Mesenchymal stromal cell (MSC)-based therapy is one of the cutting-edge topics in medical research today. It offers a novel and promising therapeutic option for various acute and chronic lung diseases due to its potent and broad-ranging immunomodulatory activities, bacterial clearance, tissue regeneration, and proangiogenic and antifibrotic properties, which rely on both cell-to-cell contact and paracrine mechanisms. This review covers the sources and therapeutic potential of MSCs. In particular, a total of 110 MSC-based clinical applications, either completed clinical trials with safety and early efficacy results reported or ongoing worldwide clinical trials of pulmonary diseases, are systematically summarized following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, including acute/viral pulmonary disease, community-acquired pneumonia (CAP), chronic obstructive pulmonary disease (COPD), bronchopulmonary dysplasia (BPD), interstitial lung diseases (ILD), chronic pulmonary fibrosis, bronchiolitis obliterans syndrome (BOS) and lung cancer. The results of recent clinical studies suggest that MSCs are a promising therapeutic approach for the treatment of lung diseases. Nevertheless, large-scale clinical trials and evaluation of long-term effects are necessary in further studies.

Mechanism of mutant calreticulin-mediated activation of the thrombopoietin receptor in cancers.

Myeloproliferative neoplasms (MPNs) are frequently driven by mutations within the C-terminal domain (C-domain) of calreticulin (CRT). CRTDel52 and CRTIns5 are recurrent mutations. Oncogenic transformation requires both mutated CRT and the thrombopoietin receptor (Mpl), but the molecular mechanism of CRT-mediated constitutive activation of Mpl is unknown. We show that the acquired C-domain of CRTDel52 mediates both Mpl binding and disulfide-linked CRTDel52 dimerization. Cysteine mutations within the novel C-domain (C400A and C404A) and the conserved N-terminal domain (N-domain; C163A) of CRTDel52 are required to reduce disulfide-mediated dimers and multimers of CRTDel52. Based on these data and published structures of CRT oligomers, we identify an N-domain dimerization interface relevant to both WT CRT and CRTDel52. Elimination of disulfide bonds and ionic interactions at both N-domain and C-domain dimerization interfaces is required to abrogate the ability of CRTDel52 to mediate cell proliferation via Mpl. Thus, MPNs exploit a natural dimerization interface of CRT combined with C-domain gain of function to achieve cell transformation.