Reduced CXCL4/PF4 expression as a driver of increased human hematopoietic stem and progenitor cell proliferation in polycythemia vera

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning

Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor. Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML. Furthermore, using different ML algorithms, prognostic models were constructed and validated using the DHRGs. At the end molecular docking studies were performed to identify potential drug candidates targeting the selected DHRGs. Results: We identified a total of 80 DHRGs by comparing the differentially expressed genes derived between AML patients and normal controls and high-risk AML genes identified by Cox regression. Genetic and epigenetic alteration analyses of the DHRGs revealed a significant association of their copy number variations and methylation status with overall survival (OS) of AML patients. Out of the 137 models constructed using different ML algorithms, the combination of Ridge and plsRcox maintained the highest mean C-index and was used to build the final model. When AML patients were classified into low- and high-risk groups based on DHRGs, the low-risk group had significantly longer OS in the AML training and validation cohorts. Furthermore, immune infiltration, miRNA coexpression, stemness feature and hallmark pathway analyses revealed significant differences in the prognosis of the low- and high-risk AML groups. Drug sensitivity and molecular docking studies revealed top 5 drugs, including carboplatin and austocystin-D that may significantly affect the DHRGs in AML. Conclusion: The findings from the current study identified a set of high-risk genes that may be used as prognostic and therapeutic markers for AML patients. In addition, significant use of the ML algorithms in constructing and validating the prognostic models in AML was demonstrated. Although our study used extensive bioinformatics and machine learning methods to identify the hub genes in AML, their experimental validations using knock-out/-in methods would strengthen our findings.

Single-cell RNA sequencing-guided fate-mapping toolkit delineates the contribution of yolk sac erythro-myeloid progenitors

Erythro-myeloid progenitors of the yolk sac that originates during early embryo development has been suggested to generate tissue-resident macrophage, mast cell, and even endothelial cell populations from fetal to adult stages. However, the heterogeneity of erythro-myeloid progenitors (EMPs) is not well characterized. Here, we adapt single-cell RNA sequencing to dissect the heterogeneity of EMPs and establish several fate-mapping tools for each EMP subset to trace the contributions of different EMP subsets. We identify two primitive and one definitive EMP subsets from the yolk sac. In addition, we find that primitive EMPs are decoupled from definitive EMPs. Furthermore, we confirm that primitive and definitive EMPs give rise to microglia and other tissue-resident macrophages, respectively. In contrast, only Kit+ Csf1r- primitive EMPs generate endothelial cells transiently during early embryo development. Overall, our results delineate the contribution of yolk sac EMPs more clearly based on the single-cell RNA sequencing (scRNA-seq)-guided fate-mapping toolkit.

prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.

Proteomic identification of proliferation and progression markers in human polycythemia vera stem and progenitor cells

Polycythemia vera (PV) is a stem cell disorder characterized by hyperproliferation of the myeloid lineages and the presence of an activating JAK2 mutation. To elucidate mechanisms controlling PV stem and progenitor cell biology, we applied a recently developed highly sensitive data-independent acquisition mass spectrometry workflow to purified hematopoietic stem and progenitor cell (HSPC) subpopulations of patients with chronic and progressed PV. We integrated proteomic data with genomic, transcriptomic, flow cytometry and in vitro colony formation data. Comparative analyses revealed added information gained by proteomic compared with transcriptomic data in 30% of proteins with changed expression in PV patients. Upregulated biological pathways in hematopoietic stem and multipotent progenitor cells (HSC/MPPs) of PV included MTOR, STAT and interferon signaling. We further identified a prominent reduction of clusterin (CLU) protein expression and a corresponding activation of NFĸB signaling in HSC/MPPs of untreated PV patients compared with controls. Reversing the reduction of CLU and inhibiting NFĸB signaling decreased proliferation and differentiation of PV HSC/MPPs in vitro. Upon progression of PV, we identified upregulation of LGALS9 and SOCS2 protein expression in HSC/MPPs. Treatment of patients with hydroxyurea normalized the expression of CLU and NFĸB2, but not of LGALS9 and SOCS2. These findings expand the current understanding of the molecular pathophysiology underlying PV and provide new potential targets (CLU and NFĸB) for antiproliferative therapy in PV patients.

CRISPR/Cas9 mediated CXCL4 knockout in human iPS cells of polycythemia vera patient with JAK2 V617F mutation

The chemokine CXCL4/platelet factor 4 (PF4) gene, a key player in myelofibrosis, was knocked out by CRISPR/Cas9 in induced pluripotent stem cells (iPS cells) of a polycythemia vera (PV) patient with JAK2 V617F mutation. Two CXCL4^KO iPS cell lines with and without JAK2 V617F mutation (UKAi002-B-1 and UKAi002-A-1, respectively) were generated. CXCL4^KO iPS cells showed deletion of exon 1 and complete loss of CXCL4 protein. Pluripotency of iPS cells was confirmed by expression of pluripotency markers and trilineage differentiation. CXCL4^KO iPS cells are expected to provide a valuable tool for investigating the role of CXCL4 in human diseases.

Differential expression of hydroxyurea transporters in normal and polycythemia vera hematopoietic stem and progenitor cell subpopulations

Polycythemia vera (PV) is a myeloproliferative neoplasm marked by hyperproliferation of the myeloid lineages and the presence of an activating JAK2 mutation. Hydroxyurea (HU) is a standard treatment for high-risk patients with PV. Because disease-driving mechanisms are thought to arise in PV stem cells, effective treatments should target primarily the stem cell compartment. We tested for the antiproliferative effect of patient treatment with HU in fluorescence-activated cell sorting-isolated hematopoietic stem/multipotent progenitor cells (HSC/MPPs) and more committed erythroid progenitors (common myeloid/megakaryocyte-erythrocyte progenitors [CMP/MEPs]) in PV using RNA-sequencing and gene set enrichment analysis. HU treatment led to significant downregulation of gene sets associated with cell proliferation in PV HSCs/MPPs, but not in PV CMP/MEPs. To explore the mechanism underlying this finding, we assessed for expression of solute carrier membrane transporters, which mediate transmembrane movement of drugs such as HU into target cells. The active HU uptake transporter OCTN1 was upregulated in HSC/MPPs compared with CMP/MEPs of untreated patients with PV, and the HU diffusion facilitator urea transporter B (UTB) was downregulated in HSC/MPPs compared with CMP/MEPs in all patient and control groups tested. These findings indicate a higher accumulation of HU within PV HSC/MPPs compared with PV CMP/MEPs and provide an explanation for the differential effects of HU in HSC/MPPs and CMP/MEPs of patients with PV. In general, the findings highlight the importance of transporter expression in linking therapeutics with human disease.