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

Abstract

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.

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

“…Proteomics quantification experiments enable monitoring of the relative abundances of thousands of proteins in biological samples. Most studies use parallel-group designs, where one or many treatment groups are compared to the control group. , More recently, more complex experimental designs with an increasing number of samples have been studied, e.g., factorial designs and longitudinal studies (time series), sometimes with repeated measurements on the same subject. , The data can be modeled using linear fixed-, mixed-, or random-effects models . Based on these models, tests can be applied to examine whether specific factors and factor interactions are significant; e.g., it can be tested if differences in protein abundance between groups are statistically significant.…”

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

“…Most studies use parallel-group designs, where one or many treatment groups are compared to the control group (Leeuw et al 2022; Laubscher et al 2021). More recently, more complex experimental designs with an increasing number of samples are studied, e.g., factorial designs and longitudinal studies (time series), sometimes with repeated measurements on the same subject (Tan et al 2022; Meier-Abt et al 2021). The data can be modeled using linear fixed-, mixed-, or random-effects models (Bates et al 2015).…”