Inappropriate localization of proteins can interfere with normal cellular function and drive tumor development. To understand how this contributes to the development of acute myeloid leukemia (AML), we compared the nuclear proteome and transcriptome of AML blasts with normal human CD34 + cells. Analysis of the proteome identified networks and processes that significantly affected transcription regulation including misexpression of 11 transcription factors with seven proteins not previously implicated in AML. Transcriptome analysis identified changes in 40 transcription factors but none of these were predictive of changes at the protein level. The highest differentially expressed protein in AML nuclei compared with normal CD34 + nuclei (not previously implicated in AML) was S100A4. In an extended cohort, we found that overexpression of nuclear S100A4 was highly prevalent in AML (83%; 20/24 AML patients). Knock down of S100A4 in AML cell lines strongly impacted their survival whilst normal hemopoietic stem progenitor cells were unaffected. These data are the first analysis of the nuclear proteome in AML and have identified changes in transcription factor expression or regulation of transcription that would not have been seen at the mRNA level. These data also suggest that S100A4 is essential for AML survival and could be a therapeutic target in AML.
Nuclear factor I-C (NFIC) belongs to a family of NFI transcription factors that binds to DNA through CAATT-boxes and are involved in cellular differentiation and stem cell maintenance. Here we show NFIC protein is significantly overexpressed in 69% of acute myeloid leukemia patients. Examination of the functional consequences of NFIC overexpression in HSPCs showed that this protein promoted monocytic differentiation. Single-cell RNA sequencing analysis further demonstrated that NFIC overexpressing monocytes had increased expression of growth and survival genes. In contrast, depletion of NFIC through shRNA decreased cell growth, increased cell cycle arrest and apoptosis in AML cell lines and AML patient blasts. Further, in AML cell lines (THP-1), bulk RNA sequencing of NFIC knockdown led to downregulation of genes involved in cell survival and oncogenic signaling pathways including mixed lineage leukemia-1 (MLL-1). Lastly, we show that NFIC knockdown in an ex vivo mouse MLL::AF9 pre-leukemic stem cell model, decreased their growth and colony formation and increased expression of myeloid differentiation markers Gr1 and Mac1. Collectively, our results suggest that NFIC is an important transcription factor in myeloid differentiation as well as AML cell survival and is a potential therapeutic target in AML.
Background:Nuclear mislocalization of proteins can interfere with normal cellular function and cooperatively drive tumor development. To understand how this process mediates AML development, we analyzed the nuclear proteome of AML blasts in comparison with normal human CD34+ cells to identify misregulated nuclear proteins. In a preliminary study, we identified S100A4 to be over‐expressed in 73% (11/15) AML patients and mislocalized to the nucleus in AML blasts. S100A4 belongs to the S100 multigene family of calcium‐binding proteins of the EF‐hand type and has been implicated in tumor progression and metastasis in many solid tumours but little is known of its role in hematological malignancy.Aims:To validate S100A4 overexpression and mislocalization in primary AML blasts. To determine the functional role of S100A4 on hematopoietic cell development, growth and survival in normal human CD34+ cells and AML cell lines.Methods:S100A4 expression was determined by western blot. Lentiviral vectors were used to ectopically express S100A4 and shRNA was used to knock down S100A4 expression. Effects on cell growth, survival and differentiation were determined by flow cytometry. To determine S100A4 interacting proteins, S100A4 was co‐immunoprecipitated with its binding partners using Ca+2 enriched conditions from nuclear extracts of ME‐1 cells followed by LC‐MS/MS analysis.Results:Using western blotting, S100A4 protein expression was observed in the nucleus of AML blasts (73%; 24/33) whilst normal CD34+ or CD14+ differentiated monocytic controls have shown only cytosolic expression of S100A4. Upregulated S100A4 is also supported by transcriptome analysis indicating that overexpression arises at least partly at a transcriptional level. An independent dataset (TCGA) supports the overexpression of S100A4 mRNA in AML and suggests that overexpression may confer a poor prognosis (p = 0.0118). To determine whether ectopic expression of nuclear S100A4 can affect the growth and survival of CD34+ cells, we attempted to overexpress nuclear‐targeted S100A4 in normal human hematopoietic cells. Overexpression of nuclear S100A4 could not be demonstrated in transduced CD34+ or in normal differentiated cells (probably due to rapid degradation of ectopically expressed S100A4 in these cells). To examine the importance of S100A4 expression in normal and leukaemic cells we next knocked‐down S100A4 expression. In CD34+ cells we observed no significant effect on the growth or lineage development of these cells suggesting S100A4 is not required for normal hematopoiesis. We next examined the consequences of knocking down S100A4 expression in AML cell lines (NOMO‐1, TF‐1, THP‐1, and OCI‐AML2). S100A4 knockdown severely impaired the growth TF‐1 and OCI‐AML2 (p < 0.05). Significant apoptosis was observed in OCI‐AML‐2, TF‐1, and NOMO‐1 (p < 0.05). Using co‐immunoprecipitation coupled with LC/MS to identify binding partners of S100A4, we identified heterogeneous nuclear Ribonucleoprotein M (hnRNPM) as novel binding partner of S100A4 AML. Interestingly, hnRNPM has been implicated in metastasis in breast cancer through mediating alternative splicing.Summary/Conclusion:We found that S100A4 is mislocalized to the nucleus in AML blasts compared to normal hematopoeitic cells and is essential for AML cells growth and survival. HnRNPM emerged as novel binding partner to S100A4 and we hypothesise that S100A4 mediates its effect through forming a splicing complex with known leukemic splicing factors such as SF3B1 and SRSF2 (also been pulled down with S100A4 co‐IP).
BackgroundCardiovascular diseases represent a major contributor to the increased mortality. Risk factors varies according to the subtypes of cardiovascular events and perhaps racial background. Little is known about the risk of major cardiovascular events (MACE) in Saudi lupus Population.ObjectivesThis study aim to examine the prevalence of MACE among Saudi SLE compared to general population and factors associated with such outcome.MethodsThis a cross-sectional study nested within two prospective cohorts to assess the period prevalence of any MACE among SLE patients who were enrolled in national prospective cohort of SLE, Saudi Arabia since its initiation in 2020. As a comparison, Prospective Urban Rural Epidemiology Study Saudi sub-cohort (PURE-Saudi) for participants who have been enrolled during the same follow up period. Participants in both studies were followed using standardized published protocol. MACE was defined as the diagnosis of Myocardial infraction, stroke or Angina. We adjust for demographics, traditional cardiovascular risk factors and the diagnosis of SLE using logistic regression models.ResultsA total of 488 with SLE and 746 from PURE were included. SLE patients were younger (40.7±12.5 compared to 49.5±8.6) and female predominant (90.6% compared to.41.6%). Prevalence of traditional risk factors were less in SLE patients including dyslipidemia (28.9% compared to 49.4%), obesity (63% compared to 85%) diabetes (7.8% compared to 27.2%) but not HTN (19.3 compared to 18.8%). Odds of MACE were significantly related to Age and Lupus diagnosis (OR: 1.08, 95% CI: 1.04-1.11, p=0.00) and (OR: 7.64, 95% CI: 2.65-22.07, p=0.00) but not CVS risk factors (OR: 0.8, 95% CI: 0.10-6.39, p=0.83).ConclusionSLE patients at significant risk of MACE compared to general population. This risk is not well explained by traditional risk factors which may explain the failure of CVS risk scores to adequately stratify SLE patients. Further work is needed to understand the pathogenesis of CVS risk in SLE and subsequently mitigate it.References[1]Yurkovich M, Vostretsova K, Chen W, Avina-Zubieta JA. Overall and cause-specific mortality in patients with systemic lupus erythematosus: a meta-analysis of observational studies. Arthritis care & research. 2014;66(4):608-616.[2]Rees F, Doherty M, Grainge MJ, Lanyon P, Davenport G, Zhang W. Mortality in systemic lupus erythematosus in the United Kingdom 1999–2012. Rheumatology. 2016;55(5):854-860.[3]Falasinnu T, Chaichian Y, Li J, et al. Does SLE widen or narrow race/ethnic disparities in the risk of five co-morbid conditions? Evidence from a community-based outpatient care system. Lupus. 2019;28(14):1619-1627.Acknowledgements:NIL.Disclosure of InterestsNone Declared.
BackgroundThe utilization of telemedicine has been rapidly growing among patients with rheumatic disease especially following COVID-19 pandemic. The ease and convenience seems to dominate the reasons of such growth. Yet the effect of this approach on patients with systemic lupus erythematosus (SLE) is yet to be revealed.ObjectivesIn this study, we aimed to examine the effect of telemedicine in SLE patients on the outcome of disease activity.MethodsThis is a case-cross over study nested within the national prospective cohort of SLE, Saudi Arabia. SLE patients who fulfils the SLICC classification criteria within the period between March 2020 to March 2021 and have been assessed at three-time point with three months’ time difference between assessments according to standardized protocol were included. Telemedicine was conducted at first point while in person assessment were used for second and third points. Primary outcome was difference in SLEDAI-2K score. Primary analysis was conducted using GEE model and adjusted for potential confounders including demographics, medications and changes in steroid doses. Several sensitivity analyses were conducted to mitigate selection and time varying confounders.ResultsA total of 92 participants were included. Majority of them were females (88%), with a mean (±SD) age of 36 (±13). mean (±SD) disease activity scores at baseline as follows: SLEDI 5 (±5); SRI 3.8 (±3.5); SDI 1 (±1). Mean difference of SLEDI score of -1.641 (95% CI -2.773 ─ -0.510), p=0.005* between telemedicine and follow up visits. Adjusted value and mean with mean diffrence in Figure 1. Results were consistent in all sensitivity analyses.ConclusionWe found that the Telemedicine assessment was associated with much higher disease activity score compared to in person in subsequent assessments which may suggest potentially overestimation of disease activity and later assessment accuracy. Cautious adoption is suggested in SLE patients with active disease.References[1]Anderson JT, Bouchacourt LM, Sussman KL, Bright LF, Wilcox GB. Telehealth adoption during the COVID-19 pandemic: A social media textual and network analysis. Digit Health. 2022 Mar 31;8:20552076221090041. doi: 10.1177/20552076221090041. PMID: 35392254; PMCID: PMC8979849.[2]Tang, W., Inzerillo, S., Weiner, J., Khalili, L., Barasch, J., Gartshteyn, Y., Dall’Era, M., Aranow, C., Mackay, M.C., & Askanase, A.D. (2022). The Impact of Telemedicine on Rheumatology Care. Frontiers in Medicine, 9.Figure 1Repeated measure analysis of difference in SLEDAI between the virtual visit and the physical visit taking factors affecting SLEDAI on univariate analysis into consideration (abnormal urine test, fibromyalgia, haemolytic anaemia and mycophenolate use).Acknowledgements:NIL.Disclosure of InterestsNone Declared.
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