658 BLOOD, 24 JULY 2014 x VOLUME 124, NUMBER 4 CORRESPONDENCE expression and miR-processing enzymes are described for several types of cancer. Dicer1, as one of the main components of the miR processing ensemble, has been implicated in the context of cancer initiation and evolution, especially in myeloma cells.4 The induction of miR-128 by mutant (Mut) p53 has also been observed in tumor cells, suggesting its role in the generation of chemoresistance.5 Here, the human myeloma cancer cell lines p53-Mut (L363) and p53 wild-type (p53-WT) (MMS1) were used as a model system to investigate the effects of bortezomib on MM cell growth. As shown in Figure 1A, we detected a dose-dependent decrease in the growth of both WT and p53-Mut myeloma cell lines with 50% inhibitory concentration values of 11.6 nM (against L363) and 7.4 nM (against MMS1) 24 hours after exposure to bortezomib. Having obtained this information, we next asked whether bortezomib modulates Dicer1 expression levels. As depicted in Figure 1B, we found bortezomib to decrease Dicer1 protein levels in MMS1 cells. In contrast, no change in Dicer1 expression levels was observed in p53-Mut L363 cells. In MM cells, the cell cycle inhibitor p21WAF1 is reduced by increased proteasome activity, which fosters progression through the cell cycle. In addition, miR-106a, miR-106b, miR-17-5p, and miR-20b have been shown to critically participate in the regulation of cell cycle progression by downregulating p21WAF1.6-8 We next evaluated the p21 expression pattern that is associated with Dicer1 in response to bortezomib. Dicer1 reduction was found to inversely correlate with p21 protein accumulation in p53-WT MMS1 cells (Figure 1B). Notably, p53-Mut L363 cells failed to accumulate p21WAF1 protein. p21WAF1 stabilization was detected only with bortezomib at higher concentrations in a p53-independent manner in the p53-Mut cell line. Consistent with these findings, bortezomib effectively inhibited cell growth and induced cell death in MMS1 cells with p53-WT but was less effective in inhibiting cell growth and inducing cell death for L363 cells with p53-Mut (Figure 1C). This indicates that p53 status– associated Dicer1 expression is an essential parameter for predicting bortezomib sensitivity. The data presented here illustrate that cells with p53-WT show a low level of Dicer1 and are sensitive to bortezomib, and myeloma cells with p53-Mut show a constant high level of Dicer1 and are less sensitive to bortezomib-mediated apoptosis induction. Suzuki et al9 reported a role for p53 in promoting the maturation of miRs via interaction with the miR processing complex, DDR8Drosha. Both p53-WT and p53-Mut have prominent but opposing roles at various levels in the process of miR maturation. Impaired processing of miR was associated with MM disease progression. The findings of Zhou et al8 clearly suggest that depletion of AGO2 or Dicer1, 2 key regulators for miR maturation and functionality, significantly decreased viability of myeloma cells. Knockdown of Dicer1 by siRNA increased sensitivity to bortezomib in p53-WT MMS1 cells. When Dicer1 expression was antagonized in the p53Mut L363 cell line, a greater cytotoxic response was seen in treatment with bortezomib (Figure 1B-C). In conclusion, this study identified that Dicer1 knockdown can be exploited to sensitize myeloma for chemotherapy, even in p53-Mut cells. Gernot Stuhler Department of Internal Medicine II, Julius-Maximilian’s University, Wuerzburg, Germany Tatyana S. Nekova Department of Internal Medicine II, Julius-Maximilian’s University, Wuerzburg, Germany Acknowledgments: This work was supported by the Deutsche Jose Carreras Leukamie ¨ Stiftung (DJCLS R07/10) (G.S.). Contribution: T.S.N. performed the laboratory work for this study and wrote the paper; G.S. made a contribution to conception and design. Conflict-of-interest disclosure: The authors declare no competing financial interests. Correspondence: Tatyana S. Nekova, Department of Internal Medicine II, Julius-Maximilian’s University of Wuerzburg, Josef-Schneider Strasse 2, 97080 Wuerzburg, Germany; e-mail: [email protected]. References 1. McConkey DJ, Zhu K. Mechanisms of proteasome inhibitor action and resistance in cancer. Drug Resist Updat. 2008;11(4-5):164-179. 2. Ling X, Calinski D, Chanan-Khan AA, Zhou M, Li F. Cancer cell sensitivity to bortezomib is associated with survivin expression and p53 status but not cancer cell types. J Exp Clin Cancer Res. 2010;29(1):8. 3. Mendell JT, Olson EN. MicroRNAs in stress signaling and human disease. Cell. 2012;148(6):1172-1187. 4. Benetatos L, Vartholomatos G. Deregulated microRNAs in multiple myeloma. Cancer. 2012;118(4):878-887. 5. Donzelli S, Fontemaggi G, Fazi F, et al. MicroRNA-128-2 targets the transcriptional repressor E2F5 enhancing mutant p53 gain of function. Cell Death Differ. 2012;19(6):1038-1048. 6. Ivanovska I, Ball AS, Diaz RL, et al. MicroRNAs in the miR-106b family regulate p21/CDKN1A and promote cell cycle progression. Mol Cell Biol. 2008;28(7): 2167-2174. 7. Gibcus JH, Kroesen B-J, Koster R, et al. MiR-17/106b seed family regulates p21 in Hodgkin’s lymphoma. J Pathol. 2011;225(4):609-617. 8. Zhou Y, Chen L, Barlogie B, et al. High-risk myeloma is associated with global elevation of miRNAs and overexpression of EIF2C2/AGO2. Proc Natl Acad Sci USA. 2010;107(17):7904-7909. 9. Suzuki HI, Yamagata K, Sugimoto K, Iwamoto T, Kato S, Miyazono K. Modulation of microRNA processing by p53. Nature. 2009;460(7254):529-533. © 2014 by The American Society of Hematology To the editor: Imaging flow cytometry documents incomplete resistance of human sickle F-cells to ex vivo hypoxia-induced sickling We have read with great interest the “Perspectives” article by Steinberg et al in the January 23, 2014, edition of Blood.1 The authors present a mathematical model that implies that the critical content of fetal hemoglobin (HbF) required to prevent polymerization of sickle hemoglobin is higher than the threshold HbF content that renders an erythrocyte detectable as an “F-cell” on immunofluorescent staining by flow cytometry. This would lead to a hypothesis that only a subset of F-cells are resistant to hypoxia-induced sickling. We obtained data from a new imaging flow cytometry assay that can qualitatively support this general principle, although with clear assay limitations that don’t permit us to address quantitatively the predictions of Steinberg et al in detail. These new results show that intracellular HbF is a major factor influencing sickling, but it is clearly not the only factor. We investigated the influence of HbF content on sickling at the individual cell level through our recently developed Sickle Imaging Flow Cytometry Assay (SIFCA), a robust, reproducible sickling assay.2 We developed an enhanced version of SIFCA that allows simultaneous analysis of both intracellular expression of HbF and morphological BLOOD, 24 JULY 2014 x VOLUME 124, NUMBER 4 CORRESPONDENCE 659 Figure 1. Imaging flow cytometry documents incomplete resistance of human sickle F-cells to ex vivo hypoxiainduced sickling. (A) HbF in the hemolysate correlates positively with the percentage of F-cells detected by SIFCA. HbF protective effect against sickling is shown by a positive correlation with the percent of cells that remain in normal shape after deoxygenation (B), and an opposite correlation with the percent of cells that become sickled (C). Sample images acquired by the imaging flow cytometer show simultaneous brightfield images (left column) and fluorescence of an anti-HbF-PE antibody (right column) in a normal non-F-cell (Di), sickled non-F-cell (Dii), normal F-cell (Diii), and a sickled F-cell (Div). (E) Transmission electron microscopy of RBCs enriched by fluorescence-activated cell sorting was performed to confirm that sickled cells contained similar fibers corresponding to hemoglobin S polymers regardless of whether they were non-F-cells (top) or F-cells (bottom). Bar 5 100 nm. Images acquired with a JEM1400 electron microscope (JEOL) equipped with an AMT XR-111 digital camera (Advanced Microscopy Techniques Corporation). Image Adjust-Levels and Image Adjust Brightness functions in Photoshop Creative Suite 6 software (Adobe Systems Corporation) were used to equalize the densities in both images. (F) Non-F-cells sickled more than F-cells in both patients off hydroxyurea (left side, *P 5 .0014, paired Student t test) and on HU (right side, *P 5 .0057, paired Student t test), but cells from treated patients sickle less than from untreated patients (Mann-Whitney test, #P 5 .0197 for F-cells from patients off HU compared with on HU, P 5 .0501 for comparison between non-F-cells). features of each red blood cell (RBC). Peripheral venous blood samples were collected with written consent from 22 adult sickle cell anemia (SCA) patients (8 off and 14 on hydroxyurea, median HbF 10.7% and 18.3%, respectively) with a wide HbF range (1.5% to 33.0%). We subjected 1% RBC suspensions to deoxygenation for 2 hours at 2% oxygen followed by fluorescent labeling against HbF. Images from 20 000 cells were obtained by imaging flow cytometry (ImageStreamX Mk II; Amnis Corporation), allowing combined analysis of shape change and HbF expression for each RBC. We confirmed previous observations3 using conventional flow cytometry that F-cell count significantly correlates with percent HbF determined by HPLC (Figure 1A). F-cell count by SIFCA correlated highly with conventional F-cell flow cytometry by an independent Clinical Laboratory Improvement Amendments–certified facility (r2 5 0.9976, 95% CI 0.9861-0.9996, P , .0001, data not shown). SIFCA yields automated determination of the percentage of “normal cells,” which remain biconcave discs, and of “abnormal cells,” which change their shape on hypoxic incubation, including 660 BLOOD, 24 JULY 2014 x VOLUME 124, NUMBER 4 CORRESPONDENCE the majority of sickled cells. As expected, the HbF percentage correlated positively and negatively with the percentage of normal and abnormal cells, respectively (Figure 1B-C). Fluorescent labeling of HbF allowed us to discriminate nonF-cells (Figure 1Di-ii) from F-cells (Figure 1Diii-iv), and analyze their shape as captured in the brightfield images. The images confirmed the prediction that some RBCs with detectable HbF content still sickle (Figure 1Div), and also identified RBCs that are resistant to sickling despite no detectable HbF (Figure 1Dii). The percentage of non-Fcells sickling on deoxygenation was significantly higher than among F-cells (20.08% [95% CI 15.56-24.60] vs 13.44% [95% CI 10.2116.68], P , .0001). This difference was statistically significant both in patients not taking HU and in treated patients (Figure 1F). F-cells from patients on HU sickled significantly less than F-cells from patients off HU, and the same difference was borderline significant when comparing non-F-cells from patients on HU with those off HU. Our observations support that the threshold used for detection of F-cells is not the same threshold that defines protection against sickling. Similar to previous investigators, we also have found that the very high concentration of intraerythrocytic hemoglobin poses a difficult challenge in achieving the antigen saturation with antig-globin antibody that is necessary to measure accurately the amount of HbF per F-cell,4 which will be needed ultimately to test fully the mathematical modeling predictions of Steinberg et al. Differential RBC susceptibility of non-F-cells to sickling between patients on or off HU also supports the existence of additional beneficial mechanisms of HU other than HbF induction.4 The identification of factors besides HbF that modulate sickle hemoglobin polymerization may help in the design of novel therapies for HU-resistant SCA patients. Kleber Yotsumoto Fertrin Sickle Cell Vascular Disease Section, Hematology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD Eduard J. van Beers Sickle Cell Vascular Disease Section, Hematology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD Leigh Samsel Flow Cytometry Core Facility, National Heart, Lung, and Blood Institute, Bethesda, MD Laurel G. Mendelsohn Sickle Cell Vascular Disease Section, Hematology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD Rehan Saiyed Sickle Cell Vascular Disease Section, Hematology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD James S. Nichols Sickle Cell Vascular Disease Section, Hematology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD David A. Hepp Electron Microscopy Core Facility, National Heart, Lung, and Blood Institute, Bethesda, MD Christine A. Brantner Electron Microscopy Core Facility, National Heart, Lung, and Blood Institute, Bethesda, MD Mathew P. Daniels Electron Microscopy Core Facility, National Heart, Lung, and Blood Institute, Bethesda, MD J. Philip McCoy Flow Cytometry Core Facility, National Heart, Lung, and Blood Institute, Bethesda, MD Gregory J. Kato Department of Medicine, Division of Hematology-Oncology, Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA Contribution: K.Y.F., E.J.v.B., L.S., L.G.M., and R.S. performed sample processing and flow cytometry experiments with supervision of J.P.M. and G.J.K.; J.S.N. enrolled patients and procured samples; D.A.H. and C.A.B. performed electron microscopy processing, imaging and developed related figures with supervision of M.P.D.; K.Y.F., J.P.M., and G.J.K. designed the study; K.Y.F. and G.J.K. performed data analysis and drafted the manuscript; and all authors reviewed and edited the final manuscript. Conflict-of-interest disclosure: The authors declare no competing financial interests. Correspondence: Gregory J. Kato, Department of Medicine, Division of Hematology-Oncology, and the Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, 200 Lothrop St, BST E1240, Pittsburgh, PA 15261; e-mail: [email protected]. References 1. Steinberg MH, Chui DHK, Dover GJ, Sebastiani P, Alsultan A. Fetal hemoglobin in sickle cell anemia: a glass half full? Blood. 2014;123(4): 481-485. 2. van Beers EJ, Samsel L, Mendelsohn LG, et al. Imaging flow cytometry for fully automated quantification of percentage of sickled cells in sickle cell anemia. Am J Hematol. 2014;89(6):598-603. 3. Steinberg MH, Lu ZH, Barton FB, Terrin ML, Charache S, Dover GJ; Multicenter Study of Hydroxyurea. Fetal hemoglobin in sickle cell anemia: determinants of response to hydroxyurea. Blood. 1997;89(3):1078-1088. 4. Segel GB, Simon W, Lichtman MA. Should we still be focused on red cell hemoglobin F as the principal explanation for the salutary effect of hydroxyurea in sickle cell disease? Pediatr Blood Cancer. 2011;57(1):8-9. To the editor: Misleading results from saliva samples of patients post-BMT in exome analyses We routinely perform clinical exome sequencing on blood samples from patients without bone marrow transplants (BMTs). Moreover, we accept saliva samples due to the acceptable DNA quality, noninvasiveness, and improved percentage of participants.1 We assess the quality at the wet laboratory as well as the bioinformatics process which includes monitoring the number of variants after inheritance model filtering. Recently, a 4-member family (mother, father, female proband, and affected sister) was referred for exome analysis due to combined immune deficiency of undetermined genetic etiology in the proband. The samples that were submitted for analysis were blood for both parents and saliva for both affected sisters. All of the quality controls passed the set thresholds except for the inheritance
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