MicroRNA-486 regulates normal erythropoiesis and enhances growth and modulates drug response in CML progenitors.

MicroRNAs (miRNAs) are key regulators of hematopoietic cell differentiation and may contribute to altered growth of leukemic stem cells. Using microarray-based miRNA profiling, we found that miRNA 486 (miR-486) is significantly upregulated in chronic myeloid leukemia (CML) compared with normal CD34(+) cells, particularly in the megakaryocyte-erythroid progenitor population. miR-486-5p expression increased during erythroid differentiation of both CML and normal CD34(+) cells. Ectopic miR-486-5p expression enhanced in vitro erythroid differentiation of normal CD34(+) cells, whereas miR-486-5p inhibition suppressed normal CD34(+) cell growth in vitro and in vivo and inhibited erythroid differentiation and erythroid cell survival. The effects of miR-486-5p on hematopoietic cell growth and survival are mediated at least in part via regulation of AKT signaling and FOXO1 expression. Using gene expression and bioinformatics analysis, together with functional screening, we identified several novel miR-486-5p target genes that may modulate erythroid differentiation. We further show that increased miR-486-5p expression in CML progenitors is related to both kinase-dependent and kinase-independent mechanisms. Inhibition of miR-486-5p reduced CML progenitor growth and enhanced apoptosis following imatinib treatment. In conclusion, our studies reveal a novel role for miR-486-5p in regulating normal hematopoiesis and of BCR-ABL-induced miR-486-5p overexpression in modulating CML progenitor growth, survival, and drug sensitivity.

The effects of digestion enzymes on islet viability and cellular composition.

The choice of enzyme blend is critical for successful islet isolation. Islet yield, viability, integrity, and function are important factors that influence the outcome of islet transplantation. Liberase HI has been used as a standard enzyme for pancreas digestion and has successfully produced islets that reversed diabetes. However, the replacement of Liberase HI with collagenase NB1 has significantly influenced the process outcome, both in quality and quantity of the isolated islets. The assessment of islet cells by Flow Cytometry (FC) has been reported to be useful for evaluating islet quality. The aim of this study was to assess the isolation outcomes and islet quality when comparing human islet cell processed with Liberase HI and NB1. A total of 66 islet isolations, 46 processed using Liberase HI and 20 using Serva NB1, were retrospectively analyzed. Islet yield, function in vitro, islet cell viability by FC, as well as isolation-related factors were compared. There was no significant difference in donor characteristics such as age and height; however, body mass index (BMI) in the Liberase HI group was significantly higher. There was also no significant difference in prepurification, postisolation, or postculture IEQ or percent recovery between the two groups. Flow data showed Liberase HI preparations had a significantly higher percent of live cells (DAPI(-)) and NG(+)/TMRE(+) when compared to NB1. Stimulation Indices (SI) for Liberase HI (n = 45) showed 3.17 and NB1 (n = 18) 2.71 (p = NS). The results of Annexin V/DAPI staining for live, apoptotic, and necrotic cells were 50.7 ± 2.24%, 14.4 ± 1.02%, and 27.8 ± 1.92% for Liberase HI versus 48.1 ± 1.93%, 12.3 ± 0.92%, and 33.9 ± 2.28% for NB1. Islets isolated using Liberase HI showed higher viable ß cells by NG/TMRE staining and decreased necrosis by Annexin V/DAPI staining. FC assessment may be useful for determining the choice of digestion enzyme to maximize viable islets.

A computational model of the human glucose-insulin regulatory system.

OBJECTIVE: A computational model of insulin secretion and glucose metabolism for assisting the diagnosis of diabetes mellitus in clinical research is introduced. The proposed method for the estimation of parameters for a system of ordinary differential equations (ODEs) that represent the time course of plasma glucose and insulin concentrations during glucose tolerance test (GTT) in physiological studies is presented. The aim of this study was to explore how to interpret those laboratory glucose and insulin data as well as enhance the Ackerman mathematical model. METHODS: Parameters estimation for a system of ODEs was performed by minimizing the sum of squared residuals (SSR) function, which quantifies the difference between theoretical model predictions and GTT's experimental observations. Our proposed perturbation search and multiple-shooting methods were applied during the estimating process. RESULTS: Based on the Ackerman's published data, we estimated the key parameters by applying R-based iterative computer programs. As a result, the theoretically simulated curves perfectly matched the experimental data points. Our model showed that the estimated parameters, computed frequency and period values, were proven a good indicator of diabetes. CONCLUSION: The present paper introduces a computational algorithm to biomedical problems, particularly to endocrinology and metabolism fields, which involves two coupled differential equations with four parameters describing the glucose-insulin regulatory system that Ackerman proposed earlier. The enhanced approach may provide clinicians in endocrinology and metabolism field insight into the transition nature of human metabolic mechanism from normal to impaired glucose tolerance.

Microassay for glucose-induced preproinsulin mRNA expression to assess islet functional potency for islet transplantation.

BACKGROUND: The capacity for insulin synthesis in islets is important for islet transplantation to succeed. We developed a microassay that evaluates the potency of human islets by measuring changes in glucose-induced human insulin gene (INS) expression using a single islet in octuplicate samples. METHODS: Poly (A) messenger RNA (mRNA) was purified from a set of single handpicked human islets. Glucose-induced mature (postspliced) and premature (prespliced) insulin mRNA were quantified by reverse-transcriptase polymerase chain reaction using several insulin mRNA primers designed at different locations including, intron, exon, and an exon-intron junction. RESULTS: The synthesis of premature INS mRNA was significantly increased in islets exposed to high glucose for 16 vs. 4 hr (P<0.01), whereas mature INS mRNA showed no difference. Glucose-induced premature INS mRNA synthesis was attenuated in heat-damaged islets. Stimulation index (SI) calculated by normalizing premature by mature INS mRNA (SI_INS mRNA) positively correlated with SI of insulin release (SI_16h insulin) from the same set of islets during 16-hr incubation in high or low glucose media, and SI of glucose-mediated insulin release obtained from the same islet lot in a perifusion system (n=12). Furthermore, linear multiple regression analysis using SI_INS mRNA and SI_16h insulin predicted islet transplantation outcome in nonobese diabetic (NOD) scid mice (n=8). CONCLUSION: The measurement of glucose-induced premature INS mRNA normalized by mature INS mRNA can be used to assess the functional quality of human islets and may predict islet function after transplantation in type 1 diabetic patients.

Effective targeting of quiescent chronic myelogenous leukemia stem cells by histone deacetylase inhibitors in combination with imatinib mesylate.

Imatinib mesylate (IM) induces remission in chronic myelogenous leukemia (CML) patients but does not eliminate leukemia stem cells (LSCs), which remain a potential source of relapse. Here we investigated the ability of HDAC inhibitors (HDACis) to target CML stem cells. Treatment with HDACis combined with IM effectively induced apoptosis in quiescent CML progenitors resistant to elimination by IM alone, and eliminated CML stem cells capable of engrafting immunodeficient mice. In vivo administration of HDACis with IM markedly diminished LSCs in a transgenic mouse model of CML. The interaction of IM and HDACis inhibited genes regulating hematopoietic stem cell maintenance and survival. HDACi treatment represents an effective strategy to target LSCs in CML patients receiving tyrosine kinase inhibitors.

Quantitative assessment of ß-cell apoptosis and cell composition of isolated, undisrupted human islets by laser scanning cytometry.

BACKGROUND: Assays for assessing human islet cell quality, which provide results before transplantation, would be beneficial to improve the outcomes for islet transplantation therapy. Parameters such as percent ß-cell apoptosis and cell composition are found to vary markedly between different islet preparations and may serve as markers of islet quality. We have developed fluorescence-based assays using laser scanning cytometry for assessing ß-cell apoptosis and islet cell composition on serial sections of intact isolated islets. METHODS: Isolated human islets were fixed in formalin and embedded in paraffin. Serial sections were immunostained for the pancreatic hormones and acinar and ductal cell markers. DNA fragmentation was used to label apoptotic cells. Stained cells were quantified using an iCys laser scanning cytometer. RESULTS: Islet preparations from 102 human pancreatic islet isolations were analyzed. For the whole set of islet preparations, we found a mean islet cell composition of 54.5%±1.2% insulin-positive, 33.9%±1.2% glucagon, 12.1%±0.7% somatostatin, and 1.5%±0.2% pancreatic polypeptide-positive cells. The apoptotic ß cells were 2.85%±0.4% with a range of 0.27% to 18.3%. The percentage of apoptotic ß cells correlated well (P<0.0001, n=59) with results obtained in vivo by transplantation of the corresponding islets in diabetic NODscid mice. CONCLUSIONS: The analysis of whole, nondissociated islets for cell composition and ß-cell apoptosis using laser scanning cytometry gives reliable and reproducible results and could be performed both before islet transplantation and on preserved cell blocks at any time in future. Thus, they can be a powerful tool for islet quality assessment.

A perturbation-based estimate algorithm for parameters of coupled ordinary differential equations, applications from chemical reactions to metabolic dynamics.

Conversion of complex phenomena in medicine, pharmaceutical and systems biology fields to a system of ordinary differential equations (ODEs) and identification of parameters from experimental data and theoretical model equations can be treated as a computational engine to arrive at the best solution for chemical reactions, biochemical metabolic and intracellular pathways. Particularly, to gain insight into the pathophysiology of diabetes's metabolism in our current clinical studies, glucose kinetics and insulin secretion can be assessed by the ODE model. Parameter estimation is usually performed by minimizing a cost function which quantifies the difference between theoretical model predictions and experimental measurements. This paper explores how the numerical method and iteration program are developed to search ODE's parameters using the perturbation method, instead of the Gauss-Newton or Levenberg-Marquardt method. Several interesting applications, including Lotka-Volterra chemical reaction system, Lorenz chaos, dynamics of tetracycline hydrochloride concentration, and Bergman's Minimal Model for glucose kinetics are illustrated.