Molecular analysis of PDGFRA and PDGFRB genes by rapid single-strand conformation polymorfism (SSCP) in patients with core-binding factor leukaemias with KIT or FLT3 mutation

BACKGROUND: Mutations involving KIT and FLT3 genes, encoding tyrosine kinase (TK) membrane receptors, are detected in core-binding factor leukaemia (CBFL) patients. PDFGRA and PDGFRB encode class III TK receptors and are involved both in physiological processes and in the pathogenesis of haematological and solid tumours. The aim of this study was to investigate if PDGFR mutations are involved in CBFL. PATIENTS AND METHODS: In order to detect PDGFR mutations in CBFL, 35 patients without KIT or FLT3 mutations patients were screened by rapid and sensitive single-strand conformation polymorphism (SSCP) analysis. Sequence analysis was performed in polymerase chain reaction (PCR) products showing altered mobility in SSCP analysis in order to determine the nucleotide changes. RESULTS: Three types of single-nucleotide polymorphism (SNP) were detected in the PDGFRA gene (exon 12, exon 13 and exon 18) while no mutation of PDGFRB was detected in the tested CBFLs. CONCLUSION: These data showed that no pathogenic mutations in PDGFRA and PDGFRB were detected in the context of CBFL without KIT and FLT3 mutations. Thus, PDGFR genes do not seem to be involved in CBFL and future studies are needed to establish the genetic causes of the disease in these particular patients

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background

<p>Abstract</p> <p>Background</p> <p>Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis</p> <p>Results</p> <p>Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg.</p> <p>Conclusion</p> <p>This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.</p

Plasma homocysteine levels and cardiovascular mortality in patients with end-stage renal disease

Hyperhomocysteinemia is considered an independent risk factor for atherosclerosis in patients with normal renal function. Plasma homocysteine (Hcy) is increased in patients with chronic renal failure (CRF) and could be linked to their high cardiovascular (CV) morbidity and mortality. We prospectively studied 77 patients (47 males and 30 females aged 62.85 \ub1 1.53 yrs) who had been on maintenance hemodialysis (HD) (4 hr/ 73/week) for 65.5 \ub1 7.23 months. Patients were followed-up for 44 months. At baseline, blood samples were taken for hemoglobin (Hb), total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, serum calcium, serum phosphates, parathyroid hormone (PTH), Hcy, vitamin B12, serum and erythrocyte folate and methylentetrahydrofolate-reductase (t-MTH-FR) genotype determination. Plasma Hcy levels of patients were divided into four quartiles. The univariate analysis demonstrated a significant relationship between Hcy and diastolic blood pressure (BP) (r=0.45; p=0.003), and both plasma (r=-0.30; p=0.03) and erythrocyte (r=-0.48; p=0.01) folate levels and CV score (r=0.39; p=0.007). Kaplan-Meier analysis showed that the mortality rate due to CV events was statistically significantly higher in the 4th Hcy quartile (68%; 12 patients) vs. the 3rd quartile (12%; two patients), the 2nd quartile (28%; four patients) and the 1st quartile (14%; two patients) (log-rank test p=0.02). Cox's regression analysis for CV survival showed that Hcy was a positive CV mortality predictor (\u3b2=0.02; hazard ratio=1.031; 95% confidence interval (95% CI): 1.013-1.050; p=0.001), while LDL cholesterol and albumin related negatively to CV mortality (LDL cholesterol: \u3b2=-0.02; hazard ratio=0.095; 95% CI: 0.0957-0.0997; p=0.035; albumin: P=-2.35; hazard ratio=0.097; 95% CI: 0.011-0.847; p=0.026). Our results show that Hcy is a strong independent mortality predictor in HD patients with a 3% increase in mortality for each 1 \u3bcmol/L increase in plasma Hcy concentration. This agrees with previous findings confirming the role of Hcy in predicting CV risk factors in uremic patients

A simple discountinuos buffer system for increased resolution and speed in gel electrophoretic analysis of DNA sequence

NUCLEIC ACID RESEARC