Methylenetetrahydrofolate reductase (MTHFR) polymorphisms and risk of molecularly defined subtypes of childhood acute leukemia

Low folate intake as well as alterations in folate metabolism as a result of polymorphisms in the enzyme methylenetetrahydrofolate reductase (MTHFR) have been associated with an increased incidence of neural tube defects, vascular disease, and some cancers. Polymorphic variants of MTHFR lead to enhanced thymidine pools and better quality DNA synthesis that could afford some protection from the development of leukemias, particularly those with translocations. We now report associations of MTHFR polymorphisms in three subgroups of pediatric leukemias: infant lymphoblastic or myeloblastic leukemias with MLL rearrangements and childhood lymphoblastic leukemias with either TEL-AML1 fusions or hyperdiploid karyotypes. Pediatric leukemia patients (n = 253 total) and healthy newborn controls (n = 200) were genotyped for MTHFR polymorphisms at nucleotides 677 (C→T) and 1,298 (A→C). A significant association for carriers of C677T was demonstrated for leukemias with MLL translocations (MLL+, n = 37) when compared with controls [adjusted odd ratios (OR) = 0.36 with a 95% confidence interval (CI) of 0.15–0.85; P = 0.017]. This protective effect was not evident for A1298C alleles (OR = 1.14). In contrast, associations for A1298C homozygotes (CC; OR = 0.26 with a 95% CI of 0.07–0.81) and C677T homozygotes (TT; OR = 0.49 with a 95% CI of 0.20–1.17) were observed for hyperdiploid leukemias (n = 138). No significant associations were evident for either polymorphism with TEL-AML1+ leukemias (n = 78). These differences in allelic associations may point to discrete attributes of the two alleles in their ability to alter folate and one-carbon metabolite pools and impact after DNA synthesis and methylation pathways, but should be viewed cautiously pending larger follow-up studies. The data provide evidence that molecularly defined subgroups of pediatric leukemias have different etiologies and also suggest a role of folate in the development of childhood leukemia.

Biomechanical activation of vascular endothelium as a determinant of its functional phenotype

One of the striking features of vascular endothelium, the single-cell-thick lining of the cardiovascular system, is its phenotypic plasticity. Various pathophysiologic factors, such as cytokines, growth factors, hormones, and metabolic products, can modulate its functional phenotype in health and disease. In addition to these humoral stimuli, endothelial cells respond to their biomechanical environment, although the functional implications of this biomechanical paradigm of activation have not been fully explored. Here we describe a high-throughput genomic analysis of modulation of gene expression observed in cultured human endothelial cells exposed to two well defined biomechanical stimuli—a steady laminar shear stress and a turbulent shear stress of equivalent spatial and temporal average intensity. Comparison of the transcriptional activity of 11,397 unique genes revealed distinctive patterns of up- and down-regulation associated with each type of stimulus. Cluster analyses of transcriptional profiling data were coupled with other molecular and cell biological techniques to examine whether these global patterns of biomechanical activation are translated into distinct functional phenotypes. Confocal immunofluorescence microscopy of structural and contractile proteins revealed the formation of a complex apical cytoskeleton in response to laminar shear stress. Cell cycle analysis documented different effects of laminar and turbulent shear stresses on cell proliferation. Thus, endothelial cells have the capacity to discriminate among specific biomechanical forces and to translate these input stimuli into distinctive phenotypes. The demonstration that hemodynamically derived stimuli can be strong modulators of endothelial gene expression has important implications for our understanding of the mechanisms of vascular homeostasis and atherogenesis.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.