Short-Term Prognosis of Transient Ischemic Attack and Predictive Value of the ABCD2 Score in Hong Kong Chinese

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Duplications of the critical Rubinstein-Taybi deletion region on chromosome 16p13.3 cause a novel recognisable syndrome

Background The introduction of molecular karyotyping technologies facilitated the identification of specific genetic disorders associated with imbalances of certain genomic regions. A detailed phenotypic delineation of interstitial 16p13.3 duplications is hampered by the scarcity of such patients. Objectives To delineate the phenotypic spectrum associated with interstitial 16p13.3 duplications, and perform a genotype-phenotype analysis. Results The present report describes the genotypic and phenotypic delineation of nine submicroscopic interstitial 16p13.3 duplications. The critically duplicated region encompasses a single gene, CREBBP, which is mutated or deleted in Rubinstein-Taybi syndrome. In 10 out of the 12 hitherto described probands, the duplication arose de novo. Conclusions Interstitial 16p13.3 duplications have a recognizable phenotype, characterized by normal to moderately retarded mental development, normal growth, mild arthrogryposis, frequently small and proximally implanted thumbs and characteristic facial features. Occasionally, developmental defects of the heart, genitalia, palate or the eyes are observed. The frequent de novo occurrence of 16p13.3 duplications demonstrates the reduced reproductive fitness associated with this genotype. Inheritance of the duplication from a clinically normal parent in two cases indicates that the associated phenotype is incompletely penetrant

Crystal Structures of Two Aminoglycoside Kinases Bound with a Eukaryotic Protein Kinase Inhibitor

Antibiotic resistance is recognized as a growing healthcare problem. To address this issue, one strategy is to thwart the causal mechanism using an adjuvant in partner with the antibiotic. Aminoglycosides are a class of clinically important antibiotics used for the treatment of serious infections. Their usefulness has been compromised predominantly due to drug inactivation by aminoglycoside-modifying enzymes, such as aminoglycoside phosphotransferases or kinases. These kinases are structurally homologous to eukaryotic Ser/Thr and Tyr protein kinases and it has been shown that some can be inhibited by select protein kinase inhibitors. The aminoglycoside kinase, APH(3′)-IIIa, can be inhibited by CKI-7, an ATP-competitive inhibitor for the casein kinase 1. We have determined that CKI-7 is also a moderate inhibitor for the atypical APH(9)-Ia. Here we present the crystal structures of CKI-7-bound APH(3′)-IIIa and APH(9)-Ia, the first structures of a eukaryotic protein kinase inhibitor in complex with bacterial kinases. CKI-7 binds to the nucleotide-binding pocket of the enzymes and its binding alters the conformation of the nucleotide-binding loop, the segment homologous to the glycine-rich loop in eurkaryotic protein kinases. Comparison of these structures with the CKI-7-bound casein kinase 1 reveals features in the binding pockets that are distinct in the bacterial kinases and could be exploited for the design of a bacterial kinase specific inhibitor. Our results provide evidence that an inhibitor for a subset of APHs can be developed in order to curtail resistance to aminoglycosides

Detailed deletion mapping of chromosome band 14q32 in human neuroblastoma defines a 1.1-Mb region of common allelic loss

Neuroblastoma (NB) is a well-known malignant disease in infants, but its molecular mechanisms have not yet been fully elucidated. To investigate the genetic contribution of abnormalities on the long arm of chromosome 14 (14q) in NB, we analysed loss of heterozygosity (LOH) in 54 primary NB samples using 12 microsatellite markers on 14q32. Seventeen (31%) of 54 tumours showed LOH at one or more of the markers analysed, and the smallest common region of allelic loss was identified between D14S62 and D14S987. This region was estimated to be 1-cM long from the linkage map. Fluorescence in situ hybridization also confirmed the loss. There was no statistical correlation between LOH and any clinicopathologic features, including age, stage, amplification of MYCN and ploidy. We further constructed a contig spanning the lost region using bacterial artificial chromosome and estimated this region to be approximately 1.1-Mb by pulsed-field gel electrophoresis. Our results will contribute to cloning and characterizing the putative tumour-associated gene(s) in 14q32 in NB. © 2000 Cancer Research Campaig

Role of defects and disorder in the half-metallic full-Heusler compounds

Half-metallic ferromagnets and especially the full-Heusler alloys containing Co are at the center of scientific research due to their potential applications in spintronics. For realistic devices it is important to control accurately the creation of defects in these alloys. We review some of our late results on the role of defects and impurities in these compounds. More precisely we present results for the following cases (i) doping and disorder in Co$_2$Cr(Mn)Al(Si) alloys, (ii) half-metallic ferrimagnetism appeared due to the creation of Cr(Mn) antisites in these alloys, (iii) Co-doping in Mn$_2$VAl(Si) alloys leading to half-metallic antiferromagnetism, and finally (iv) the occurrence of vacancies in the full-Heusler alloys containing Co and Mn. These results are susceptible of encouraging further theoretical and experimental research in the properties of these compounds.Comment: Chapter intended for a book with contributions of the invited speakers of the International Conference on Nanoscale Magnetism 2007. Revised version contains new figure

Mapping transcription mechanisms from multimodal genomic data

Background Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data. Results We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate. Conclusions The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.National Human Genome Research Institute (U.S.) (R01HG003354)National Institute of Allergy and Infectious Diseases (U.S.) (U19 AI067854-05)National Heart, Lung, and Blood Institute (grant T32 HL007427-28)National Institutes of Health (U.S.) (grant K99 LM009826

From bit to it: How a complex metabolic network transforms information into living matter

Organisms live and die by the amount of information they acquire about their environment. The systems analysis of complex metabolic networks allows us to ask how such information translates into fitness. A metabolic network transforms nutrients into biomass. The better it uses information on available nutrient availability, the faster it will allow a cell to divide. I here use metabolic flux balance analysis to show that the accuracy I (in bits) with which a yeast cell can sense a limiting nutrient's availability relates logarithmically to fitness as indicated by biomass yield and cell division rate. For microbes like yeast, natural selection can resolve fitness differences of genetic variants smaller than 10-6, meaning that cells would need to estimate nutrient concentrations to very high accuracy (greater than 22 bits) to ensure optimal growth. I argue that such accuracies are not achievable in practice. Natural selection may thus face fundamental limitations in maximizing the information processing capacity of cells. The analysis of metabolic networks opens a door to understanding cellular biology from a quantitative, information-theoretic perspective

Identification of metabolic engineering targets through analysis of optimal and sub-optimal routes

Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are therefore much needed. Towards addressing this need, we here introduce the notion of structural flux, which is derived from the enumeration of all pathways in the metabolic network in question and accounts for the contribution towards a given biological objective function. We show that the theoretically estimated structural fluxes are good predictors of experimentally measured intra-cellular fluxes in two model organisms, namely, Escherichia coli and Saccharomyces cerevisiae. For a small number of fluxes for which the predictions were poor, the corresponding enzyme-coding transcripts were also found to be distinctly regulated, showing the ability of structural fluxes in capturing the underlying regulatory principles. Exploiting the observed correspondence between in vivo fluxes and structural fluxes, we propose an in silico metabolic engineering approach, iStruF, which enables the identification of gene deletion strategies that couple the cellular biological objective with the product flux while considering optimal as well as sub-optimal routes and their efficiency.This work was supported by the Portuguese Science Foundation [grant numbers MIT-Pt/BS-BB/0082/2008, SFRH/BPD/44180/2008 to ZS] (http://www.fct.pt/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering