Genetic engineering of vein grafts resistant to atherosclerosis.

Previously, researchers have speculated that genetic engineering can improve the long-term function of vascular grafts which are prone to atherosclerosis and occlusion. In this study, we demonstrated that an intraoperative gene therapy approach using antisense oligodeoxynucleotide blockage of medial smooth muscle cell proliferation can prevent the accelerated atherosclerosis that is responsible for autologous vein graft failure. Selective blockade of the expression of genes for two cell cycle regulatory proteins, proliferating cell nuclear antigen and cell division cycle 2 kinase, was achieved in the smooth muscle cells of rabbit jugular veins grafted into the carotid arteries. This alteration of gene expression successfully redirected vein graft biology away from neointimal hyperplasia and toward medial hypertrophy, yielding conduits that more closely resembled normal arteries. More importantly, these genetically engineered grafts proved resistant to diet-induced atherosclerosis. These findings establish the feasibility of developing genetically engineered bioprostheses that are resistant to failure and better suited to the long-term treatment of occlusive vascular disease.

Why is Europe lagging on next generation access networks? Bruegel Policy Contribution ISSUE 2015/14, September 2015

Why is Europe lagging on next generation access networks? Fibre-based next generation access (NGA) roll-out across the European Union is one of the goals of the European Commission’s Digital Agenda strategy, however, there remains considerable uncertainty about how the roll-out goal can best be achieved. The underlying differences between the economics of copper-based and new fibre-based broadband infrastructures should lead to a revision of the regulatory framework for telecommunications markets. While the current regulatory measures have been useful in the past decade to sustain competition and facilitate entry into a market with already-existing infrastructures, the need to create new, much faster broadband networks calls for a rethink of the scope and strictness of regulation.

Logical Modeling and Dynamical Analysis of Cellular Networks

The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.

Complex genetic findings in a female patient with pyruvate dehydrogenase complex deficiency: Null mutations in the PDHX gene associated with unusual expression of the testis-specific PDHA2 gene in her somatic cells

Human pyruvate dehydrogenase complex (PDC) catalyzes a key step in the generation of cellular energy and is composed by three catalytic elements (E1, E2, E3), one structural subunit (E3-binding protein), and specific regulatory elements, phosphatases and kinases (PDKs, PDPs). The E1α subunit exists as two isoforms encoded by different genes: PDHA1 located on Xp22.1 and expressed in somatic tissues, and the intronless PDHA2 located on chromosome 4 and only detected in human spermatocytes and spermatids. We report on a young adult female patient who has PDC deficiency associated with a compound heterozygosity in PDHX encoding the E3-binding protein. Additionally, in the patient and in all members of her immediate family, a full-length testis-specific PDHA2 mRNA and a 5′UTR-truncated PDHA1 mRNA were detected in circulating lymphocytes and cultured fibroblasts, being bothmRNAs translated into full-length PDHA2 and PDHA1 proteins, resulting in the co-existence of both PDHA isoforms in somatic cells.Moreover, we observed that DNA hypomethylation of a CpG island in the coding region of PDHA2 gene is associatedwith the somatic activation of this gene transcription in these individuals. This study represents the first natural model of the de-repression of the testis-specific PDHA2 gene in human somatic cells, and raises some questions related to the somatic activation of this gene as a potential therapeutic approach for most forms of PDC deficiency.

Multiple regulatory variants located in cell type-specific enhancers within the PKP2 locus form major risk and protective haplotypes for canine atopic dermatitis in German shepherd dogs.

BACKGROUND Canine atopic dermatitis (CAD) is a chronic inflammatory skin disease triggered by allergic reactions involving IgE antibodies directed towards environmental allergens. We previously identified a ~1.5 Mb locus on canine chromosome 27 associated with CAD in German shepherd dogs (GSDs). Fine-mapping indicated association closest to the PKP2 gene encoding plakophilin 2. RESULTS Additional genotyping and association analyses in GSDs combined with control dogs from five breeds with low-risk for CAD revealed the top SNP 27:19,086,778 (p = 1.4 × 10(-7)) and a rare ~48 kb risk haplotype overlapping the PKP2 gene and shared only with other high-risk CAD breeds. We selected altogether nine SNPs (four top-associated in GSDs and five within the ~48 kb risk haplotype) that spanned ~280 kb forming one risk haplotype carried by 35 % of the GSD cases and 10 % of the GSD controls (OR = 5.1, p = 5.9 × 10(-5)), and another haplotype present in 85 % of the GSD cases and 98 % of the GSD controls and conferring a protective effect against CAD in GSDs (OR = 0.14, p = 0.0032). Eight of these SNPs were analyzed for transcriptional regulation using reporter assays where all tested regions exerted regulatory effects on transcription in epithelial and/or immune cell lines, and seven SNPs showed allelic differences. The DNA fragment with the top-associated SNP 27:19,086,778 displayed the highest activity in keratinocytes with 11-fold induction of transcription by the risk allele versus 8-fold by the control allele (pdifference = 0.003), and also mapped close (~3 kb) to an ENCODE skin-specific enhancer region. CONCLUSIONS Our experiments indicate that multiple CAD-associated genetic variants located in cell type-specific enhancers are involved in gene regulation in different cells and tissues. No single causative variant alone, but rather multiple variants combined in a risk haplotype likely contribute to an altered expression of the PKP2 gene, and possibly nearby genes, in immune and epithelial cells, and predispose GSDs to CAD.

Systematic proteomic strategies to map the human signaling landscape

Thesis (Ph.D.)--University of Washington, 2016-05

Generalization by symbolic abstraction in cascaded recurrent networks

Generalization performance in recurrent neural networks is enhanced by cascading several networks. By discretizing abstractions induced in one network, other networks can operate on a coarse symbolic level with increased performance on sparse and structural prediction tasks. The level of systematicity exhibited by the cascade of recurrent networks is assessed on the basis of three language domains. (C) 2004 Elsevier B.V. All rights reserved.

Genetic algorithms with applications in wireless communications

Genetic algorithms (GAs) are known to locate the global optimal solution provided sufficient population and/or generation is used. Practically, a near-optimal satisfactory result can be found by Gas with a limited number of generations. In wireless communications, the exhaustive searching approach is widely applied to many techniques, such as maximum likelihood decoding (MLD) and distance spectrum (DS) techniques. The complexity of the exhaustive searching approach in the MLD or the DS technique is exponential in the number of transmit antennas and the size of the signal constellation for the multiple-input multiple-output (MIMO) communication systems. If a large number of antennas and a large size of signal constellations, e.g. PSK and QAM, are employed in the MIMO systems, the exhaustive searching approach becomes impractical and time consuming. In this paper, the GAs are applied to the MLD and DS techniques to provide a near-optimal performance with a reduced computational complexity for the MIMO systems. Two different GA-based efficient searching approaches are proposed for the MLD and DS techniques, respectively. The first proposed approach is based on a GA with sharing function method, which is employed to locate the multiple solutions of the distance spectrum for the Space-time Trellis Coded Orthogonal Frequency Division Multiplexing (STTC-OFDM) systems. The second approach is the GA-based MLD that attempts to find the closest point to the transmitted signal. The proposed approach can return a satisfactory result with a good initial signal vector provided to the GA. Through simulation results, it is shown that the proposed GA-based efficient searching approaches can achieve near-optimal performance, but with a lower searching complexity comparing with the original MLD and DS techniques for the MIMO systems.

System for foreign exchange trading using genetic algorithms and reinforcement learning

Foreign exchange trading has emerged recently as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. A major issue for traders in the deregulated Foreign Exchange Market is when to sell and when to buy a particular currency in order to maximize profit. This paper presents novel trading strategies based on the machine learning methods of genetic algorithms and reinforcement learning.

Identification of two evolutionarily conserved and functional regulatory elements in intron 2 of the human BRCA1 gene

Cross-species comparative genomics is a powerful strategy for identifying functional regulatory elements within noncoding DNA. In this paper, comparative analysis of human and mouse intronic sequences in the breast cancer susceptibility gene (BRCA1) revealed two evolutionarily conserved noncoding sequences (CNS) in intron 2, 5 kb downstream of the core BRCA1 promoter. The functionality of these elements was examined using homologous-recombination-based mutagenesis of reporter gene-tagged cosmids incorporating these regions and flanking sequences from the BRCA1 locus. This showed that CNS-1 and CNS-2 have differential transcriptional regulatory activity in epithelial cell lines. Mutation of CNS-1 significantly reduced reporter gene expression to 30% of control levels. Conversely mutation of CNS-2 increased expression to 200% of control levels. Regulation is at the level of transcription and shows promoter specificity. Both elements also specifically bind nuclear proteins in vitro. These studies demonstrate that the combination of comparative genomics and functional analysis is a successful strategy to identify novel regulatory elements and provide the first direct evidence that conserved noncoding sequences in BRCA1 regulate gene expression. (c) 2005 Elsevier Inc. All rights reserved.

Inherent size constraints on prokaryote gene networks due to "accelerating" growth

Networks exhibiting accelerating growth have total link numbers growing faster than linearly with network size and either reach a limit or exhibit graduated transitions from nonstationary-to-stationary statistics and from random to scale-free to regular statistics as the network size grows. However, if for any reason the network cannot tolerate such gross structural changes then accelerating networks are constrained to have sizes below some critical value. This is of interest as the regulatory gene networks of single-celled prokaryotes are characterized by an accelerating quadratic growth and are size constrained to be less than about 10,000 genes encoded in DNA sequence of less than about 10 megabases. This paper presents a probabilistic accelerating network model for prokaryotic gene regulation which closely matches observed statistics by employing two classes of network nodes (regulatory and non-regulatory) and directed links whose inbound heads are exponentially distributed over all nodes and whose outbound tails are preferentially attached to regulatory nodes and described by a scale-free distribution. This model explains the observed quadratic growth in regulator number with gene number and predicts an upper prokaryote size limit closely approximating the observed value. (c) 2005 Elsevier GmbH. All rights reserved.

Small regulatory RNAs in mammals

Mammalian cells harbor numerous small non-protein-coding RNAs, including small nucleolar RNAs (snoRNAs), microRNAs (miRNAs), short interfering RNAs (siRNAs) and small double-stranded RNAs, which regulate gene expression at many levels including chromatin architecture, RNA editing, RNA stability, translation, and quite possibly transcription and splicing. These RNAs are processed by multistep pathways from the introns and exons of longer primary transcripts, including protein-coding transcripts. Most show distinctive temporal- and tissue-specific expression patterns in different tissues, including embryonal stem cells and the brain, and some are imprinted. Small RNAs control a wide range of developmental and physiological pathways in animals, including hematopoietic differentiation, adipocyte differentiation and insulin secretion in mammals, and have been shown to be perturbed in cancer and other diseases. The extent of transcription of non-coding sequences and the abundance of small RNAs suggests the existence of an extensive regulatory network on the basis of RNA signaling which may underpin the development and much of the phenotypic variation in mammals and other complex organisms and which may have different genetic signatures from sequences encoding proteins.

Genetics and genomics of osteoclast differentiation: Integrating cell signaling pathways and gene networks

The regulation of osteoclast differentiation in the bone microenvironment is critical for normal bone remodeling, as well as for various human bone diseases. Over the last decade, our knowledge of how osteoclast differentiation occurs has progressed rapidly. We highlight some of the major advances in understanding how cell signaling and transcription are integrated to direct the differentiation of this cell type. These studies used genetic, molecular, and biochemical approaches. Additionally, we summarize data obtained from studies of osteoclast differentiation that used the functional genomic approach of global gene profiling applied to osteoclast differentiation. This genomic data confirms results from studies using the classical experimental approaches and also may suggest new modes by which osteoclast differentiation and function can be modulated. Two conclusions that emerge are that osteoclast differentiation depends on a combination of fairly ubiquitously expressed transcription factors rather than unique osteoclast factors, and that the overlay of cell signaling pathways on this set of transcription factors provides a powerful mechanism to fine tune the differentiation program in response to the local bone microenvironment.

Tracking the states of a nonlinear and nonstationary system in the weight-space of artificial neural networks

We propose a novel interpretation and usage of Neural Network (NN) in modeling physiological signals, which are allowed to be nonlinear and/or nonstationary. The method consists of training a NN for the k-step prediction of a physiological signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. We de. ne a novel feature, Normalized Vector Separation (gamma(ij)), to measure the separation of two arbitrary states i and j in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that gamma(ij) can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled (within a root-means-squared-error of 0.01) by the proposed method, and the blood perfusion state of the brain could be monitored via gamma(ij), with small NNs having no more than 21 connection weight altogether.