Liver transplantation for type I and type IV glycogen storage disease

Progressive liver failure or hepatic complications of the primary disease led to orthotopic liver transplantation in eight children with glycogen storage disease over a 9-year period. One patient had glycogen storage disease (GSD) type I (von Gierke disease) and seven patients had type IV GSD (Andersen disease). As previously reported [19], a 16.5-year-old-girl with GSD type I was successfully treated in 1982 by orthotopic liver transplantation under cyclosporine and steroid immunosuppression. The metabolic consequences of the disease have been eliminated, the renal function and size have remained normal, and the patient has lived a normal young adult life. A late portal venous thrombosis was treated successfully with a distal splenorenal shunt. Orthotopic liver transplantation was performed in seven children with type N GSD who had progressive hepatic failure. Two patients died early from technical complications. The other five have no evidence of recurrent hepatic amylopectinosis after 1.1–5.8 postoperative years. They have had good physical and intellectual maturation. Amylopectin was found in many extrahepatic tissues prior to surgery, but cardiopathy and skeletal myopathy have not developed after transplantation. Postoperative heart biopsies from patients showed either minimal amylopectin deposits as long as 4.5 years following transplantation or a dramatic reduction in sequential biopsies from one patient who initially had dense myocardial deposits. Serious hepatic derangement is seen most commonly in types T and IV GSD. Liver transplantation cures the hepatic manifestations of both types. The extrahepatic deposition of abnormal glycogen appears not to be problematic in type I disease, and while potentially more threatening in type IV disease, may actually exhibit signs of regression after hepatic allografting

Cross-frequency coupling of brain oscillations in studying motivation and emotion

Research has shown that brain functions are realized by simultaneous oscillations in various frequency bands. In addition to examining oscillations in pre-specified bands, interactions and relations between the different frequency bandwidths is another important aspect that needs to be considered in unraveling the workings of the human brain and its functions. In this review we provide evidence that studying interdependencies between brain oscillations may be a valuable approach to study the electrophysiological processes associated with motivation and emotional states. Studies will be presented showing that amplitude-amplitude coupling between delta-alpha and delta-beta oscillations varies as a function of state anxiety and approach-avoidance-related motivation, and that changes in the association between delta-beta oscillations can be observed following successful psychotherapy. Together these studies suggest that cross-frequency coupling of brain oscillations may contribute to expanding our understanding of the neural processes underlying motivation and emotion

Natural computation meta-heuristics for the in silico optimization of microbial strains

<p>Abstract</p> <p>Background</p> <p>One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for <it>in silico </it>metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution.</p> <p>Results</p> <p>This work reports on improved EAs, as well as novel Simulated Annealing (SA) algorithms to address the task of <it>in silico </it>metabolic engineering. Both approaches use a variable size set-based representation, thereby allowing the automatic finding of the best number of gene deletions necessary for achieving a given productivity goal. The work presents extensive computational experiments, involving four case studies that consider the production of succinic and lactic acid as the targets, by using <it>S. cerevisiae </it>and <it>E. coli </it>as model organisms. The proposed algorithms are able to reach optimal/near-optimal solutions regarding the production of the desired compounds and presenting low variability among the several runs.</p> <p>Conclusion</p> <p>The results show that the proposed SA and EA both perform well in the optimization task. A comparison between them is favourable to the SA in terms of consistency in obtaining optimal solutions and faster convergence. In both cases, the use of variable size representations allows the automatic discovery of the approximate number of gene deletions, without compromising the optimality of the solutions.</p

Elevated Serum Uric Acid Is Associated with High Circulating Inflammatory Cytokines in the Population-Based Colaus Study

BACKGROUND: The relation of serum uric acid (SUA) with systemic inflammation has been little explored in humans and results have been inconsistent. We analyzed the association between SUA and circulating levels of interleukin-6 (IL-6), interleukin-1beta (IL-1beta), tumor necrosis factor- alpha (TNF-alpha) and C-reactive protein (CRP). METHODS AND FINDINGS: This cross-sectional population-based study conducted in Lausanne, Switzerland, included 6085 participants aged 35 to 75 years. SUA was measured using uricase-PAP method. Plasma TNF-alpha, IL-1beta and IL-6 were measured by a multiplexed particle-based flow cytometric assay and hs-CRP by an immunometric assay. The median levels of SUA, IL-6, TNF-alpha, CRP and IL-1beta were 355 micromol/L, 1.46 pg/mL, 3.04 pg/mL, 1.2 mg/L and 0.34 pg/mL in men and 262 micromol/L, 1.21 pg/mL, 2.74 pg/mL, 1.3 mg/L and 0.45 pg/mL in women, respectively. SUA correlated positively with IL-6, TNF-alpha and CRP and negatively with IL-1beta (Spearman r: 0.04, 0.07, 0.20 and 0.05 in men, and 0.09, 0.13, 0.30 and 0.07 in women, respectively, P&lt;0.05). In multivariable analyses, SUA was associated positively with CRP (beta coefficient +/- SE = 0.35+/-0.02, P&lt;0.001), TNF-alpha (0.08+/-0.02, P&lt;0.001) and IL-6 (0.10+/-0.03, P&lt;0.001), and negatively with IL-1beta (-0.07+/-0.03, P = 0.027). Upon further adjustment for body mass index, these associations were substantially attenuated. CONCLUSIONS: SUA was associated positively with IL-6, CRP and TNF-alpha and negatively with IL-1beta, particularly in women. These results suggest that uric acid contributes to systemic inflammation in humans and are in line with experimental data showing that uric acid triggers sterile inflammation

OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis

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

Computational Design of Auxotrophy-Dependent Microbial Biosensors for Combinatorial Metabolic Engineering Experiments

Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools)

Inflammation-dependent cerebrospinal fluid hypersecretion by the choroid plexus epithelium in posthemorrhagic hydrocephalus

This is the author accepted manuscript. The final version is available from Springer Nature via the DOI in this recordThere is another record in ORE for this publication: http://hdl.handle.net/10871/33419The choroid plexus epithelium (CPE) secretes higher volumes of fluid (cerebrospinal fluid, CSF) than any other epithelium and simultaneously functions as the blood-CSF barrier to gate immune cell entry into the central nervous system. Posthemorrhagic hydrocephalus (PHH), an expansion of the cerebral ventricles due to CSF accumulation following intraventricular hemorrhage (IVH), is a common disease usually treated by suboptimal CSF shunting techniques. PHH is classically attributed to primary impairments in CSF reabsorption, but little experimental evidence supports this concept. In contrast, the potential contribution of CSF secretion to PHH has received little attention. In a rat model of PHH, we demonstrate that IVH causes a Toll-like receptor 4 (TLR4)- and NF-κB-dependent inflammatory response in the CPE that is associated with a ∼3-fold increase in bumetanide-sensitive CSF secretion. IVH-induced hypersecretion of CSF is mediated by TLR4-dependent activation of the Ste20-type stress kinase SPAK, which binds, phosphorylates, and stimulates the NKCC1 co-transporter at the CPE apical membrane. Genetic depletion of TLR4 or SPAK normalizes hyperactive CSF secretion rates and reduces PHH symptoms, as does treatment with drugs that antagonize TLR4-NF-κB signaling or the SPAK-NKCC1 co-transporter complex. These data uncover a previously unrecognized contribution of CSF hypersecretion to the pathogenesis of PHH, demonstrate a new role for TLRs in regulation of the internal brain milieu, and identify a kinase-regulated mechanism of CSF secretion that could be targeted by repurposed US Food and Drug Administration (FDA)-approved drugs to treat hydrocephalus.We thank D.R. Alessi (Dundee) and R.P. Lifton (Rockefeller) for their support. K.T.K. is supported by the March of Dimes Basil O'Connor Award, a Simons Foundation SFARI Grant, the Hydrocephalus Association Innovator Award, and the NIH (4K12NS080223-05). J.M.S. is supported by the National Institute of Neurological Disorders and Stroke (NINDS) (NS060801; NS061808) and the US Department of Veterans Affairs (1BX002889); R.M. is supported by the Howard Hughes Medical Institute

Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here

Luteinising hormone-releasing hormone analogue reverses the cell adhesion profile of EGFR overexpressing DU-145 human prostate carcinoma subline

Cetrorelix, a luteinising hormone-releasing hormone (LHRH) analogue, has been shown to limit growth of the human androgen-independent prostate cell line DU-145, although other inhibitory actions may also be affected. Both growth and invasion of DU-145 cells are linked to autocrine epidermal growth factor receptor (EGFR) signalling. Invasiveness requires not only cells to migrate to conduits, but also reduced adhesiveness between tumour cells to enable separation from the tumour mass. Thus, we investigated whether Cetrorelix alters the DU-145 cell–cell adhesion and if this occurs via altered EGFR signalling. Pharmacologic levels of Cetrorelix limited the invasiveness of a highly invasive DU-145 subline overexpressing full-length EGFR (DU-145 WT). Extended exposure of the cells to Cetrorelix resulted in increased levels of the cell–cell adhesion complex molecules E-cadherin, α- and β-catenin, and p120. Puromycin blocked the increases in E-cadherin and β-catenin levels, suggesting that de novo protein synthesis is required. The Cetrorelix effect appears to occur via transmodulation of EGFR by a protein kinase C (PKC)-dependent mechanism, as there were no changes in DU-145 cells expressing EGFR engineered to negate the PKC transattenuation site (DU-145 A654); downregulation of EGFR signalling produced a similar upregulation in adhesion complex proteins, further suggesting a role for autocrine signalling. Cetrorelix increased the cell–cell adhesiveness of DU-145 WT cells to an extent similar to that seen when autocrine EGFR signalling is blocked; as expected, DU-145 A654 cell–cell adhesion also was unaffected by Cetrorelix. The increased adhesiveness is expected as the adhesion complex molecules moved to the cells' periphery. These data offer direct insight into the possible crosstalk pathways between the LHRH and EGFR receptor signalling. The ability of Cetrorelix to downregulate EGFR signalling and subsequently reverse the antiadhesiveness found in metastatic prostate cancer highlights a novel potential target for therapeutic strategies