Treatment of enterohemorrhagic Escherichia coli (EHEC) infection and hemolytic uremic syndrome (HUS)

Verotoxigenic Escherichia coli (VTEC) are a specialized group of E. coli that can cause severe colonic disease and renal failure. Their pathogenicity derives from virulence factors that enable the bacteria to colonize the colon and deliver extremely powerful toxins known as verotoxins (VT) or Shiga toxins (Stx) to the systemic circulation. The recent devastating E. coli O104:H4 epidemic in Europe has shown how helpless medical professionals are in terms of offering effective therapies. By examining the sources and distribution of these bacteria, and how they cause disease, we will be in a better position to prevent and treat the inevitable future cases of sporadic disease and victims of common source outbreaks. Due to the complexity of pathogenesis, it is likely a multitargeted approach is warranted. Developments in terms of these treatments are discussed

Engineering Yarrowia lipolytica to Produce Glycoproteins Homogeneously Modified with the Universal Man3GlcNAc2 N-Glycan Core

Yarrowia lipolytica is a dimorphic yeast that efficiently secretes various heterologous proteins and is classified as “generally recognized as safe.” Therefore, it is an attractive protein production host. However, yeasts modify glycoproteins with non-human high mannose-type N-glycans. These structures reduce the protein half-life in vivo and can be immunogenic in man. Here, we describe how we genetically engineered N-glycan biosynthesis in Yarrowia lipolytica so that it produces Man3GlcNAc2 structures on its glycoproteins. We obtained unprecedented levels of homogeneity of this glycanstructure. This is the ideal starting point for building human-like sugars. Disruption of the ALG3 gene resulted in modification of proteins mainly with Man5GlcNAc2 and GlcMan5GlcNAc2 glycans, and to a lesser extent with Glc2Man5GlcNAc2 glycans. To avoid underoccupancy of glycosylation sites, we concomitantly overexpressed ALG6. We also explored several approaches to remove the terminal glucose residues, which hamper further humanization of N-glycosylation; overexpression of the heterodimeric Apergillus niger glucosidase II proved to be the most effective approach. Finally, we overexpressed an α-1,2-mannosidase to obtain Man3GlcNAc2 structures, which are substrates for the synthesis of complex-type glycans. The final Yarrowia lipolytica strain produces proteins glycosylated with the trimannosyl core N-glycan (Man3GlcNAc2), which is the common core of all complex-type N-glycans. All these glycans can be constructed on the obtained trimannosyl N-glycan using either in vivo or in vitro modification with the appropriate glycosyltransferases. The results demonstrate the high potential of Yarrowia lipolytica to be developed as an efficient expression system for the production of glycoproteins with humanized glycans

Learning low-dimensional separable decompositions of MIMO non-linear systems

We present a new internal structure exploration method developed for the multiple-input multiple-output (MIMO) dynamical systems with finite memory and almost arbitrary non-linear characteristic. The proposed Double Separation Algorithm applies distance correlation screening for pre-selection of those system inputs that contribute to the consecutive outputs and, based on the first-stage inference outcomes, estimates projection coefficients sensitive to the existence of additive system sub-characteristics. In effect, the proposed approach allows for effective exploration of the internal system structure. A numerical experiment on an MIMO nonlinear finite impulse response (NFIR) system illustrates the ability of the proposed approach to indicate which of the system inputs contribute to which of the system outputs. The experiment also illustrates the ability of the approach to detect which of the nonlinear sub-characteristics, recovered in the first stage of the approach, can be separated into a sum of lower-dimensional sub-characteristics.</p

Incorporating Best Linear Approximation within LS-SVM-Based Hammerstein System Identification

© 2015 IEEE. Hammerstein systems represent the coupling of a static nonlinearity and a linear time invariant (LTI) system. The identification problem of such systems has been a focus of research during a long time as it is not a trivial task. In this paper a methodology for identifying Hammerstein systems is proposed. To achieve this, a combination of two powerful techniques is used, namely, we combine Least Squares Support Vector Machines (LS-SVM) and the Best Linear Approximation (BLA). First, an approximation to the LTI block is obtained through the BLA method. Then, the estimated coefficients of the transfer function from the LTI block are included in a LS-SVM formulation for modeling the system. The results indicate that a good estimation of the underlying nonlinear system can be obtained up to a scaling factor.status: publishe

Hammerstein system identification using LS-SVM and steady state time response

© 2016 EUCA. In this paper a new system identification approach for Hammerstein systems is proposed. A straightforward estimation of the nonlinear block through the use of LS-SVM is done by making use of the behavior of Hammerstein systems in steady state. Using the estimated nonlinear block, the intermediate variable is calculated. Using the latter and the known output, the linear block can be estimated. The results indicate that the method can effectively identify Hammerstein systems also in the presence of a considerable amount of noise. The well-known capabilities of LS-SVM for the representation of nonlinear functions play an important role in the generalization capabilities of the method allowing to work with a wide range of model classes. The proposed method's main strength lies precisely in the identification of the nonlinear block of the Hammerstein system. The relevance of these findings resides in the fact that a very good estimation of the inner workings of a Hammerstein system can be achieved.status: publishe

The age-dependent expression of the F18(+) E.coli receptor on porcine gut epithelial cells is positively correlated with the presence of histo-blood group antigens

F18(+) Escherichia coli have the ability to colonize the gut and cause oedema disease or post-weaning diarrhoea by adhering to specific F18 receptors (F18R) on the porcine epithelium. Although it is well established that a DNA polymorphism on base pair 307 of the FUT1 gene, encoding an alpha(1,2)fucosyltransferase, accounts for the F18R phenotype, the F18R nature is not elucidated yet. The aim of the present study was to investigate the correlation between the presence of H-2 histo-blood group antigens (HBGAs) or its derivative A-2 HBGAs on the porcine gut epithelium and F18(+) E. coli adherence. A significant positive correlation was found between expression of both the H-2 (r = 0.586, P < 0.01) and A-2 (r = 0.775, P < 0.01) HBGAs and F18(+) E. coli adherence after examination of 74 pigs aged from 0 to 23 weeks. The majority of the genetically resistant pigs (FUT1M307(A/A)) showed no HBGA expression (91.7%) and no F18(+) E. coli adherence (83.3%). In addition, it was found that F18R expression levels rise with increasing age during the first 3 weeks after birth and that F18R expression is maintained in older pigs (3-23 weeks old). Taken together, these data suggest that, apart from H-2 HBGAs, A-2 HBGAs might be involved in F18(+) E. coli adherence. (c) 2007 Elsevier V.B. All rights reserved.status: publishe

Comparison of several data-driven non-linear system identification methods on a simplified glucoregulatory system example

In this study, several advanced data-driven non-linear identification techniques are compared on a specific problem: a simplified glucoregulatory system modelling example. This problem represents a challenge in the development of an artificial pancreas for Type 1 diabetes mellitus treatment, since for this application good non-linear models are needed to design accurate closed-loop controllers to regulate the glucose level in the blood. Block-oriented as well as state-space models are used to describe both the dynamics and the non-linear behaviour of the insulin–glucose system, and the advantages and drawbacks of each method are pointed out. The obtained non-linear models are accurate in simulating the patient's behaviour, and some of them are also sufficiently simple to be considered in the implementation of a model-based controller to develop the artificial pancreas