IL-17F co-expression improves cell growth characteristics and enhances recombinant protein production during CHO cell line engineering.

The generation of a high productivity cell line is a critical step in the production of a therapeutic protein. Many innovative engineering strategies have been devised in order to maximize the expression rate of production cells for increased process efficiency. Less effort has focused on improvements to the cell line generation process, which is typically long and laborious when using mammalian cells. Based on unexpected findings when generating stable CHO cell lines expressing human IL-17F, we studied the benefit of expressing this protein during the establishment of production cell lines. We demonstrate that IL-17F expression enhances the rate of selection and overall number of selected cell lines as well as their transgene expression levels. We also show that this benefit is observed with different parental CHO cell lines and selection systems. Furthermore, IL-17F expression improves the efficiency of cell line subcloning processes. IL-17F can therefore be exploited in a standard manufacturing process to obtain higher productivity clones in a reduced time frame.

MM-GBSA binding free energy decomposition and T cell receptor engineering.

Recognition by the T-cell receptor (TCR) of immunogenic peptides (p) presented by class I major histocompatibility complexes (MHC) is the key event in the immune response against virus infected cells or tumor cells. The major determinant of T cell activation is the affinity of the TCR for the peptide-MHC complex, though kinetic parameters are also important. A study of the 2C TCR/SIYR/H-2Kb system using a binding free energy decomposition (BFED) based on the MM-GBSA approach had been performed to assess the performance of the approach on this system. The results showed that the TCR-p-MHC BFED including entropic terms provides a detailed and reliable description of the energetics of the interaction (Zoete and Michielin, 2007). Based on these results, we have developed a new approach to design sequence modifications for a TCR recognizing the human leukocyte antigen (HLA)-A2 restricted tumor epitope NY-ESO-1. NY-ESO-1 is a cancer testis antigen expressed not only in melanoma, but also on several other types of cancers. It has been observed at high frequencies in melanoma patients with unusually positive clinical outcome and, therefore, represents an interesting target for adoptive transfer with modified TCR. Sequence modifications of TCR potentially increasing the affinity for this epitope have been proposed and tested in vitro. T cells expressing some of the proposed TCR mutants showed better T cell functionality, with improved killing of peptide-loaded T2 cells and better proliferative capacity compared to the wild type TCR expressing cells. These results open the door of rational TCR design for adoptive transfer cancer therapy.

CHO cell engineering to prevent polypeptide aggregation and improve therapeutic protein secretion.

The ability to efficiently produce recombinant proteins in a secreted form is highly desirable and cultured mammalian cells such as CHO cells have become the preferred host as they secrete proteins with human-like post-translational modifications. However, attempts to express high levels of particular proteins in CHO cells may consistently result in low yields, even for non-engineered proteins such as immunoglobulins. In this study, we identified the responsible faulty step at the stage of translational arrest, translocation and early processing for such a "difficult-to-express" immunoglobulin, resulting in improper cleavage of the light chain and its precipitation in an insoluble cellular fraction unable to contribute to immunoglobulin assembly. We further show that proper processing and secretion were restored by over-expressing human signal receptor protein SRP14 and other components of the secretion pathway. This allowed the expression of the difficult-to-express protein to high yields, and it also increased the production of an easy-to-express protein. Our results demonstrate that components of the secretory and processing pathways can be limiting, and that engineering of the secretory pathway may be used to improve the secretion efficiency of therapeutic proteins from CHO cells.

In vitro characterization of immune-related properties of human fetal bone cells for potential tissue engineering applications.

We describe herein some immunological properties of human fetal bone cells recently tested for bone tissue-engineering applications. Adult mesenchymal stem cells (MSCs) and osteoblasts were included in the study for comparison. Surface markers involved in bone metabolism and immune recognition were analyzed using flow cytometry before and after differentiation or treatment with cytokines. Immunomodulatory properties were studied on activated peripheral blood mononuclear cells (PBMCs). The immuno-profile of fetal bone cells was further investigated at the gene expression level. Fetal bone cells and adult MSCs were positive for Stro-1, alkaline phosphatase, CD10, CD44, CD54, and beta2-microglobulin, but human leukocyte antigen (HLA)-I and CD80 were less present than on adult osteoblasts. All cells were negative for HLA-II. Treatment with recombinant human interferon gamma increased the presence of HLA-I in adult cells much more than in fetal cells. In the presence of activated PBMCs, fetal cells had antiproliferative effects, although with patterns not always comparable with those of adult MSCs and osteoblasts. Because of the immunological profile, and with their more-differentiated phenotype than of stem cells, fetal bone cells present an interesting potential for allogeneic cell source in tissue-engineering applications.

Application of text-mining for updating protein post-translational modification annotation in UniProtKB.

BACKGROUND: The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. RESULTS: The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. CONCLUSIONS: The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.

Human fetal bone cells in delivery systems for bone engineering.

The aim of this study was to culture human fetal bone cells (dedicated cell banks of fetal bone derived from 14 week gestation femurs) within both hyaluronic acid gel and collagen foam, to compare the biocompatibility of both matrices as potential delivery systems for bone engineering and particularly for oral application. Fetal bone cell banks were prepared from one organ donation and cells were cultured for up to 4 weeks within hyaluronic acid (Mesolis(®)) and collagen foams (TissueFleece(®)). Cell survival and differentiation were assessed by cell proliferation assays and histology of frozen sections stained with Giemsa, von Kossa and ALP at 1, 2 and 4 weeks of culture. Within both materials, fetal bone cells could proliferate in three-dimensional structure at ∼70% capacity compared to monolayer culture. In addition, these cells were positive for ALP and von Kossa staining, indicating cellular differentiation and matrix production. Collagen foam provides a better structure for fetal bone cell delivery if cavity filling is necessary and hydrogels would permit an injectable technique for difficult to treat areas. In all, there was high biocompatibility, cellular differentiation and matrix deposition seen in both matrices by fetal bone cells, allowing for easy cell delivery for bone stimulation in vivo. Copyright © 2011 John Wiley & Sons, Ltd.

Covalent cell surface functionalization of human fetal osteoblasts for tissue engineering.

The chemical functionalization of cell-surface proteins of human primary fetal bone cells with hydrophilic bioorthogonal intermediates was investigated. Toward this goal, chemical pathways were developed for click reaction-mediated coupling of alkyne derivatives with cellular azido-expressing proteins. The incorporation via a tetraethylene glycol linker of a dipeptide and a reporter biotin allowed the proof of concept for the introduction of cell-specific peptide ligands and to follow the reaction in living cells. Tuning the conditions of the click reaction resulted in chemical functionalization of living human fetal osteoblasts with excellent cell survival.

Mining the human phenome using allelic scores that index biological intermediates.

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

Gene transfer engineering for astrocyte-specific silencing in the CNS.

Cell-type-specific gene silencing is critical to understand cell functions in normal and pathological conditions, in particular in the brain where strong cellular heterogeneity exists. Molecular engineering of lentiviral vectors has been widely used to express genes of interest specifically in neurons or astrocytes. However, we show that these strategies are not suitable for astrocyte-specific gene silencing due to the processing of small hairpin RNA (shRNA) in a cell. Here we develop an indirect method based on a tetracycline-regulated system to fully restrict shRNA expression to astrocytes. The combination of Mokola-G envelope pseudotyping, glutamine synthetase promoter and two distinct microRNA target sequences provides a powerful tool for efficient and cell-type-specific gene silencing in the central nervous system. We anticipate our vector will be a potent and versatile system to improve the targeting of cell populations for fundamental as well as therapeutic applications.