Monocyte chemoattractant protein-1 serum levels in ovarian cancer patients

The chemokine monocyte chemoattractant protein (MCP)-1 is an important mediator of monocyte infiltration in various solid tumours of epithelial origin. The aim of the present study was to evaluate the role of MCP-1 in the natural history of ovarian cancer and to determine its value as differentiation marker and prognostic marker regarding disease free and overall survival. This retrospective study comprises 86 patients with ovarian cancer, 48 with primary ovarian cancer and 38 with recurrent ovarian cancer, 67 patients with benign ovarian cysts and 42 healthy women. Median serum levels in patients with primary ovarian cancer, recurrent ovarian cancer, benign ovarian cysts and in healthy women were 535.6 (range 129.6–1200) pg ml–1, 427.3 (range 193.4–1101) pg ml–1, 371.2 (range 222–986.8) pg ml–1 and 318.7 (range 241.3–681.4) pg ml–1 respectively (Mann–Whitney U-test, P < 0.001). Univariate logistic regression models revealed a significant influence of MCP-1 serum levels on the odds of presenting with primary ovarian cancer versus benign cysts and versus healthy women respectively (univariate logistic regression, P < 0.001 and P < 0.001 respectively). In a multivariate logistic regression model considering MCP-1 and CA 125 serum levels simultaneously, both MCP-1 and CA 125 revealed statistical significance on the odds of presenting with primary ovarian cancer versus benign cysts (multivariate logistic regression, P = 0.05 and P < 0.001 respectively). In ovarian cancer patients, MCP-1 serum levels showed a statistically significant correlation with histological grade (Mann–Whitney U-test, P = 0.02) and age at the time of diagnosis (Mann–Whitney U-test, P = 0.03). Elevated MCP-1 serum levels prior to therapy were not associated with disease-free and overall survival (log-rank test, P = 0.2 and P = 0.7 respectively). In summary these data indicate that MCP-1 might play a functional role in the natural history of ovarian cancer and might serve as differentiation marker between benign ovarian cysts and ovarian cancer, providing additional information to the established tumour marker CA 125. © 1999 Cancer Research Campaig

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

In praise of arrays

Microarray technologies have both fascinated and frustrated the transplant community since their introduction roughly a decade ago. Fascination arose from the possibility offered by the technology to gain a profound insight into the cellular response to immunogenic injury and the potential that this genomic signature would be indicative of the biological mechanism by which that stress was induced. Frustrations have arisen primarily from technical factors such as data variance, the requirement for the application of advanced statistical and mathematical analyses, and difficulties associated with actually recognizing signature gene-expression patterns and discerning mechanisms. To aid the understanding of this powerful tool, its versatility, and how it is dramatically changing the molecular approach to biomedical and clinical research, this teaching review describes the technology and its applications, as well as the limitations and evolution of microarrays, in the field of organ transplantation. Finally, it calls upon the attention of the transplant community to integrate into multidisciplinary teams, to take advantage of this technology and its expanding applications in unraveling the complex injury circuits that currently limit transplant survival

Mass Transportation for Deformable Image Registration with Application to Lung CT

Computed Tomography (CT) of the lungs play a key role in clinical investigation of thoracic malignancies, as well as having the potential to increase our knowledge about pulmonary diseases including cancer. It enables longitudinal trials to monitor lung disease progression, and to inform assessment of lung damage resulting from radiation therapy. We present a novel deformable image registration method that accommodates changes in the density of lung tissue depending on the amount of air present in the lungs inspiration/expiration state. We investigate the Monge-Kantorovich theory of optimal mass transportation to model the appearance of lung tissue and apply it in a method for registration. To validate the model, we apply our method to an inhale and exhale lung CT data set, and compare it against registration using the sum of squared differences (SSD) as a representative of the most popular similarity measures used in deformable image registration. The results show that the developed registration method has the potential to handle intensity distortions caused by air and tissue compression, and in addition it can provide accurate annotations of the lungs