SEPT9_v4 expression induces morphological change, increased motility and disturbed polarity

Several lines of evidence indicate that altered expression of SEPT9 is seen in human neoplasia. In particular there is evidence of altered expression of the SEPT9_v4 isoform. The functional consequences of this remain unclear. We have studied the expression of wild-type- and GTP-binding mutants (G144V and S148N) of the SEPT9_v4 isoform in the MCF7 cell line as a model for its deregulation in neoplasia. We find that SEPT9_v4 expression induces dramatic actin cytoskeletal reorganization with the formation of processes around the cell periphery. Expression of the SEPT9_v4 isoform and a G144V mutant cause delocalization of endogenous SEPT9 from filamentous structures but the S148N mutant does not have this effect. In addition SEPT9_v4 isoform expression enhances cell motility and is associated with perturbation of directional movement. Expression of SEPT9_v4 GTP binding mutants also has potent effects on morphology and motility and causes loss of normal polarity, as judged by Golgi reorientation assays. The phenotypes induced by expression of the SEPT9_v4 isoform and the GTP mutants provide an insight into possible mechanisms of SEPT9_v4 function and suggest that the GTPase functions have both ras- and rab-like features. We propose a model in which overexpression of the SEPT9_v4 isoform in neoplasia is associated with perturbation of SEPT9 complexes, leading to phenotypes associated with neoplasia. Copyright (c) 2005 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Multi-potent mesenchymal stromal cells in blood.

Peripheral blood-derived multi-potent mesenchymal stromal cells circulate in low number. They share, though not all, but most of the surface markers with bone marrow-derived multi-potent mesenchymal stromal cells, possess diverse and complicated gene expression characteristics, and are capable of differentiating along and even beyond mesenchymal lineages. Although their origin and physio-pathological function are still unclear, their presence in the adult peripheral blood might relate to some interesting but controversial subjects in the filed of adult stem cell biology, such as systemic migration of bone marrow-derived multi-potent mesenchymal stromal cells and the existence of common hematopoietic-mesenchymal precursors. In this review, current studies/knowledge about peripheral blood-derived multi-potent mesenchymal stromal cells is summarized and the above-mentioned topics are discussed.

Proteomic Analysis of Recurrent Joint Inflammation in Juvenile Idiopathic Arthritis

The synovial fluid proteome in juvenile idiopathic arthritis was investigated to isolate joint-specific biomarkers that are expressed in patients displaying recurrent joint inflammation. To identify the synovial specific proteome, matched synovial fluid and plasma samples were subjected to protein separation by 2-dimension electrophoresis (2DE). Forty-three protein spots, overexpressed in the joint, were identified. Synovial fluids from children with single-event knee joint inflammation were then compared with a group with recurrent knee disease. Nine synovial specific proteins were significantly differentially expressed in the recurrent group. Proteolytic fragments of collagen X, fibrin beta-chain, and T-cell receptor alpha-region have been identified among this protein cluster. Putative biomarkers, overexpressed in the joint and differentially expressed in children with recurrent joint inflammation, have been identified. These proteins may play a significant role determining the pathological state within the chronically inflamed joint and influence disease progression in JIA. This is the first study of the synovial proteome in children.

Computerised Diagnostic Decision Support System for the Classification of Pre-Invasive Cervical Squamous Lesions.

Previous studies have revealed considerable interobserver and intraobserver variation in the histological classification of preinvasive cervical squamous lesions. The aim of the present study was to develop a decision support system (DSS) for the histological interpretation of these lesions. Knowledge and uncertainty were represented in the form of a Bayesian belief network that permitted the storage of diagnostic knowledge and, for a given case, the collection of evidence in a cumulative manner that provided a final probability for the possible diagnostic outcomes. The network comprised 8 diagnostic histological features (evidence nodes) that were each independently linked to the diagnosis (decision node) by a conditional probability matrix. Diagnostic outcomes comprised normal; koilocytosis; and cervical intraepithelial neoplasia (CIN) 1, CIN II, and CIN M. For each evidence feature, a set of images was recorded that represented the full spectrum of change for that feature. The system was designed to be interactive in that the histopathologist was prompted to enter evidence into the network via a specifically designed graphical user interface (i-Path Diagnostics, Belfast, Northern Ireland). Membership functions were used to derive the relative likelihoods for the alternative feature outcomes, the likelihood vector was entered into the network, and the updated diagnostic belief was computed for the diagnostic outcomes and displayed. A cumulative probability graph was generated throughout the diagnostic process and presented on screen. The network was tested on 50 cervical colposcopic biopsy specimens, comprising 10 cases each of normal, koilocytosis, CIN 1, CIN H, and CIN III. These had been preselected by a consultant gynecological pathologist. Using conventional morphological assessment, the cases were classified on 2 separate occasions by 2 consultant and 2 junior pathologists. The cases were also then classified using the DSS on 2 occasions by the 4 pathologists and by 2 medical students with no experience in cervical histology. Interobserver and intraobserver agreement using morphology and using the DSS was calculated with K statistics. Intraobserver reproducibility using conventional unaided diagnosis was reasonably good (kappa range, 0.688 to 0.861), but interobserver agreement was poor (kappa range, 0.347 to 0.747). Using the DSS improved overall reproducibility between individuals. Using the DSS, however, did not enhance the diagnostic performance of junior pathologists when comparing their DSS-based diagnosis against an experienced consultant. However, the generation of a cumulative probability graph also allowed a comparison of individual performance, how individual features were assessed in the same case, and how this contributed to diagnostic disagreement between individuals. Diagnostic features such as nuclear pleomorphism were shown to be particularly problematic and poorly reproducible. DSSs such as this therefore not only have a role to play in enhancing decision making but also in the study of diagnostic protocol, education, self-assessment, and quality control. (C) 2003 Elsevier Inc. All rights reserved.

An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN)

The histological grading of cervical intraepithelial neoplasia (CIN) remains subjective, resulting in inter- and intra-observer variation and poor reproducibility in the grading of cervical lesions. This study has attempted to develop an objective grading system using automated machine vision. The architectural features of cervical squamous epithelium are quantitatively analysed using a combination of computerized digital image processing and Delaunay triangulation analysis; 230 images digitally captured from cases previously classified by a gynaecological pathologist included normal cervical squamous epithelium (n = 30), koilocytosis (n = 46), CIN 1 (n = 52), CIN 2 (n = 56), and CIN 3 (n=46). Intra- and inter-observer variation had kappa values of 0.502 and 0.415, respectively. A machine vision system was developed in KS400 macro programming language to segment and mark the centres of all nuclei within the epithelium. By object-oriented analysis of image components, the positional information of nuclei was used to construct a Delaunay triangulation mesh. Each mesh was analysed to compute triangle dimensions including the mean triangle area, the mean triangle edge length, and the number of triangles per unit area, giving an individual quantitative profile of measurements for each case. Discriminant analysis of the geometric data revealed the significant discriminatory variables from which a classification score was derived. The scoring system distinguished between normal and CIN 3 in 98.7% of cases and between koilocytosis and CIN 1 in 76.5% of cases, but only 62.3% of the CIN cases were classified into the correct group, with the CIN 2 group showing the highest rate of misclassification. Graphical plots of triangulation data demonstrated the continuum of morphological change from normal squamous epithelium to the highest grade of CIN, with overlapping of the groups originally defined by the pathologists. This study shows that automated location of nuclei in cervical biopsies using computerized image analysis is possible. Analysis of positional information enables quantitative evaluation of architectural features in CIN using Delaunay triangulation meshes, which is effective in the objective classification of CIN. This demonstrates the future potential of automated machine vision systems in diagnostic histopathology. Copyright (C) 2000 John Wiley and Sons, Ltd.