Phenotypic and genotypic analysis of intestinal spirochacies

Pulsed field gel electrophoresis of 82 intestinal spirochaete isolates showed specific differentiation of Serpulina pilosicoli and Serpulina hyodysenteriae although considerable heterogeneity was observed, especially amongst S. pilosicoli isolates. In several cases genotypically similar isolates originated from different animals suggesting that cross-species transmission may have occurred. The Caco-2 and Caco-21HT29 cell models have been proposed as potentially realistic models of intestinal infection. Quantitation of adhesion to the cells showed isolate 3 82/91 (from a bacteraemia) to adhere at significantly greater numbers than any other isolate tested. This isolate produced a PFGE profile which differed from other S. pilosicoli isolates and so would be of interest for further study. Comparison of bacteraemic and other S. pilosicoli isolates suggested that bacteraemic isolates were not more specifically adapted for adhesion to, or invasion of the epithelial cell layer than other S. pilosicoli isolates. Genotypically similar isolates from differing animal origins adhered to the Caco-2 model at similar levels. Generation of a random genomic library of S. pilosicoli and screening with species specific monoclonal antibody has enabled the identification of a gene sequence encoding a protein which showed significant homology with an ancestral form of the enzyme pyruvate oxidoreductase. Immunoscreening with polyclonal serum identified the sequences of two gene clusters and a probable arylsulphatase. One gene cluster represented a ribosomal gene cluster which has a similar molecular arrangement to Borrelia burgdorjeri, Treponema pallidum and Thermatoga maritima. The other gene cluster contained an ABC transporter protein, sorbitol dehydrogenase and phosphomannose isomerase. An ELISA type assay was used to demonstrate that isolates of S. pilosicoli could adhere to components of the extracellular matrix such as collagen (type 1), fibronectin, laminin, and porcine gastric mucin.

DNA Mutation of the progranulin (GRN) gene in familial frontotemporal lobar degeneration (FTLD):a study of the pathology of nine cases

Frontotemporal lobar degeneration (FTLD) with transactive response (TAR) DNA-binding protein of 43kDa (TDP-43) proteinopathy (FTLD-TDP) is a neurodegenerative disease characterized by variable neocortical and allocortical atrophy principally affecting the frontal and temporal lobes. Histologically, there is neuronal loss, microvacuolation in the superficial cortical laminae, and a reactive astrocytosis. A variety of TDP-43 immunoreactive changes are present in FTLD-TDP including neuronal cytoplasmic inclusions (NCI), neuronal intranuclear inclusions (NII), dystrophic neurites (DN) and, oligodendroglial inclusions (GI). Many cases of familial FTLD-TDP are caused by DNA mutations of the progranulin (GRN) gene. Hence, the density, spatial patterns, and laminar distribution of the pathological changes were studied in nine cases of FLTD-TDP with GRN mutation. The densities of NCI and DN were greater in cases caused by GRN mutation compared with sporadic cases. In cortical regions, the commonest spatial pattern exhibited by the TDP-43 immunoreactive lesions was the presence of clusters of inclusions regularly distributed parallel to the pia mater. In approximately 50% of cortical gyri, the NCI exhibited a peak of density in the upper cortical laminae while the GI were commonly distributed across all laminae. The distribution of the NII and DN was variable, the most common pattern being a peak of NII density in the lower cortical laminae and DN in the upper cortical laminae. These results suggest in FTLD-TDP caused by GRN mutation: 1) there are greater densities of NCI and DN than in sporadic cases of the disease, 2) there is degeneration of the cortico-cortical and cortico-hippocampal pathways, and 3) cortical degeneration occurs across the cortical laminae, the various TDP-43 immunoreactive inclusions often being distributed in different cortical laminae.

Insulin gene therapy for the treatment of diabetes mellitus

Currently available treatments for insulin-dependent diabetes mellitus are often inadequate in terms of both efficacy and patient compliance. Gene therapy offers the possibility of a novel and improved method by which exogenous insulin can be delivered to a patient. This was approached in the present study by constructing a novel insulin-secreting cell line. For the purposes of this work immortalized cell lines were used. Fibroblasts and pituitary cells were transfected with the human preproisinulin gene to create stable lines of proinsulin- and insulin-secreting cells. The effect of known β-cell secretagogues on these cells were investigated, and found mostly to have no stimulatory effect, although IBMX, arginine and ZnSO4 each increased the rate of secretion. Cyclosporin (CyA) is currently the immunosuppresant of choice for transplant recipients; the effect of this treatment on endogenous β-cell function was assessed both in vivo and in vitro. Therapeutic doses of CyA were found to reduce plasma insulin concentrations and to impair glucose tolerance. The effect of immunoisolation on insulin release by HIT T15 cells was also investigated. The presence of an alginate membrane was found to severely impair insulin release. For the first implantation of the insulin-secreting cells, the animal model selected was the athymic nude mouse. This animal is immunoincompetent, and hence the use of an immunosuppressive regimen is circumvented. Graft function was assessed by measurement of plasma human C peptide concentrations, using a highly specific assay. Intraperitoneal implantation of genetically manipulated insulin-secreting pituitary cells into nude mice subsequently treated with a large dose of streptozotocin (STZ) resulted in a significantly delayed onset of hyperglycaemia when compared to control animals. Consumption of a ZnSO4 solution was shown to increase human C peptide release by the implant. Ensuing studies in nude mice examined the efficacy of different implantation sites, and included histochemical examination of the tumours. Aldehyde fuchsin staining and immunocytochemical processing demonstrated the presence of insulin containing cells within the excised tissue. Following initial investigations in nude mice, implantation studies were performed in CyA-immunosuppressed normal and STZ-diabetic mice. Graft function was found to be less efficacious, possibly due to the subcutaneous implantation site, or to the immunosuppresive regimen. Histochemical and transmission electron microscopic analysis of the tumour-like cell clusters found at autopsy revealed necrosis of cells at the core, but essentially normal cell morphology, with dense secretory granules in peripheral cells. The thesis provides evidence that gene therapy offers a feasibly new approach to insulin delivery.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.