16 research outputs found
Collagen-Like Proteins in Pathogenic E. coli Strains
The genome sequences of enterohaemorrhagic E. coli O157:H7 strains show multiple open-reading frames with collagen-like sequences that are absent from the common laboratory strain K-12. These putative collagens are included in prophages embedded in O157:H7 genomes. These prophages carry numerous genes related to strain virulence and have been shown to be inducible and capable of disseminating virulence factors by horizontal gene transfer. We have cloned two collagen-like proteins from E. coli O157:H7 into a laboratory strain and analysed the structure and conformation of the recombinant proteins and several of their constituting domains by a variety of spectroscopic, biophysical, and electron microscopy techniques. We show that these molecules exhibit many of the characteristics of vertebrate collagens, including trimer formation and the presence of a collagen triple helical domain. They also contain a C-terminal trimerization domain, and a trimeric α-helical coiled-coil domain with an unusual amino acid sequence almost completely lacking leucine, valine or isoleucine residues. Intriguingly, these molecules show high thermal stability, with the collagen domain being more stable than those of vertebrate fibrillar collagens, which are much longer and post-translationally modified. Under the electron microscope, collagen-like proteins from E. coli O157:H7 show a dumbbell shape, with two globular domains joined by a hinged stalk. This morphology is consistent with their likely role as trimeric phage side-tail proteins that participate in the attachment of phage particles to E. coli target cells, either directly or through assembly with other phage tail proteins. Thus, collagen-like proteins in enterohaemorrhagic E. coli genomes may have a direct role in the dissemination of virulence-related genes through infection of harmless strains by induced bacteriophages
Similarity-Based Extraction of Individual Networks from Gray Matter
Recently graph theory has been successfully applied to magnetic resonance
imaging data. However, it remains unclear as to what the nodes and edges in a
network should represent. This problem is particularly difficult when extracting
morphological networks (i.e., from grey matter segmentations). Existing
morphological network studies have used anatomical regions as nodes that are
connected by edges when these regions covary in thickness or volume across a
sample of subjects. Covariance in cortical thickness or volume has been
hypothesised to be caused by anatomical connectivity, experience driven plasticity
and/or mutual trophic influences. A limitation of this approach is that it requires
magnetic resonance imaging (MRI) scans to be warped into a standard template.
These warping processes could filter out subtle structural differences that are of
most interest in, for example, clinical studies.
The focus of the work in this thesis was to address these limitations by
contributing a new method to extract morphological networks from individual
cortices. Briefly, this method divides the cortex into small regions of interest that
keep the three-dimensional structure intact, and edges are placed between any two
regions that have a statistically similar grey matter structure. The method was
developed in a sample of 14 healthy individuals, who were scanned at two
different time points. For the first time individual grey matter networks based on
intracortical similarity were studied. The topological organisation of intracortical
similarities was significantly different from random topology. Additionally, the
graph theoretical properties were reproducible over time supporting the robustness
of the method. All network properties closely resembled those reported in other
imaging studies.
The second study in this thesis focussed on the question whether
extracting networks from individual scans would be more sensitive than
traditional methods (that use warping procedures) to subtle grey matter
differences in MRI data. In order to investigate this question, the method was
applied to the first round of scans from the Edinburgh High Risk study of
Schizophrenia (EHRS), before any of the subjects was diagnosed with (symptoms
of) the disease. Where traditional methods failed to find differences at the whole brain level between the high risk group and healthy controls, the new method did
find subtle disruptions of global network topology between the groups. Finally,
the diagnostic value of the networks was studied with exploratory analyses that
found that, in comparison to healthy controls, people at high risk of schizophrenia
showed more intracortical similarities in the left angular gyrus. Furthermore
within the high risk group an increase of intracortical similarities could predict
disease outcome up to 74% accuracy.
The main conclusion of this thesis was that the new method provides a
robust and concise statistical description of the grey matter structure in individual
cortices, that is of particular importance for the study of clinical populations when
structural disruptions are subtle