Biomedical subjects

Gabriela Kalna

Publications and source records attributed to Gabriela Kalna.

3 recordsLinked to original sources

PubMed2007

Nanotopographical stimulation of mechanotransduction and changes in interphase centromere positioning.

We apply a recently developed method for controlling the spreading of cultured cells using electron beam lithography (EBL) to create polymethylmethacrylate (PMMA) substrata with repeating nanostructures. There are indications that the reduced cell spreading on these substrata, compared with planar PMMA, results from a reduced adhesivity since there are fewer adhesive structures and fewer of their associated stress fibres. The reduced cell spreading also results in a reduced nuclear area and a closer spacing of centrosomes within the nucleus, suggesting that the tension applied to the nucleus is reduced as would be expected from the reduction in stress fibres. In order to obtain further evidence for this, we have used specific inhibitors of components of the cytoskeleton and have found effects comparable with those induced by the new substrata. We have also obtained evidence that these subtrata result in downregulation of gene expression which suggests that this may be due to the changed tension on the nucleus: an intriguing possibility that merits further investigation.

Cell Adhesion↗

PubMed2006

Spectral analysis of two-signed microarray expression data.

We give a simple and informative derivation of a spectral algorithm for clustering and reordering complementary DNA microarray expression data. Here, expression levels of a set of genes are recorded simultaneously across a number of samples, with a positive weight reflecting up-regulation and a negative weight reflecting down-regulation. We give theoretical support for the algorithm based on a biologically justified hypothesis about the structure of the data, and illustrate its use on public domain data in the context of unsupervised tumour classification. The algorithm is derived by considering a discrete optimization problem and then relaxing to the continuous realm. We prove that in the case where the data have an inherent 'checkerboard' sign pattern, the algorithm will automatically reveal that pattern. Further, our derivation shows that the algorithm may be regarded as imposing a random graph model on the expression levels and then clustering from a maximum likelihood perspective. This indicates that the output will be tolerant to perturbations and will reveal 'near-checkerboard' patterns when these are present in the data. It is interesting to note that the checkerboard structure is revealed by the first (dominant) singular vectors--previous work on spectral methods has focussed on the case of nonnegative edge weights, where only the second and higher singular vectors are relevant. We illustrate the algorithm on real and synthetic data, and then use it in a tumour classification context on three different cancer data sets. Our results show that respecting the two-signed nature of the data (thereby distinguishing between up-regulation and down-regulation) reveals structures that cannot be gleaned from the absolute value data (where up- and down-regulation are both regarded as 'changes').

Algorithms↗

PubMed2006

Divergent routes to oral cancer.

Most head and neck squamous cell carcinoma (HNSCC) patients present with late-stage cancers, which are difficult to treat. Therefore, early diagnosis of high-risk premalignant lesions and incipient cancers is important. HNSCC is currently perceived as a single progression mechanism, resulting in immortal invasive cancers. However, we have found that approximately 40% of primary oral SCCs are mortal in culture, and these have a better prognosis. About 60% of oral premalignancies (dysplasias) are also mortal. The mortal and immortal tumors are generated in vivo as judged by p53 mutations and loss of p16(INK4A) expression being found only in the original tumors from which the immortal cultures were derived. To investigate the relationships of dysplasias to SCCs, we did microarray analysis of primary cultures of 4 normal oral mucosa biopsies, 19 dysplasias, and 16 SCCs. Spectral clustering using the singular value decomposition and other bioinformatic techniques showed that development of mortal and immortal SCCs involves distinct transcriptional changes. Both SCC classes share most of the transcriptional changes found in their respective dysplasias but have additional changes. Moreover, high-risk dysplasias that subsequently progress to SCCs more closely resemble SCCs than nonprogressing dysplasias. This indicates for the first time that there are divergent mortal and immortal pathways for oral SCC development via intermediate dysplasias. We believe that this new information may lead to new ways of classifying HNSCC in relation to prognosis.

Carcinoma, Squamous Cell↗