Bioengineered 3D platform to explore cell-ECM interactions and drug resistance of epithelial ovarian cancer cells

The behaviour of cells cultured within three-dimensional (3D) structures rather than onto two-dimensional (2D) culture plastic more closely reflects their in vivo responses. Consequently, 3D culture systems are becoming crucial scientific tools in cancer cell research. We used a novel 3D culture concept to assess cell-matrix interactions implicated in carcinogenesis: a synthetic hydrogel matrix equipped with key biomimetic features, namely incorporated cell integrin-binding motifs (e.g. RGD peptides) and the ability of being degraded by cell-secreted proteases (e.g. matrix metalloproteases). As a cell model, we chose epithelial ovarian cancer, an aggressive disease typically diagnosed at an advanced stage when chemoresistance occurs. Both cell lines used (OV-MZ-6, SKOV-3) proliferated similarly in 2D, but not in 3D. Spheroid formation was observed exclusively in 3D when cells were embedded within hydrogels. By exploiting the design flexibility of the hydrogel characteristics, we showed that proliferation in 3D was dependent on cell-integrin engagement and the ability of cells to proteolytically remodel their extracellular microenvironment. Higher survival rates after exposure to the anti-cancer drug paclitaxel were observed in cell spheroids grown in hydrogels (40-60%) compared to cell monolayers in 2D (20%). Thus, 2D evaluation of chemosensitivity may not reflect pathophysiological events seen in patients. Because of the design flexibility of their characteristics and their stability in long-term cultures (28 days), these biomimetic hydrogels represent alternative culture systems for the increasing demand in cancer research for more versatile, physiologically relevant and reproducible 3D matrices.

Large body size in an island-dwelling bird : a microevolutionary analysis

Island races of passerine birds display repeated evolution towards larger body size compared with their continental ancestors. The Capricorn silvereye (Zosterops lateralis chlorocephalus) has become up to six phenotypic standard deviations bigger in several morphological measures since colonization of an island approximately 4000 years ago. We estimated the genetic variance-covariance (G) matrix using full-sib and 'animal model' analyses, and selection gradients, for six morphological traits under field conditions in three consecutive cohorts of nestlings. Significant levels of genetic variance were found for all traits. Significant directional selection was detected for wing and tail lengths in one year and quadratic selection on culmen depth in another year. Although selection gradients on many traits were negative, the predicted evolutionary response to selection of these traits for all cohorts was uniformly positive. These results indicate that the G matrix and predicted evolutionary responses are consistent with those of a population evolving in the manner observed in the island passerine trend, that is, towards larger body size.

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Differential expression of chemokine and matrix re-modelling genes is associated with contrasting schistosome-induced hepatopathology in murine models

The pathological outcomes of schistosomiasis are largely dependent on the molecular and cellular mechanisms of the host immune response. In this study, we investigated the contribution of variations in host gene expression to the contrasting hepatic pathology observed between two inbred mouse strains following Schistosoma japonicum infection. Whole genome microarray analysis was employed in conjunction with histological and immunohistochemical analysis to define and compare the hepatic gene expression profiles and cellular composition associated with the hepatopathology observed in S. japonicum-infected BALB/c and CBA mice. We show that the transcriptional profiles differ significantly between the two mouse strains with high statistical confidence. We identified specific genes correlating with the more severe pathology associated with CBA mice, as well as genes which may confer the milder degree of pathology associated with BALB/c mice. In BALB/c mice, neutrophil genes exhibited striking increases in expression, which coincided with the significantly greater accumulation of neutrophils at granulomatous regions seen in histological sections of hepatic tissue. In contrast, up-regulated expression of the eosinophil chemokine CCL24 in CBA mice paralleled the cellular influx of eosinophils to the hepatic granulomas. Additionally, there was greater down-regulation of genes involved in metabolic processes in CBA mice, reflecting the more pronounced hepatic damage in these mice. Profibrotic genes showed similar levels of expression in both mouse strains, as did genes associated with Th1 and Th2 responses. However, imbalances in expression of matrix metalloproteinases (e.g. MMP12, MMP13) and tissue inhibitors of metalloproteinases (TIMP1) may contribute to the contrasting pathology observed in the two strains. Overall, these results provide a more complete picture of the molecular and cellular mechanisms which govern the pathological outcome of hepatic schistosomiasis. This improved understanding of the immunopathogenesis in the murine model schistosomiasis provides the basis for a better appreciation of the complexities associated with chronic human schistosomiasis.

Pedigree-free animal models : the relatedness matrix reloaded

Animal models typically require a known genetic pedigree to estimate quantitative genetic parameters. Here we test whether animal models can alternatively be based on estimates of relatedness derived entirely from molecular marker data. Our case study is the morphology of a wild bird population, for which we report estimates of the genetic variance-covariance matrices (G) of six morphological traits using three methods: the traditional animal model; a molecular marker-based approach to estimate heritability based on Ritland's pairwise regression method; and a new approach using a molecular genealogy arranged in a relatedness matrix (R) to replace the pedigree in an animal model. Using the traditional animal model, we found significant genetic variance for all six traits and positive genetic covariance among traits. The pairwise regression method did not return reliable estimates of quantitative genetic parameters in this population, with estimates of genetic variance and covariance typically being very small or negative. In contrast, we found mixed evidence for the use of the pedigree-free animal model. Similar to the pairwise regression method, the pedigree-free approach performed poorly when the full-rank R matrix based on the molecular genealogy was employed. However, performance improved substantially when we reduced the dimensionality of the R matrix in order to maximize the signal to noise ratio. Using reduced-rank R matrices generated estimates of genetic variance that were much closer to those from the traditional model. Nevertheless, this method was less reliable at estimating covariances, which were often estimated to be negative. Taken together, these results suggest that pedigree-free animal models can recover quantitative genetic information, although the signal remains relatively weak. It remains to be determined whether this problem can be overcome by the use of a more powerful battery of molecular markers and improved methods for reconstructing genealogies.

Matrix metalloproteinase localisation by in situ-RT-PCR in archival human breast biopsy material

Utilising archival human breast cancer biopsy material we examined the stromal/epithelial interactions of several matrix metalloproteinases (MMPs) using in situ-RT-PCR (IS-RT-PCR). In breast cancer, the stromal/epithelial interactions that occur, and the site of production of these proteases, are central to understanding their role in invasive and metastatic processes. We examined MT1-MMP (MMP-14, membrane type-1-MMP), MMP-1 (interstitial collagenase) and MMP-3 (stromelysin-1) for their localisation profile in progressive breast cancer biopsy material (poorly differentiated invasive breast carcinoma (PDIBC), invasive breast carcinomas (IBC) and lymph node metastases (LNM)). Expression of MT1-MMP, MMP-1 and MMP-3 was observed in both the tumour epithelial and surrounding stromal cells in most tissue sections examined. MT1-MMP expression was predominantly localised to the tumour component in the pre-invasive lesions. MMP-1 gene expression was relatively well distributed between both tissue compartments, while MMP-3 demonstrated highest expression levels in the stromal tissue surrounding the epithelial tumour cells. The results demonstrate the ability to distinguish compartmental gene expression profiles using IS-RT-PCR. Further, we suggest a role for MT1-MMP in early tumour progression, expression of MMP-1 during metastasis and focal expression pattern of MMP-3 in areas of expansion. These expression profiles may provide markers for early breast cancer diagnoses and present potential therapeutic targets.

Matrix metalloproteinases 2 and 9 (gelatinases A and B) expression in malignant mesothelioma and benign pleura

Matrix metalloproteinases (MMPs), in particular the gelatinases (MMP-2 and -9), play a significant role in tumour invasion and angiogenesis. The expression and activities of MMPs have not been characterised in malignant mesothelioma (MM) tumour samples. In a prospective study, gelatinase activity was evaluated in homogenised supernatants of snap frozen MM (n = 35), inflamed pleura (IP, n = 12) and uninflammed pleura (UP, n = 14) tissue specimens by semiquantitative gelatin zymography. Matrix metalloproteinases were correlated with clinicopathological factors and with survival using Kaplan-Meier and Cox proportional hazard models. In MM, pro- and active MMP-2 levels were significantly greater than for MMP-9 (P = 0.006, P<0.001). Active MMP-2 was significantly greater in MM than in UP (P=0.04). MMP-2 activity was equivalent between IP and MM, but both pro- and active MMP-9 activities were greater in IP (P=0.02, P=0.009). While there were trends towards poor survival with increasing total and pro-MMP-2 activity (P=0.08) in univariate analysis, they were both independent poor prognostic factors in multivariate analysis in conjunction with weight loss (pro-MMP-2 P = 0.03, total MMP-2 P = 0.04). Total and pro-MMP-2 also contributed to the Cancer and Leukemia Group B prognostic groups. MMP-9 activities were not prognostic. Matrix metalloproteinases, and in particular MMP-2, the most abundant gelatinase, may play an important role in MM tumour growth and metastasis. Agents that reduce MMP synthesis and/or activity may have a role to play in the management of MM. © 2003 Cancer Research UK.

Interleukin-1β stimulates human renal fibroblast proliferation and matrix protein production by means of a transforming growth factor-β-dependent mechanism

One of the hallmarks of progressive renal disease is the development of tubulointerstitial fibrosis. This is frequently preceded by macrophage infiltration, raising the possibility that macrophages relay fibrogenic signals to resident tubulointerstitial cells. The aim of this study was to investigate the potentially fibrogenic role of interleukin-1beta (IL-1beta), a macrophage-derived inflammatory cytokine, on cortical fibroblasts (CFs). Primary cultures of human renal CFs were established and incubated for 24 hours in the presence or absence of IL-1beta. We found that IL-1beta significantly stimulated DNA synthesis (356.7% +/- 39% of control, P <.003), fibronectin secretion (261.8 +/- 11% of control, P <.005), collagen type 1 production, (release of procollagen type 1 C-terminal-peptide, 152.4% +/- 26% of control, P <.005), transforming growth factor-beta (TGF-beta) secretion (211% +/- 37% of control, P <.01), and nitric oxide (NO) production (342.8% +/- 69% of control, P <.002). TGF-beta (1 ng/mL) and the phorbol ester phorbol 12-myristate 13-acetate (PMA, 25 nmol/L) produced fibrogenic effects similar to those of IL-1beta. Neither a NO synthase inhibitor (N(G)-methyl-l-arginine, 1 mmol/L) nor a protein kinase C (PKC) inhibitor (bis-indolylmaleimide 1, 1 micromol/L) altered the enhanced level of fibronectin secretion or DNA synthesis seen in response to IL-1beta treatment. However, addition of a TGF-beta-neutralizing antibody significantly reduced IL-1beta-induced fibronectin secretion (IL-1beta + IgG, 262% +/- 72% vs IL-1beta + alphaTGF-beta 156% +/- 14%, P <.02), collagen type 1 production (IL-1beta + IgG, 176% +/- 28% vs IL-1beta + alphaTGF-beta, 120% +/- 14%, P <.005) and abrogated IL-1beta-induced DNA synthesis (245% +/- 49% vs 105% +/- 21%, P <.005). IL-1beta significantly stimulated CF DNA synthesis and production of fibronectin, collagen type 1, TGFbeta, and NO. The fibrogenic and proliferative action of IL-1beta on CF appears not to involve activation of PKC or production of NO but is at least partly TGFbeta-dependent.

Polarized infrared absorption spectrum of matrix-isolated methylperoxyl radicals, CH3OO (X)over-tilde 2A ''

We have used a tandem pair of supersonic nozzles to produce clean samples of CH3OO radicals in cryogenic matrices. One hyperthermal nozzle decomposes azomethane (CH3NNCH3) to generate intense pulses of CH3 radicals, While the second nozzle alternately fires a burst Of O-2/Ar at the 20 K matrix. The CH3/O-2/20 K argon radical sandwich acts to produce target methylperoxyl radicals: CH3 + O-2 --> CH3OO. The absorption spectra of the radicals are monitored with a Fourier transform infrared spectrometer. We report 10 of the 12 fundamental infrared bands of the methylperoxyl radical CH3OO, (X) over tilde (2)A", in an argon matrix at 20 K. The experimental frequencies (cm(-1)) and polarizations follow: the a' modes are 3032, 2957, 1448, 1410, 1180, 1109, 90, 492, while the a" modes are 3024 and 1434. We cannot detect the asymmetric CH3 rocking mode, nu(11), nor the torsion, nu(12). The infrared spectra of (CH3OO)-O-18-O-18, (CH3OO)-C-13, and CD3OO have been measured as well in order to determine the isotopic shifts. The experimental frequencies, {nu}, for the methylperoxyl radicals are compared to harmonic frequencies, {omega}, resulting from a UB3LYP/6-311G(d,p) electronic structure calculation. Linear dichroism spectra were measured with photooriented radical samples in order to establish the experimental polarizations of most vibrational bands. The methylperoxyl radical matrix frequencies listed above are within +/-2% of the gas-phase vibrational frequencies. A final set of vibrational frequencies for the H radical, are recommended. See also http://ellison.colorado.edu/methylperoxyl.

Pro-matrix metalloproteinase-2 transfection increases orthotopic primary growth and experimental metastasis of MDA-MB-231 human breast cancer cells in nude mice

The ability to activate pro-matrix metalloproteinase (pro-MMP)-2 via membrane type-MMP is a hallmark of human breast cancer cell lines that show increased invasiveness, suggesting that MMP-2 contributes to human breast cancer progression. To investigate this, we have stably transfected pro-MMP-2 into the human breast cancer cell line MDA-MB-231, which lacks MMP-2 expression but does express its cell surface activator, membrane type 1-MMP. Multiple clones were derived and shown to produce pro-MMP-2 and to activate it in response to concanavalin A. In vitro analysis showed that the pro-MMP-2-transfected clones exhibited an increased invasive potential in Boyden chamber and Matrigel outgrowth assays, compared with the parental cells or those transfected with vector only. When inoculated into the mammary fat pad of nude mice, each of the MMP-2-tranfected clones grew faster than each of the vector controls tested. After intracardiac inoculation into nude mice, pro-MMP-2-transfected clones showed a significant increase in the incidence of metastasis to brain, liver, bone, and kidney compared with the vector control clones but not lung. Increased tumor burden was seen in the primary site and in lung metastases, and a trend toward increased burden was seen in bone, however, no change was seen in brain, liver, or kidney. This data supports a role for MMP-2 in breast cancer progression, both in the growth of primary tumors and in their spread to distant organs. MMP-2 may be a useful target for breast cancer therapy when refinement of MMP inhibitors provides for MMP-specific agents.

Tyrosine phosphorylation mediates ConA-induced membrane type 1-matrix metalloproteinase expression and matrix metalloproteinase-2 activation in MDA-MB-231 human breast carcinoma cells

ConA-induced cell surface activation of pro-matrix metalloproteinase-2 (pro-MMP-2) by MDA-MB-231 human breast cancer cells is apparently mediated by up-regulation of membrane type 1 MMP (MT1-MMP) through transcriptional and posttranscriptional mechanisms. Here, we have explored the respective roles of cell surface clustering and protein tyrosine phosphorylation in the ConA- induction effects. Treatment with succinyl-ConA, a variant lacking significant clusterability, partially stimulated MT1-MMP mRNA and protein levels but did not induce MMP-2 activation, suggesting that clustering contributes to the transcriptional regulation by ConA but appears to be critical for the nontranscriptional component. We further found that genistein, an inhibitor of tyrosine phosphorylation, blocked ConA-induced pro-MMP-2 activation and ConA-induced MT1-MMP mRNA level in a dose-dependent manner, implicating tyrosine phosphorylation in the transcriptional aspect. This was confirmed by the dose-dependent promotion of pro-MMP-2 activation by sodium orthovanadate in the presence of suboptimal concentrations of ConA (7.5 μg/ml), with optimal effects seen at 25 μg/g orthovanadate. Genistein did not inhibit the ConA potentiation of MMP-2 activation in MCF-7 cells, in which transfected MT1-MMP is driven by a heterologous promoter, supporting the major implication of phosphotyrosine in the transcriptional component of ConA regulation. These data describe a major signaling event upstream of MT1- MMP induction by ConA and set the stage for further analysis of the nontranscriptional component.

Scleral matrix metalloproteinases, serine proteinase activity and hydrational capacity are increased in myopia induced by retinal image degradation

In the avian model of myopia, retinal image degradation quickly leads to ocular enlargement. We now give evidence that regionally specific changes in ocular size are correlated with both biomechanical indices of scleral remodeling, e.g. hydration capacity and with biochemical changes in proteinase activities. The latter include a 72 kDa matrix metalloproteinase (putatively MMP-2), other gelatin-binding MMPs, an acid pH MMP and a serine protease. Specifically, we have found that increases in scleral hydrational capacity parallel increases in collagen degrading activities. Gelatin zymography reveals that eyes with 7 days of retinal image degradation have elevated levels (1.4-fold) of gelatinolytic activities at 72 and 67 kDa M(r) in equatorial and posterior pole regions of the sclera while, after 14 days of treatment, increases are no longer apparent. Lower M(r) zymographic activities at 50, 46 and 37 kDa M(r) are collectively increased in eyes treated for both 7 and 14 days (1.4- and 2.4-fold respectively) in the equator and posterior pole areas of enlarging eyes. Western blot analyses of scleral extracts with an antibody to human MMP-2 reveals immunoreactive bands at 65, 30 and 25 kDa. Zymograms incubated under slightly acidic conditions reveal that, in enlarging eyes, MMP activities at 25 and 28 kDa M(r) are increased in scleral equator and posterior pole (1.6- and 4.5-fold respectively). A TIMP-like protein is also identified in sclera and cornea by Western blot analysis. Finally, retinal-image degradation also increases (~2.6-fold) the activity of a 23.5 kDa serine proteinase in limbus, equator and posterior pole sclera that is inhibited by aprotinin and soybean trypsin inhibitor. Taken together, these results indicate that eye growth induced by retinal-image degradation involves increases in the activities of multiple scleral proteinases that could modify the biomechanical properties of scleral structural components and contribute to tissue remodeling and growth.